adapting to the work-life interface: the influence of individual differences, work...

i

Adapting to the work-life interface: The influence of individual differences, work

and family on well-being, mental health and work engagement.

By

Prudence M. R. Millear

B. Sc. Ag. (Hons), Grad. Dip Psych, B. Psych. (Hons)

A thesis submitted in fulfilment of the requirements of the degree of

Doctor of Philosophy

School of Psychology and Counselling

Faculty of Health

Queensland University of Technology

February 2010

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Keywords

Bronfenbrenner, dispositional optimism, coping self-efficacy, affective commitment,

skill discretion, job autonomy, life satisfaction, psychological well-being, mental

health, work engagement, burnout, longitudinal modelling, gain spirals, loss spirals,

Conservation of Resources, resource caravans, working adults

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Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, this thesis contains no material previously

published or written by another person except where due reference is made.

Signature ……………………………………………………………………………

Date …………………………………………………………………………………

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Publications and presentations arising from the PhD research

Journal articles

1. Millear, P.M & Liossis, P.L., Gain spirals and resource caravans: An integrated

longitudinal model of well-being, mental health and work engagement among

Australian workers, under review, Journal of Occupational and

Organizational Psychology

Book Chapters

1 Millear, P.M. & Liossis, P.L. (2010) Longitudinal modelling of individual

differences and the workplace: well-being and work engagement. Chapter 18

in Hicks, R.E. (Ed.) Personality and Individual Differences: Current

Directions. Brisbane, Australia: Australian Academic Press

2. Millear, P.M. & Liossis, P.L. Doing it for yourself: The choices and strategies of

managing the work-life challenge, accepted for publication, Wayfinding

through life‟s challenges: Coping and survival, Nova Science Publishers, NY,

K. Gow & M. Celinski (Eds)

Conference Presentations

2008 European Academy of Occupational Health Psychology conference at the

University of Valencia, November 2008, 2 presentations: 1 Longitudinal

modelling of well-being and mental health in Australian workers; 2 Exploring

burnout and work engagement in diverse occupations: A continuum or two

separate factors?

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2008 Australian Conference for Personality and Individual Differences (ACPID),

Bond University, (November, 2008) “Longitudinal modelling of the influence

of individual differences and the workplace on well-being and work

engagement”

2009 8th

Industrial and Organizational Psychology conference, Sydney (June

2009), Paper Presentation, “An integrated longitudinal model of well-being,

mental health and work engagement among Australian workers”

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Acknowledgements

I would like to acknowledge and sincerely thank my supervisors, Dr Poppy

Liossis, my Principal Supervisor and Professor Ian Shochet, my Associate

Supervisor for the guidance and support that they have provided throughout my

candidature. I would also like to thank Dr Cameron Hurst and Dr Trish Obst for their

assistance with the Structural Equation Modelling that I undertook and thank

Cameron particularly for deciphering the process of longitudinal modelling. I would

like to thank my postgraduate friends for their unstinting support, coffee and

sympathy and thank my family and friends for helping where they could.

My greatest thanks are to my husband and children for bearing with me and

understanding the work involved in completing my thesis, in the middle of family

life, rugby and the house renovations. We have done this together.

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Abstract

Bronfenbrenner‟s Bioecological Model, expressed as the developmental equation, D

f PPCT, is the theoretical framework for two studies that bring together diverse

strands of psychology to study the work-life interface of working adults.

Occupational and organizational psychology is focused on the demands and

resources of work and family, without emphasising the individual in detail. Health

and personality psychology examine the individual but without emphasis on the

individual‟s work and family roles. The current research used Bronfenbrenner‟s

theoretical framework to combine individual differences, work and family to

understand how these factors influence the working adult‟s psychological

functioning. Competent development has been defined as high well-being (measured

as life satisfaction and psychological well-being) and high work engagement (as

work vigour, work dedication and absorption in work) and as the absence of mental

illness (as depression, anxiety and stress) and the absence of burnout (as emotional

exhaustion, cynicism and professional efficacy).

Study 1 and 2 were linked, with Study 1 as a cross-sectional survey and Study

2, a prospective panel study that followed on from the data used in Study1.

Participants were recruited from a university and from a large public hospital to take

part in a 3-wave, online study where they completed identical surveys at 3-4 month

intervals (N = 470 at Time 1 and N = 198 at Time 3). In Study 1, hierarchical

multiple regressions were used to assess the effects of individual differences (Block

1, e.g. dispositional optimism, coping self-efficacy, perceived control of time,

humour), work and family variables (Block 2, e.g. affective commitment, skill

discretion, work hours, children, marital status, family demands) and the work-life

interface (Block 3, e.g. direction and quality of spillover between roles, work-life

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balance) on the outcomes. There were a mosaic of predictors of the outcomes with a

group of seven that were the most frequent significant predictors and which

represented the individual (dispositional optimism and coping self-efficacy), the

workplace (skill discretion, affective commitment and job autonomy) and the work-

life interface (negative work-to-family spillover and negative family-to-work

spillover). Interestingly, gender and working hours were not important predictors.

The effects of job social support, generally and for work-life issues, perceived

control of time and egalitarian gender roles on the outcomes were mediated by

negative work-to-family spillover, particularly for emotional exhaustion. Further, the

effect of negative spillover on depression, anxiety and work engagement was

moderated by the individual‟s personal and workplace resources.

Study 2 modelled the longitudinal relationships between the group of the

seven most frequent predictors and the outcomes. Using a set of non-nested models,

the relative influences of concurrent functioning, stability and change over time were

assessed. The modelling began with models at Time 1, which formed the basis for

confirmatory factor analysis (CFA) to establish the underlying relationships between

the variables and calculate the composite variables for the longitudinal models. The

CFAs were well fitting with few modifications to ensure good fit. However, using

burnout and work engagement together required additional analyses to resolve poor

fit, with one factor (representing a continuum from burnout to work engagement)

being the only acceptable solution. Five different longitudinal models were

investigated as the Well-Being, Mental Distress, Well-Being-Mental Health, Work

Engagement and Integrated models using differing combinations of the outcomes.

The best fitting model for each was a reciprocal model that was trimmed of trivial

paths. The strongest paths were the synchronous correlations and the paths within

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variables over time. The reciprocal paths were more variable with weak to mild

effects. There was evidence of gain and loss spirals between the variables over time,

with a slight net gain in resources that may provide the mechanism for the

accumulation of psychological advantage over a lifetime. The longitudinal models

also showed that there are leverage points at which personal, psychological and

managerial interventions can be targeted to bolster the individual and provide

supportive workplace conditions that also minimise negative spillover.

Bronfenbrenner‟s developmental equation has been a useful framework for

the current research, showing the importance of the person as central to the

individual‟s experience of the work-life interface. By taking control of their own life,

the individual can craft a life path that is most suited to their own needs. Competent

developmental outcomes were most likely where the person was optimistic and had

high self-efficacy, worked in a job that they were attached to and which allowed

them to use their talents and without too much negative spillover between their work

and family domains. In this way, individuals had greater well-being, better mental

health and greater work engagement at any one time and across time.

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Table of Contents

Keywords .................................................................................................................... iii Statement of Original Authorship ................................................................................ v

Publications and presentations arising from the PhD research .................................. vii Acknowledgements ..................................................................................................... ix Abstract ....................................................................................................................... xi Table of Contents ...................................................................................................... xiv List of Tables ............................................................................................................. xix

List of Figures .......................................................................................................... xxii List of Appendices .................................................................................................. xxiii Chapter 1: Theories and literature review of Bronfenbrenner‟s developmental

equation, applied to individuals and the work-life interface ........................................ 1 1.1 Bronfenbrenner‟s Bioecological Model of Human Development ..................... 2

1.2 Theories for D, the developmental outcomes, defined by well-being, mental

health, burnout and work engagement ..................................................................... 9

1.2.1 Well-being ................................................................................................... 9 1.2.1.1 Prevalence. ......................................................................................... 12 1.2.1.2 Stability of well-being ........................................................................ 14 1.2.1.3 Australian health and working provisions .......................................... 14

1.2.2 Mental health, as the absence of mental illnesses ..................................... 15 1.2.2.1 Costs and prevalence .......................................................................... 18

1.2.3 Burnout and work engagement ................................................................. 22

1.2.4 Bringing together well-being, mental health, burnout and engagement ... 26 1.3 Understanding the person, P, in the developmental equation .......................... 29

1.3.1 Generative dispositions and demand characteristics ................................. 29 1.3.2 Theories of the generative disposition of P, the person occupying and

managing multiple roles ..................................................................................... 30 1.3.3 Linkages between the generative disposition and positive affect, positive

psychology and resilience .................................................................................. 34 1.3.4 Gender and the generative disposition of the active participant, P ........... 39 1.3.5 Gender ....................................................................................................... 40

1.3.5.1 Gender and the work environment ..................................................... 41 1.3.5.2 Gender and parenting ......................................................................... 46

1.3.5.3 Gender and house work. ..................................................................... 48 1.3.6 Dispositional optimism ............................................................................. 50 1.3.7 Self-efficacy, as coping self-efficacy ........................................................ 56 1.3.8 Perceived control of time .......................................................................... 58 1.3.9 Theories of the demand characteristics of P, the person occupying and

managing multiple roles ..................................................................................... 60

1.3.10 Humour .................................................................................................... 64

1.3.11 Social skills and relationships ................................................................. 69 1.3.12 Conclusion for P, the Person ................................................................... 73

1.4 Understanding C, the Context for multiple roles ............................................. 75 1.4.1 Theories and models of C, the Context for multiple roles ........................ 75 1.4.2 Direction for the literature review of C, the context ................................. 80

1.4.3 Working hours and schedules ................................................................... 82 1.4.4 Demands and resources ............................................................................. 87 1.4.5 Affective commitment ............................................................................... 93

1.4.6 Managerial support of work-life issues ..................................................... 95

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1.4.7 Family characteristics................................................................................ 99

1.4.8 Multiple roles and spillover .................................................................... 104 1.4.9 Exploring the interactions between work and non-work domains .......... 106

1.4.9.1 Comparing types of jobs .................................................................. 108

1.4.9.2 Importance of roles .......................................................................... 110 1.4.9.3 Individual factors ............................................................................. 112 1.4.9.4 Workplace and family factors .......................................................... 113

1.4.10 Exploring work-life balance and work-life fit ...................................... 120 1.4.11 Conclusions of the Context of the work-life interface .......................... 122

1.5 T, the time frame over which multiple roles develop and occur .................... 124 1.5.1 Longitudinal studies from a developmental perspective ......................... 130 1.5.2 Longitudinal studies from an organizational perspective ....................... 136 1.5.3 Conclusions for Time in the developmental equation............................. 142

1.6 Proposed research program ............................................................................ 143

1.6.2 Study 1 .................................................................................................... 143 1.6.3 Study 2 .................................................................................................... 145

Chapter 2, Study 1: Using hierarchical multiple regressions to explore the predictors

of well-being, mental illness, burnout and work engagement of working adults .... 147 2.1.1 Hypothesis for Study 1. ........................................................................... 148

2.2 Methods .............................................................................................................. 149

2.2.1 Participants .............................................................................................. 149 2.2.1.1 Recruitment ...................................................................................... 149

2.2.2 Internet survey development ................................................................... 150

2.2.3 Internet survey methodology .................................................................. 151 2.2.4 Measures ................................................................................................. 155

2.2.4.1 Demographics .................................................................................. 155 2.2.4.2 Schedules, education, job conditions and income. ........................... 156

2.2.4.3 Work-life fit, work-life balance, feeling busy and personal problems.

...................................................................................................................... 157

2.2.5 Reliabilities and details of the measures ................................................. 157 2.2.6 P, the Person: Generative disposition variables ...................................... 158

2.2.6.1 Dispositional optimism. ................................................................... 158

2.2.6.2 Coping self-efficacy ......................................................................... 158 2.2.6.3 Control ............................................................................................. 159

2.2.7 P, the Person: Demand characteristic variables ...................................... 161 2.2.8 C, the Context: Workplace conditions .................................................... 161

2.2.8.4 Managerial support for work-life issues .......................................... 162 2.2.8.5 Affective commitment ..................................................................... 162

2.2.9 C, the Context: The work-life interface .................................................. 163

2.2.10 Well-being, mental illness, burnout and work engagement .................. 164

2.2.10.3 Satisfaction with life domains ........................................................ 165

2.2.10.5 Burnout ........................................................................................... 166 2.2.10.6 Work Engagement .......................................................................... 166

2.2.11 Procedure............................................................................................... 167 2.2.12 Analytical strategy for the hierarchical multiple regression (HMR)

analyses ............................................................................................................ 169

2.3 Results ................................................................................................................ 174 2.3.1 Data cleaning and screening.................................................................... 174 2.3.2 Demographics ......................................................................................... 176

2.3.3 Scale construction and sample size ......................................................... 179

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2.3.4 Means, standard deviations and correlations between the variables ....... 180

2.3.5 Presentation of the results of the HMR ................................................... 189 2.3.6 Life satisfaction ....................................................................................... 191 2.3.7 Psychological well-being ........................................................................ 193

2.3.8 Satisfaction with work ............................................................................. 194 2.3.9 Work vigour ............................................................................................ 198 2.3.10 Work dedication .................................................................................... 200 2.3.11 Work absorption .................................................................................... 201 2.3.12 Depression ............................................................................................. 205

2.3.13 Anxiety .................................................................................................. 207 2.3.14 Stress ..................................................................................................... 210 2.3.15 Emotional exhaustion ............................................................................ 212 2.3.16 Cynicism ................................................................................................ 215 2.3.17 Professional efficacy ............................................................................. 218

2.3.18 Summary of the significant predictors of the hierarchical multiple

regressions ........................................................................................................ 220

2.3.19 Post-hoc analysis: Examining moderation between the most common

predictors for the outcomes .............................................................................. 224 2.3.20 Post-hoc analysis: What happened to humour? ..................................... 230 2.3.21 Post-hoc analysis: An examination of gender ....................................... 232

2.3.22 Post-hoc analysis: What predicts negative spillover? ........................... 234 2.3.23 Post-hoc analysis: Understanding positive spillover ............................. 236

2.4 Discussion .......................................................................................................... 238

2.4.1 Limitations and strengths of Study 1....................................................... 255 Chapter 3, Study 2: Longitudinal modelling ............................................................ 257

3.1.1 Hypothesis for Study 2 ............................................................................ 259 3.2 Methods .............................................................................................................. 259

3.2.1 Participants .............................................................................................. 259 3.2.2 Recruitment of participants, survey methods and materials ................... 260

3.2.3 General process for longitudinal modelling ............................................ 260 3.2.4 Introduction to SEM and associated terminology ................................... 261 3.2.5 Assessing model fit ................................................................................. 263

3.2.5.1 Normed Chi-Squared statistic. ......................................................... 264 3.2.5.2 Root Mean Square Error of Approximation (RMSEA). .................. 265

3.2.5.3 Akaike Information Criteria (AIC). ................................................. 267 3.2.5.4 Comparative Fit Index (CFI) ............................................................ 267 3.2.5.5 Expected Cross-Validation Index (ECVI). ....................................... 268

3.2.6 Early SEM models .................................................................................. 269 3.2.7 Confirmatory Factor Analysis (CFA) ..................................................... 270

3.2.8 Models to be considered in the CFAs and for longitudinal modeling .... 271

3.2.9 Constructing composite variables for the longitudinal models ............... 273

3.2.10 Naming the composite variables ........................................................... 275 3.2.11 Calculations of the composite variables ................................................ 276 3.2.12 Analytical strategy for longitudinal modelling ..................................... 276

3.2.12.2 Model trimming. ............................................................................. 280 3.2.13 Summary of methods used for the longitudinal modeling .................... 281

3.3 Results of the Longitudinal Modeling ................................................................ 282 3.3.1 Sample size and characteristics ............................................................... 282

3.4 Time 1 SEMs as a basis for longitudinal models ............................................... 286

3.5 Confirmatory factor analyses (CFAs) ................................................................ 288

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3.5.1 Confirmatory factor analysis of Well-Being model ................................ 289

3.5.2 Factor Score Weights for Well-Being model .......................................... 290 3.5.3 Confirmatory factor analysis of the Mental Distress model ................... 292 3.5.4 Factor score weights for the Mental Distress model ............................... 294

3.5.5 Confirmatory factor analysis for the Well-Being-Mental Health model 295 3.5.6 Factor score weights for the Well-Being – Mental Health model .......... 297 3.5.7 Confirmatory factor analysis for the Work Engagement model, based on

the scales of burnout and work engagement .................................................... 297 3.5.8 CFA for Burnout and Engagement alone ................................................ 299

3.5.8.1 One-factor CFA. ............................................................................... 299 3.5.8.2 Two factor CFA. .............................................................................. 300

3.5.9 Confirmatory Factor Analysis for the Work Engagement model ........... 301 3.5.10 Factor score weights for the Work Engagement model ........................ 302 3.5.11 Confirmatory factor analysis of the Integrated model .......................... 303

3.5.12 Factor score weights for the Integrated model ...................................... 308 3.6 Comparing the longitudinal models ................................................................... 310

3.6.1 Competing sets of longitudinal models ................................................... 310 3.6.2 The longitudinal Well-Being Model ....................................................... 313 3.6.3 The longitudinal Mental Distress model ................................................. 316 3.6.4 The longitudinal Well-Being – Mental Health model............................. 318

3.6.5 The longitudinal Work Engagement model ............................................ 319 3.6.6 The longitudinal Integrated model .......................................................... 323 3.6.7 Synchronous correlations, standardized regression weights and

significance of paths in the longitudinal models .............................................. 325 3.4.8 Individual Factors in the longitudinal models ......................................... 330

3.6.9 Positive Workplace Factors in the longitudinal models .......................... 330 3.6.10 Negative Spillover in the longitudinal models ...................................... 331

3.6.11 Overall Well-Being in the longitudinal models .................................... 331 3.6.12 Mental Illness in the longitudinal models ............................................. 334

3.6.13 Work Engagement in the longitudinal models ...................................... 335 3.6.14 Gain and loss spirals.............................................................................. 336 3.6.15 Squared multiple correlations from the models .................................... 340

3.6.16 Summary of the results of the longitudinal models .............................. 340 3.7 Discussion of the longitudinal models ............................................................... 343

3.7.1 Discussion of the Time 1 SEMs .............................................................. 344 3.7.2 Confirmatory factor analyses .................................................................. 346 3.7.3 Factor score weight from the CFAs ........................................................ 352 3.7.4 How factor score weights explain the relationships of the Integrated model

.......................................................................................................................... 355

3.7.5 The longitudinal models .......................................................................... 357

3.7.6 Stability and change in the longitudinal models ..................................... 359

3.7.6.1 Stability in the longitudinal models ................................................. 359 3.7.6.2 Change in the longitudinal models ................................................... 362

3.7.7. Limitations and strengths of Study 2 ..................................................... 370 3.7.8 Conclusions ............................................................................................. 371

Chapter 4: Discussion of research findings and conclusions ................................... 375

4.1 The developmental equation, D f PPCT ........................................................ 376 4.1.1 P, the person: The generative disposition ............................................... 376 4.1.2 P, the person: Their demand characteristics ............................................ 376

4.1.3 C, the context. ......................................................................................... 377

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4.1.4 T, Time. ................................................................................................... 377

4.1.5 Summary of D f PPCT. ........................................................................... 378 4.2 Major findings ................................................................................................ 378 4.3 Interesting non-findings ................................................................................. 384

4.4 Applications of the research ........................................................................... 385 4.5 Future research ............................................................................................... 388 4.6 A final word ................................................................................................... 393

References ................................................................................................................ 395 Appendices ............................................................................................................... 442

Appendix A: Call for volunteers from the university alumni .................................. 442 Appendix B: Call for volunteers from the public hospital ....................................... 443 Appendix C. Time 2 Call to action .......................................................................... 444 Appendix D: Time 3 Call to action .......................................................................... 445 Appendix E: Second and third reminder calls to action ........................................... 446

Appendix F: Measures used in Study 1 and 2 .......................................................... 447 Appendix G: Simple slopes of the moderated regression analyses .......................... 454

Appendix H: Results of the Time 1structural equation modelling ........................... 457 Appendix I: Confirmatory Factor Analyses for the longitudinal models ................. 469 Appendix J: Results of the longitudinal models....................................................... 483 Appendix K: Terms and glossary for Study 2, Longitudinal modelling .................. 510

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List of Tables

Table 2.1 Variables in each block as blocks are entered into hierarchical multiple

regressions …………………………………………………………………170

Table 2.2 Retention of participants over time, with percentages of original sample of

participants ………………………………………………………………...176

Table 2.3 Correlations between the variables included in the hierarchical multiple

regressions ……………………………………………………………182-188

Table 2.4 Results for the three steps of hierarchical multiple regressions for life

satisfaction ………………………………………………………………...192

Table 2.5 Results for the three steps for the hierarchical multiple regression for

psychological well-being ………………………………………………….195


satisfaction with work ……………………………………………………..197

Table 2.7 Results for the three steps for the hierarchical multiple regression for work

vigour ……………………………………………………………………...199


dedication ………………………………………………………………….202


absorption …………………………………………………………………204


depression………………………………………………………………….206


anxiety ……………………………………………………………………..208


stress ……………………………………………………………………….211

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emotional exhaustion ……………………………………………………...214


cynicism ……………………………………………………………...……217


professional efficacy ………………………………………………………219

Table 2.16 Summary of beta weights or the predictor variables for the hierarchical

multiple regressions..………………………………………………………223

Table 2.17 Results at Step 2, showing the significant interactions in the moderated

regression analyses……...…………………………………….……………227

Table 2.18 Simple slopes for the predictor variable (X1) and the criterion variable (Y)

at Low and High levels of the second moderating variable (X2).………….228

Table 2.19 Z scores for the indirect effects between humour and the outcomes,

through dispositional optimism and coping self-efficacy as the

mediators…………………………………………………………………...231

Table 3.1 Latent and observed variables used in confirmatory factor analyses

……………………………………………………………………………...272

Table 3.2 Factor Score weights for composite variables for the Well-Being model

…………………………………………………………………………………292

Table 3.3 Factor Score weights for composite variables for the Mental Distress

model ………………………………………………………………….…...295

Table 3.4 Factor Score weights for composite variables for the Well-Being – Mental

Health model……………………………………………………………….298

Table 3.5 Factor Score weights for composite variables for the Work Engagement

model……………………………………………………………………….303

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Table 3.6 Factor score weights for the composite variables for the Integrated

model…………………………………………………………………….…309

Table 3.7 Improvement in the fit of models in the Well-Being model by including the

auto-lagged pathways from Time 1 to Time 3…………………………….311

Table 3.8 Results of longitudinal model testing for Well-Being Model…………..315

Table 3.9 Results of longitudinal model testing for Mental Distress Model ……..317

Table 3.10 Results of longitudinal model testing for Well-Being-Mental Health

models ………………………………………………………………….….320

Table 3.11 Results of longitudinal model testing for the Work Engagement model

……………………….……………………………………………………..322

Table 3.12 Results of longitudinal model testing for the Integrated model……….324

Table 3.13 Effect sizes of the standardized regression weights for the auto-lagged

and cross-lagged paths for all the longitudinal models ………….….332-333

Table 3.14 Squared Multiple Correlations for all models for Time 2 and Time 3

composite variables ……………………………………………………..…341

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List of Figures

Figure 3.1 Simplified representation of the components used in SEM …………..262

Figure 3.2 Simplified representation of the confirmatory factor analyses………..271

Figure 3.3 Representation of the basic relationships to be tested in the longitudinal

analyses…………………………………………………………………….277

Figure 3.4 The best fitting model for the Well-Being model, E, the Trimmed

Reciprocal………………………………………………………………….315

Figure 3.5 The best fitting of Mental Distress model, E, the Trimmed Reciprocal

………………………………………………………………………….…..317

Figure 3.6 The best fitting of the Well-Being-Mental Health model, E, the Trimmed

Reciprocal ………………………………………………………………....320

Figure 3.7 The best fitting of the Work Engagement model, E, the Trimmed

Reciprocal …………………………………………………………………322

Figure 3.8 The best fitting of the Integrated models, E, the Trimmed Reciprocal

model…………………………………………………………………….....324

Figure 3.9 Weighting of the auto-lagged and cross-lagged paths in the Integrated

model.............................................................................................................339

Figure 4.1 Proposed cusp catastrophe for the relationship between work engagement

and burnout……………………………………………………………..….391

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List of Appendices

Appendix A: Call for volunteers from the university alumni ………………….….441

Appendix B: Call for volunteers from the public hospital …………………….…..442

Appendix C. Time 2 Call to action ………………………..…………….………...443

Appendix D: Time 3 Call to action ………………………………………………..444

Appendix E: Second and third reminder calls to action……………….…………..445

Appendix F: Measures for Study 1 and 2 ……………………………..…………..446

Appendix G: Simple slopes of moderated regression analyses…..….....………….453

Appendix H: Results of the early structural equation modelling ………………….456

Appendix I: Confirmatory Factor Analyses for the longitudinal models.…………468

Appendix J: Results of the longitudinal models…………………………….……..482

Appendix K: Terms and glossary for Study 2, Longitudinal modelling …….…….510

1

Chapter 1: Theories and literature review of Bronfenbrenner‟s developmental

equation, applied to individuals and the work-life interface

The fullness of life is not just our working selves but also our non-work or

family selves. It is difficult to explain an individual‟s well-being and mental health

by only exploring the workplace factors that influence well-being and mental health,

without considering the individual‟s out-of-work responsibilities and activities,

whether any or all of these factors can have positive or negative influences, and

without considering the person engaged in all these roles. Yet much of the

organizational psychology literature has only recently included the positive effects of

work and without any particular focus on the „person‟ who is doing the work (Frone,

2003). Working adults are treated as a homogenous group of individuals, upon whom

workplace factors, such as working hours, have similar results. Where individual

differences are introduced, these are often limited to age, gender and negative affect

(for example, de Jonge et al., 2001). Whilst men and women are dissimilar in

obvious ways, such as the ability to bear children, gender is not the major difference

as this demarcation suggests (Barnett & Rivers, 2004). Similarly, using age reflects

chronological differences, but does not account for the current life stage of an

individual. For example, later childbearing in women could mean that comparing 40

year old women may not account for one having a teenage child, another a two year

old child, and another who has not had children. In the positive psychology literature,

whilst the characteristics and strengths of the individual are explored, individuals are

not studied in their usual context as working adults, or parents, or adult children

caring for aging parents.

In order to fully account for all influences on the working adult,

Bronfenbrenner‟s Bioecological Model of human development will form the

2

framework of this thesis. This theoretical perspective will allow person-environment

interactions (proximal and distal) to be implicitly explored, across the lifespan and at

different lifestages, and with the multiple roles of work and family. Whilst

Bronfenbrenner‟s model was originally formulated to account for child development

(Bronfenbrenner, 1979; Bronfenbrenner & Morris, 1998) and applied to successfully

understand the processes of child development (Steinberg, Darling, Fletcher, Brown,

& Dornbusch, 1995), it has also been applied to adult development. The ecological

framework adds to the explanatory power for the work-life interface, by including

more breadth to the factors to be considered as important to the work-life interface

(Barnett, 1998; Grzywacz & Marks, 2000b). There is a similarity of

Bronfenbrenner‟s model to Bandura‟s social learning and behaviour (Bandura,

1986), although Bronfenbrenner has broader outcomes about lifelong development,

rather than being focused on learning outcomes.

This chapter will start with an outline of Bronfenbrenner‟s bioecological

model, and his conceptualisation of the model‟s components. The components of the

person, their context and time frame will then be examined, examining the relevant

theoretical bases and research literature for the component that will be used to

understand how competent development would occur over time. For example, when

considering the person, self-regulation can explain how individual differences lead to

higher well-being and mental health. Similarly, when considering the context in

which the person is active, role theory can explain how spillover, gender role

attitudes, and role salience influence an individual‟s enactment of their life‟s roles.

1.1 Bronfenbrenner’s Bioecological Model of Human Development

Bronfenbrenner‟s bioecological systems theory states that competent

development is the outcome of the bidirectional interactions between an active

3

individual and a dynamic environment. As it began with understanding child

development, it was framed in terms appropriate for the settings in which a child

develops (Bronfenbrenner, 1979), the principles and hypotheses that Bronfenbrenner

gave can be equally applied to adult development in the work and family settings of

adult life, as shown in research by Barnett (1998) and Grzywacz and colleagues

(Grzywacz & Bass, 2003; Grzywacz & Marks, 2000b). The formulations of

organizational stress (Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964) and social

cognitive theories (Bandura, 1986) have similar bases to Bronfenbrenner. Both

theories include the characteristics of the individual and their environment when

considering how the relevant outcomes are achieved, as the response to stress or

learning outcomes, respectively. The bioecological model accounts for each setting

within which the individual acts and the dynamic relationships between the settings

(Bronfenbrenner & Morris, 1998, 2006).

For the purposes of this thesis, the effective and competent development in

Bronfenbrenner‟s model is viewed as the outcomes of maximising gains and

minimising the loses that occur throughout the life span and the resilience of

maintaining functioning in the face of challenge (P.B. Baltes, Lindberger, &

Staudinger, 1998; Bronfenbrenner, 1979; Masten, 2001). The best outcomes of

competent development and highest levels of psychological functioning over time

lead to mature adults as healthy and fit, with an alert and vital mind, maintaining

meaningful roles, either in a continuing vocation or in new activities, maintaining

relationships with family and friends and involvement with the community, and

finally, to be effective and wise problem-solvers (Csikszentmihalyi & Rathunde,

1998). Given the diversity of these outcomes, effective and competent development

will be measured in this thesis in a number of ways that reflect the long term balance

4

of gains and losses and maintenance of positive functioning, being measured as well-

being, mental health (or the absence of mental illness), burnout, and work

engagement. Well-being and work engagement fit together as the positive markers of

competent development, whilst mental illness and burnout fit together as the negative

markers. Well-being will be measured as both subjective well-being (Diener, Lucas,

& Oishi, 2002; Diener, Suh, Lucas, & Smith, 1999) and psychological well-being

(Ryff, 1989; Ryff & Keyes, 1995). Mental health will be taken as the absence of

depression, anxiety, and stress (Beck, 2002; P. F. Lovibond & S. H. Lovibond,

1995). Burnout will be measured as exhaustion, cynicism and the loss of professional

efficacy (Maslach, Schaufeli, & Leiter, 2001) and work engagement will be

measured with the dimensions of vigour, dedication and absorption (Gonzalez-Roma,

Schaufeli, Bakker, & Lloret, 2006; Schaufeli, Salanova, Gonzalez-Roma, & Bakker,

2002). By considering a broad range of outcomes, a better understanding of

competent outcomes can be achieved. It should be noted that the genetic component

of development is implied in this model and is understood to be expressed only as a

function of the individual‟s environment (Bronfenbrenner & Ceci, 1994). As such the

genetic component is beyond the scope of the current research project.

The first description of the ecological system focused on the context of

development in great depth, which allowed researchers to pinpoint the factors, both

proximal and distal, that are of importance to an individual‟s development. This

ecological environment was conceived as a set of concentric spheres, nested within

each other, similar to a set of Russian dolls (Bronfenbrenner, 1979). The individual

sits at the centre of their life domains, or spheres of influence which grow larger and

more distant from the individual. The closest is the microsystem, the immediate

settings in which the individual operates. Next is the mesosystem, where two

5

microsystems interact, then the exosystem of indirect influences, for instance of a

partner‟s job or government policy, followed by the macrosystem, as the influence of

society or culture, and finally, the chronosystem, which defines the particular point in

history (Bronfenbrenner, 1979; Bronfenbrenner & Morris, 1998). Each domain in

which the individual operates contain the specific roles, activities and relationships.

For example, the individual can operate as spouse or partner and/or parent in their

household setting (one microsystem), as an employee (a second microsystem),

balancing commitments to home and work (the mesosystem), as a citizen of

Australia (the macrosystem) in the early years of the 21st century (the chronosystem).

Whilst other individuals may have the same macrosystem (Australia) and same

chronosystem (current time frame), differing personal circumstances, such as divorce

or self-employment will change the experiences within the microsystems and how

those microsystems interact. These elements provide the experiences that the

individual has in the domain or microsystem and provide a way in which to capture

the development influences around the individual. By specifying the nature of each

element, a richer understanding of the dynamic processes between individual and

environment can be gained. Similarly, appreciating the changing nature of roles,

activities and relationships can reflect how different lifestages influence

developmental outcomes over time, as the individual masters new skills and

situations across the lifespan (Bronfenbrenner, 1979).

Most of the research on work-life issues focuses on the mesosystem of the

individual‟s work and home domains and the nature of the boundary and balance

between the demands and needs of the two microsystems (Barnett, 1998; Voydanoff,

2002).Whether the time and commitment required for participation in a role leads to

strain and conflict with other roles in other settings (Goode, 1960; Greenhaus &

6

Beutell, 1985) or enhancement and facilitation with the other roles (Frone, 2003;

Greenhaus & Powell, 2006; Marks, 1977; Marks & MacDermid, 1996) depends on

the dynamics of the individual interacting with the components of each setting.

When the expanded formulation of the bioecological model was published

in1998, Bronfenbrenner noted that this emphasis on context obscured a necessary

and essential component of the process, that of the individual (Bronfenbrenner &

Morris, 1998). The extension of the original model, now called the bioecological

model has the individual as an active participant engaged in bidirectional

relationships with a dynamic environment. The equation (1),

D ƒ PPCT (1)

conceptualises these relationships where the developmental outcome, D, is a function

ƒ of the interactions or proximal processes, P, between the person‟s characteristics,

P, and their context or environment, C, that occur with time, T. The characteristics of

the person are based on their disposition, gender and resources and their demand

characteristics.

The proximal processes that occur between the person and their environment

are considered to be the drivers of development and involve activities which occur

regularly and with increasing complexity. These activities are reciprocal exchanges

and interactions with people and symbols and can be moderated by the individual‟s

developmental capacity and the influence by significant others. Examples of

activities that increase in complexity over time are parenting, developing a career,

learning skills, problem solving and managing multiple roles. These processes are

those that an active, competent person would use in managing and adapting to the

maturation of roles and responsibilities are fundamental to competent development

(Bronfenbrenner & Morris, 1998). These bidirectional influences form a systems

7

approach to development where each level, whether it is genetic, neural, behavioural,

or environmental, is interconnected (Gottlieb, Wahlsten, & Lickliter, 1998). Each

part of the equation can operate at the micro-, meso- or macrosystem level in such a

way that the potential for development of the individual can be accounted for. For

example, time can be regarded in the microsystem by whether or not proximal

processes are continuous in the mesosystem by the periodicity of the proximal

processes and in the macrosystem by changes in the broader community

(Bronfenbrenner & Morris, 1998).

Specifically, proximal processes between the active individual and their

dynamic environment involve activities which occur regularly and with increasing

complexity over time. The processes that are involved in managing and adapting to

the maturation of roles and responsibilities are fundamental to competent

development and can be moderated by the individual‟s developmental state and the

influence by significant others (Bronfenbrenner, 1979; Bronfenbrenner & Morris,

1998, 2006). Some examples of the activities that increase in complexity over time

are parenting, developing a career, learning recreational or sporting skills, problem

solving, and managing multiple roles. Similarly, implicit in career development is

that the individual‟s job description becomes more complicated and involved over

time, such that early career jobs involve less responsibility and input than senior

positions that oversee many employees and require detailed knowledge of the many

facets of the relevant business situation or management goals. The active individual

therefore uses available skills and their personal and environmental resources to

successfully make the transitions from lower levels to higher levels of complexity

and ability in their lives. Proximal processes are therefore reliant on the individual‟s

characteristics and their particular situation in life and can be many and varied,

8

making the processes difficult to define. As such, the focus in the thesis will be

directly on the individual and their context. Rather than attempting to specify directly

which processes are involved or how exactly an interaction may occur between

individual characteristics and contextual factors, it is taken that these processes can

be implied from the influential personal and contextual factors. As such, proximal

process will be implied from the results of the research, rather than explicitly stated.

Understanding the individual, their family and their work and spillover

between roles will identify the factors that are most important to well-being at the

work-life interface. In the current research, explicitly using the ecological equation

will highlight the individual as an active participant of the system and in the

interactions between important life spheres. Positive person-environment interactions

contribute to competent and resourceful outcomes. In the face of adversity,

competence and the adaptive use of personal and environmental resources foster the

development of resilient children (Kumpfer, 1999; Yates & Masten, 2004).

Resilience can also be considered the actions of a competent person when facing

risky or adverse situations (Masten & Reed, 2002). Likewise, in adults the ability to

use personal and environmental resources facilitates well-being and role balance

(Barnett, 1998; Voydanoff, 2005b) and in families, it allows adjustment and

adaptation to strains caused by demands on the family unit (J. M. Patterson, 2002).

Negative person-environment interactions hinder well-being by increasing

dysfunctional behaviours, for example, when problem drinking is exacerbated by

increasing pressure from home and work (Grzywacz, 2000; Grzywacz & Marks,

2000a).

The purpose of the current research is to combine knowledge from diverse

strands of psychology, such as from the health and organizational domains and from

9

positive psychology, in a form that weighs the person and context components of the

work-life puzzle. By basing the research on Bronfenbrenner‟s bioecological model,

neither part can be overlooked. The research to be conducted in the current thesis

will use a variety of analyses to understand the competent individual. The first study

will involve regression analyses and the second study will involve longitudinal

modelling to explore and model the predictors of competent behaviour. This research

program will explore the working individual from different viewpoints: how

individuals understand themselves and how outcomes can be understood in a large

sample at one time and across time.

1.2 Theories for D, the developmental outcomes, defined by well-being,

mental health, burnout and work engagement

A range of outcomes will be used to describe competent development and to

reflect the diversity of outcomes used in the literature. The markers of competent and

successful individual development and management of multiple roles will be defined

as first, well-being, as life satisfaction and psychological well-being, second, mental

health (as the absence of depression, anxiety and stress) and third, as the affective

work state of burnout and it‟s recently quantified opposite engagement.

Bronfenbrenner describes competent outcomes as the result of the individual‟s

actions in each of the domains, or microsystems, in which they have roles, activities

and relationships (Bronfenbrenner, 1979).

1.2.1 Well-being

Well-being will be measured by subjective well-being, as life satisfaction

(Diener, Emmons, Larsen, & Griffin, 1985) and psychological well-being, as the

components described by Ryff (1989). Whilst subjective well-being has been factor

analysed into the three components of life satisfaction, positive affect and negative

10

affect (Arthaud-Day, Rode, Mooney, & Near, 2005), only life satisfaction will be

considered in this thesis as there is issue of whether affect should be treated as a state

or trait of the individual, which blurs the construct that is being measured and studied

(Wainwright & Calnan, 2002).

Psychological well-being is amongst the loosest and poorly defined terms

used for outcome measures in the research literature on personality, health and work-

life issues. It is used as an umbrella term rather than a specific construct and can be

taken as any positive outcome or the absence of negative outcomes. For example, in

a review of 25 studies on the benefits of optimism, Scheier, Carver and Bridges

(2002) reported „psychological well-being‟ had been measured as lower depression,

less anger, less loneliness, anxiety and distress, fewer perceived hassles, less negative

mood, and lower stress levels, in addition to higher life satisfaction, higher job

satisfaction, and higher self-esteem. Given that psychological well-being is central to

the conception of competent development in this thesis, it shall be defined only as

described by Ryff, (1989) as the six components that measure challenged thriving

and are based on ethical and philosophical traditions. These components are

autonomy, environmental mastery, personal growth, positive relations with others,

purpose in life, and self-acceptance (Ryff, 1989; Ryff & Keyes, 1995) and represent

the conception of the good life, full of meaning and worthwhile activities and

relationships.

By including both life satisfaction and psychological well-being in well-

being, this thesis brings together recent research that shows that these constructs are

separate and together add valuable information about the individual‟s mental state

(Ryan & Deci, 2001). Life satisfaction can be viewed as hedonia, as happiness and

pleasure in life, the „happy life‟, whilst psychological well-being, eudaimonia,

11

defines the purpose and markers of challenged thriving, the „good life‟ (Keyes,

Shmotkin, & Ryff, 2002).The combination brings together measurement of a happy

and meaningful life.

The theoretical explanations of well-being considers the influences of

objective conditions or the circumstances around the person (bottom-up), such as

age, gender, income, life domains and culture, and of subjective conditions, such as

the person‟s disposition/personality (top-down) to be the „cause‟ of the person‟s

well-being, along with adaptation and goals (Diener & Lucas, 2000; Diener et al.,

1999). Using an ecological framework, however, allows all of these theoretical

inputs to be acknowledged and accounted for, however limited the input may be.

Whilst the situation factors have limited influence on well-being, i.e. demographics

account for only 15% of variance (Argyle, 1999, in Diener & Lucas 2002), Easterlin

(2006) calculated that life satisfaction across the life span was the sum of satisfaction

with various life domains, such as work, family and financial, rather than due to

personality factors. However, given the high correlation between overall life

satisfaction and domain satisfactions, these calculations may reflect how these

relationships ebb and flow over time, rather than how domain satisfaction „causes‟

life satisfaction over time.

The subjective or top-down influences on well-being are more varied, with

the theories about personality explaining how well-being is achieved. Personality

influences are explained by temperament (hereditability of happiness) (Lykken &

Tellegen, 1996), traits (DeNeve & Cooper, 1998) and dispositions, such as optimism

(Armor & Taylor, 1998) and self-efficacy (Schwarzer & Renner, 2000). These

personality theories will be explored as part of discussions later in this chapter on

understanding the person who is occupying and managing multiple roles‟.

12

Adaptation to change and goals are also considered as precursors to well-

being. Adaptation to changing circumstances, such as widowhood or winning the

lottery, are evidence that there is a set point for well-being, that despite good or bad

fortune, an individual will return to previous levels of happiness after a period of

time (Fujita & Diener, 2005; Shmotkin, 2005). This hedonic treadmill can explain

the stability of well-being over time, although the expectation that an individual

inevitably returns to their original functioning, revisions take into account that whilst

individuals are mostly happy, they also have multiple set points for different domains

and there are individual differences in the way people react to similar situations

(Diener, Lucas, & Scollon, 2006). Goals can be seen as the way by which individuals

conceive their future, defining the direction and focus of all that individual‟s actions

(Emmons, 2003). Expectations about the outcomes of goals are also important in

how likely the individual is to persist toward their goals and whether the individual

will disengage from insoluble problems (Aspinwall, 2001; Carver & Scheier, 1998).

The adaptive value of persistence and disengagement will be explored further

through the effects of dispositional optimism on competent development.

1.2.1.1 Prevalence. Given the historical focus of psychology on pathology

(Seligman & Csikszentmihalyi, 2000) it may be surprising to consider that across

many studies in large populations, most people are happy most of the time. Although

many different scales can be used to measure well-being, the results are remarkably

consistent. By bringing together the results of almost 916 studies, with over 1 million

participants in total in 45 countries, Myers and Diener (1996) calculated that the

mean „happiness‟ rating, on a scale of 0 to 10, was 6.75, with most surveys reporting

ratings between 5.5 and 7.75, and very few surveys where people rated their

happiness at less than 5.

13

In Australia, the Australian Quality of Life Index has been calculated in a

large representative sample several times each year from 2001 onwards. The 18th

edition shows that within that time frame, the Personal Well-being Index (PWI) scale

has been remarkably stable in that time. The mean has ranged only between 73.4 and

76.4, on a scale of 0 to 100 that rates satisfaction with life as a whole. The authors

believe that this level of stability represents normal well-being (or homeostasis)

where people manage their lives successfully and are optimistic about the future

(Cummins, Woerner et al., 2007). The greatest PWI (i.e. PWI > 79) was associated

with high levels of income and the presence of a partner, whilst those with the lowest

PWI (i.e. PWI < 70), and most at risk of homeostatic failure are unemployed, have a

low income (under $15,000 pa) and live without a partner.

With regards to work status, not earning an income was particularly adverse

for the well-being of men between 26 and 55 years of age (Cummins et al., 2003;

Cummins, Woerner et al., 2007). Interestingly, whilst many people note that they

would like to reduce their working hours, this did not translate into greatly reduced

rating of their well-being. Rather it was insufficient work, not too much work, which

had the stronger, more detrimental effect on individual‟s PWI. Underemployment is

linked to lower incomes, a lower sense of achievement in life and boredom and

among men between 35 and 55 years of age (Cummins, Woerner et al., 2007). In

addition, looking for a job, whether employed or unemployed, reduced satisfaction

with life‟s achievements whilst being engaged in work gave a sense of purpose to life

and was central to overall well-being (Cummins, Woerner et al., 2007). The specific

predictors of well-being, measured as life satisfaction and psychological well-being,

will be considered in the later sections of this chapter when the effects of the person

and the context are considered.

14

1.2.1.2 Stability of well-being. Given the focus of research on over-work as a

stressor, this finding that lack of work reduced quality of life should make easy

acceptance of the „work-life imbalance‟ mantra less likely. Has feeling busy been

transformed into being pressured? By interpreting emotional distress as a possible

loss of efficacy, the individual could be underestimating how well they do actually

manage their role demands (Llorens, Schaufeli, Bakker, & Salanova, 2007),

unrealistically comparing themselves to an ideal worker/parent ideal, or not

questioning the media representations of „having it all‟. Despite the widespread

media preoccupation with negative outcomes, the stability of well-being is important

as it denotes a substantial level of happiness that buffers individuals without their

being particularly aware of that happiness. Longitudinal studies show that mature

coping mechanisms, good relationships, particularly marriage, having sufficient

income to meet one‟s needs and having meaningful work all contribute to well-being

across the lifespan (Howard, 1992; Vaillant, 2000, 2002). Well-being is reduced by

excessive alcohol use, maladaptive coping mechanisms (Vaillant, 2000, 2002) and by

materialism, which is associated with reduced relationship and family satisfaction

(Nickerson, Schwartz, Diener, & Kahneman, 2003; Solberg, Diener, & Robinson,

2004). Similarly, a focus on extrinsic goals was associated with more narcissism and

depression than the pursuit of intrinsic goals (Kasser & Ryan, 1996).

1.2.1.3 Australian health and working provisions. There are two important

caveats, particularly in comparison to the USA that must be taken into account when

studying the well-being and mental health of Australian workers. Socioeconomic

status (which includes job status) has been associated with poorer health and well-

being outcomes in the USA because of the link between the provision of health care

and employment (for example, Adler et al., 1994). However in Australia, every

15

person, regardless of employment status or income has access to government funded

heath care with access to general practioners and public hospital services at little or

no cost. Similarly, Australian Federal Government regulations (available at

www.workplace.gov.au) include provision of sick leave and minimum four weeks‟

holidays in employment contracts to provide a safety net for employees. These

provisions mean that Australian employees have medical advantages that are not tied

to any employment and employment conditions that may not be available to US

employees. These Australian conditions could therefore limit the importance of SES

as a predictor of health and well-being outcomes.

1.2.2 Mental health, as the absence of mental illnesses

This thesis will focus on the cognitive models of mental health rather than

any biological basis. Whilst a link between genetic factors and environment has been

shown in a longitudinal study of Australian teachers, depression was more strongly

linked to multiple adverse life events, rather than susceptible genetic subtype

(Wilhelm et al., 2006). As noted previously, the link between genetics and

environment is beyond the scope of this thesis, although this may provide an

interesting avenue for future research. Further, the focus of the current thesis is on

the individual difference, work and family variables (to be defined and described

later in this chapter) that are risk and protective factors for mental illness, rather than

a wider range of variables that have been examined in previous research.

The cognitive model of depression (Beck, 2002) has been remarkably

successful in explaining the underlying processes involved in depression and many

other mental disorders, such as anxiety disorders, panic disorders, and personality

disorders and their successful treatment (Beck, 1991). The maladaptive schemas that

the individual use to process the positive and negative events around lead to

http://www.workplace.gov.au/

16

dysfunctional cognitive styles, which in turn lead to a specific vulnerability to

developing depression (Alloy et al., 2000). Alongside the schemas that give rise to

cognitive vulnerability, behavioural and verbal interactions with other people can

reinforce and intensify depression which can lessen the social support available to

individuals. These individuals not only believe that they have poorer social skills, but

also exhibit behaviours, such as a monotonous tone of voice and avoiding eye

contact, that provoke negative responses in other people (Segrin & Abramson, 1994).

Excessively seeking reassurance from family and friends can hamper interpersonal

relationships, as does the tendency to seek negative feedback about oneself.

Vacillating between seeking reassurance and seeking negative feedback leads to

rejection from peers (Joiner & Metalsky, 1995) and excessive reassurance seeking

has been specifically linked to depressive symptoms (Burns, Brown, Plant, Sachs-

Ericcson, & Joiner, 2006).

The role of cognition in mental health is formalised by Beck‟s cognitive

models of depression. Seen as the way in which individuals process information

about themselves, their world and their future, negativity is shown by selective

abstraction, overgeneralization, dichotomous categorisation and personalization of

the problems that occur to the individual and resulting in distorted and depressive

cognitions (Beck, 2002). This altered thinking changes the way that individual‟s

view the world, resulting in sensitivity to negative or ambiguous cues (Wilkinson &

Blackburn, 1981), differences in depressed and non-depressed thinking (Alloy et al.,

1999) and differences in interpersonal relationships (Joiner & Metalsky, 1995).

There can also be changes in cognitions that are rational or irrational and changing

such cognitive patterns requires challenging the irrational thoughts to find more

logical and reasonable rational replacements (Ellis, 2004). Beck‟s cognitive models

17

of depression include the schemas that underpin automatic thoughts and are a

personal encyclopaedia of themselves and the world around the individual. Schemas

are shaped by the life stage when they are formed, vary functionally with the

situation, can be pervasive and a core feature of the individual (James & Blackburn,

2004). Schemas can give rise to a cognitive bias, both negative (Beck, 2002) and

positive (Cummins & Nistico, 2002), with the positive bias having the greater benefit

for mental health and well-being. The interpersonal causes of depression will be

discussed in the section on social support, as these involve the dysfunctional

interactions between the individual and people in their environment. The success of

cognitive therapy to change thinking patterns and depressive symptoms has been

shown with many years of successful therapeutic outcomes and these outcomes also

include problems with anxiety states, eating disorders, and marital problems (Beck,

2004; Hawton, Salkovskis, Kirk, & Clark, 2000; Kwon & Oei, 2003).

The theoretical understanding of stressors (precipitating events) and stress

(reactions) follow from the early research by Selye on the General Adaptation

Syndrome (Selye, 1976), which emphasized the individual‟s physiological response

to threatening events, with the process following from alarm to resistance and finally

to exhaustion, where further stress would lead to health problems. However,

inconsistent definitions and assumptions did not advance the understanding of stress.

Common to all and basic to the stress process is the interaction between the

individual and their environment and subsequent appraisals and actions, although the

emphasis may differ between the individual or the context (Wainwright & Calnan,

2002). From the individual‟s perspective, role theory (Goode, 1960; Kahn et al.,

1964), conservation of resources (Hobfoll, 2002) and coping with stress (Lazarus,

1993) are focused on the roles, resources and emotions, respectively, of the

18

individual, their characteristics and resources and how they navigate stressful

situations and relationships. The environmental factors, however, are explained

through the Demand-Control-Support (DCS) model (Karasek & Theorell, 1990),

where strain and poor health results from high work demands, limited workplace

control and low social support from co-workers. The difference in focus however

does not obscure the outcomes of mental health problems and poor health that arise

from not meeting environmental demands. The cognitive model, as applied to mood

and anxiety disorders has also been applied to stress and the prevention of stress,

with the focus on how schemas influence each individual‟s appraisal. Importantly,

the processes of appraisal and responses that follow from stress in the cognitive

model remain similar to other theories (Pretzer, Beck, & Newman, 2002).

Further to the analyses of subjective and psychological well-being, Keyes

(2002, 2005) has also shown that the absence of mental health is not the opposite of

well-being and is better considered as a separate factor. Whilst well-being, as

measured by the Australian Quality of Life surveys and as summarized by Meyer and

Diener (1996) is stable over time and among many people, mental health problems

are of significant concern to the community. In this thesis, mental health problems or

more precisely mental illness, will be considered as depression, anxiety and stress, as

more serious mental illnesses are beyond the scope of this research and are less likely

to be prevalent in the population of interest, that of working adults.

1.2.2.1 Costs and prevalence. The World Health Organization estimates that

10% of adults will have mental health problems at any given time, with individuals

having a 25% chance of developing mental health problems in their lifetime (World

Health Organization, 2001), which represents a substantial burden on the health

services of many countries (Reijneveld, 2005). In the Australian National Mental

19

Health Survey, 15.5% of the population met the DSM-IV criteria for affective,

anxiety and substance use disorders, although only 35% of individuals reporting a

mental disorder sought assistance for their problems, with general practitioners

providing most mental health services (Andrews, Henderson, & Hall, 2001).

Unfortunately, if depression, for example, is not treated, it can reoccur and become a

chronic disability over time. As a result, these individuals have a lower level of

overall health compared to individuals with chronic diseases, such as asthma or

diabetes. Where depression and a chronic disease are comorbid, the overall health for

that person is worse than having a chronic disease alone or depression alone (World

Health Organization, 2007).

In a review of the costs of mental health problems in Europe, McDaid, Curran

and Knapp (2005) reported that in Sweden, 27% of long-term sick leave is due to

mental health problems, whilst it was estimated that 0.5% of Dutch GDP was lost by

employees retiring early or becoming disabled, due to mental health problems. The

costs of mental health problems come from the direct costs of treatment, the indirect

economic costs from increased mortality, and the indirect economic burden that is

due to the loss in productivity (Wang & Kessler, 2006). Economic losses in

productivity are the greatest of these costs although there are differing calculations as

the full extent of the losses, whether only lost productivity was considered or both

paid and unpaid work was included (Luppa, Heinrich, Angermeyer, Konig, &

Riedel-Heller, 2007). In a population-based sample in South Australia, data from the

Health Omnibus Survey found 7% of South Australians had major depression and

11% had dysthymia, minor depression or partial remission from depression. The

Survey also calculated that of the $1921 million/year spent on health costs in total,

$1506 million (78%) could be attributed to lost productivity due to lost days at work

20

(absenteeism) or days of reduced work effort (presenteeism) (Hawthorne, Cheok,

Goldney, & Fisher, 2003). In the U.S.A., Kessler and Frank (1997) calculated that

the days that workers did not go to work due to an affective disorder (i.e.

absenteeism) was increased by co-morbidity with another affective, anxiety or

substance use disorder, rising from 4 million days lost per year to 15 million work

days lost per year. Where work impairment as considered, the days where work was

limited (i.e. presenteeism) rose from 20 million to 110 million days per year. When

physical health was added to mental health in the Midlife in the US (MIDUS) study,

the results showed that completely unhealthy individuals put less effort into their

work, were eight times more likely to have cutbacks on work days and six times

more likely to miss work days (Keyes & Grzywacz, 2005).

When Generalized Anxiety Disorders (GAD) were considered in conjunction

with Major Depressive Disorders (MDD) in two population-based studies in the

U.S., the MIDUS and the National Comorbidity Study, individuals showed similar

levels of impairment to social and work roles when suffering either disorder. There

was considerable overlap between the disorders, with many individuals with GAD

also having MDD and a large minority of individuals with MDD also having GAD.

Where individuals had comorbid disorders, the impairment, particularly to work

roles, experienced by the individuals substantially increased (Kessler, DuPont,

Berglund, & Wittchen, 1999). Depression was found to be more likely among

women, among individuals who are less educated, unemployed, have low incomes,

have poor health or are separated or divorced (Andrews et al., 2001; Hawthorne et

al., 2003). As the economy is increasingly reliant on the individual‟s mental output,

as the „knowledge economy‟, the losses from being physically absent, mentally

absent, or in poor health represent substantial costs to all levels of society, from the

21

individual, their families, their employers and to the wider community (de Vries &

Wilkerson, 2003). The loss of work to provide structure and identity for an individual

is seen in the higher levels of mental health problems among unemployed people

(Hawthorne et al., 2003).

Similarly, for individuals at work, the satisfaction they feel about their jobs is

linked to mental and physical health outcomes and follow similar trends of the

outcomes shown here. In a meta-analysis of 500 studies published after 1970,

Faragher, Cass and Cooper (2005) found the strongest relationships, measured as the

corrected combined correlations (similar in meaning to effect sizes), were between

job satisfaction and negatively, burnout (ř =.46), depression (ř = .41) and anxiety (ř =

.38) and positively, self-esteem (ř = .44). The lowest relationships were between job

satisfaction and cardiovascular disease (ř = .16) and musculoskeletal disorders (ř =

.08) (Faragher et al., 2005). Given the time and importance of role of work in the

individual‟s life, many hours spent each day in a role that is dissatisfactory can be

expected to have psychological consequences, which the meta-analysis shows has

occurred (Faragher et al., 2005). Conversely, where there is greater job satisfaction,

the individual is more likely to have better self-esteem and better mental health, as

they have less depression, anxiety and less burnout. The effect of the combination of

mental illnesses, as depression, anxiety and stress, reduces both productivity and

satisfaction with work, which would reinforce the negative outcomes. From these

studies, understanding the work-life conditions and individual characteristics that

most predict mental illnesses will assist in reducing the impact of mental illness on

the individual‟s effectiveness at work, therefore offering avenues to reduce those

losses and minimise the negative effects on productivity.

22

1.2.3 Burnout and work engagement

The construct of burnout and the related construct of work engagement will

also be included in the measurement of competent development in this thesis. Whilst

similar to stress, these constructs are regarded as the consequence of working

conditions, which in turn influence the individual‟s motivational-affective response

to their job, rather than a physiological or distress reaction implied by the stress

response. Burnout is multidimensional, characterised by emotional exhaustion,

cynicism, and the loss of professional efficacy (Maslach et al., 2001), whilst work

engagement is characterised by absorption, dedication and vigorous involvement

with one‟s job (Schaufeli et al., 2002). Interestingly, burnout and engagement are

used as the outcome measure more often by European researchers, whereas stress is

favoured by US and English researchers.

Whilst there are similarities, burnout is differentiated from stress by the

emphasis on interpersonal relations in the workplace as the basis for the development

of burnout (Maslach et al., 2001) whereas stress is more often framed as the response

to role stressors (Beehr & Glazer, 2005) or challenges to resources (Hobfoll, 1989).

Burnout also reflects the negative response an individual has to their job and the

disengagement from that job as opposed to the distress that the individual

experiences (Maslach et al., 2001). The balance between the demands and resources

in a job are seen as crucial to the development of burnout. This is theoretically

explained by the Job Demand-Resources (JD-R) model (Bakker, Demerouti, &

Verbeke, 2004; Bakker & Geurts, 2004), using the conception of resources as

defined by Hobfall‟s conservation of resources theory (Hobfoll, 1989). The Job

Demand-Resources model has broader definitions of the work conditions considered

as both demands and resources than the Demand-Control-Support model (Karasek &

23

Theorell, 1990), whilst leading to similar outcomes of burnout and poorer work

performance. As the relationship between job demands and resources underpins

burnout, there is a link to the concept of flow, which is an optimal or desired

experience arising from immersion in a task, lying between distress and boredom.

Challenges that are well in excess of the individual‟s skills and resources lead to

distress, whilst challenges that are considerably less than available resources lead to

boredom. Flow emerges from the happy medium, where challenges and skills are

well matched (Csikszentmihalyi, 2002). Flow shares similarities with work

engagement, although flow is regarded as a focused or creative state whereas

engagement is a general affective state around one‟s work.

The study of burnout began as the study of emotions in the workplace, with

interviews with human service workers identifying that intense involvement in the

problems and lives of other people lead helping professionals to exhibit emotional

exhaustion, cynicism and the loss of a sense of personal accomplishment. The

emotional exhaustion came from demanding workloads and conflict in relationships

at work, whilst the cynicism or depersonalization allowed distance and detachment to

cope with the intensity of emotional arousal from the work, and accompanied by a

loss of confidence in how well one is performing at work (Maslach, 1998; Maslach

& Jackson, 1981; Schaufeli & Buunk, 1996). At the centre of burnout is the

constancy of dealing intensely with another person‟s problems, where causes are

ambiguous or conflicted, solutions are difficult to achieve and the process is often

frustrated by lack of resources or long standing inequalities (Maslach & Jackson,

1981). Professionals can feel that there is little they can do to change the outcomes

for their clients, that the problems stay the same although the actual client with the

problem may change and that they lack support from their supervisors.

24

In contrast to research on stress and the individual‟s response to stressors,

burnout occurs in a workplace social context (Maslach, 1998) and occurs more

frequently or contagiously in work teams that already experience burnout. For

example, among Dutch police officers, when burnout was measured collectively at

the team level, individual members of that team were much more likely to have

burnout, indicating that the attitudes of one‟s co-workers can provide a barometer of

organization commitment and personal enthusiasm. Interestingly, in the same

population of police officers, work engagement in teams was similarly boosted by

individual work engagement (Bakker, van Emmerik, & Euwema, 2006).

Maslach and colleagues proposed that burnout came from a mismatch

between the person and their job and was increased by work overload (as jobs have

increasing intensity, take more time and become more complex); by lack of

appropriate control over work tasks and resources; by insufficient rewards both in

monetary terms and as recognition of effort; by the breakdown of community and

loss of support of managers and co-workers; by the absence of fairness in

organizational practices; and by a conflict in values between the individual and the

organization (Maslach & Leiter, 1997; Maslach et al., 2001). There is support for

these proposed factors. For example, increasing workload, job insecurity and the

problems associated with a hospital restructuring significantly increased the

exhaustion and cynicism in hospital workers, whilst personal resources such as self-

efficacy bolstered professional efficacy and buffered the individual against

exhaustion and cynicism (Greenglass & Burke, 2002). Among teachers and bank

workers, where employees had more work that they could complete, these greater

workloads increased emotional exhaustion whilst social support from co-workers

reduced the exhaustion they felt. Social support also reduced the likelihood that

25

employees wanted to leave their current employment (Houkes, Janssen, De Jonge, &

Nijhuis, 2001). In addition, being able to control work schedules to fit with family

responsibilities mediated between hours worked and burnout among married doctors

(Barnett, Gareis, & Brennan, 1999).

Expanding the burnout construct in recent years to work engagement reflects

how psychology is turning its attention to positive states, as shown by the influence

of positive psychology challenging the focus on mental illness and exploring well-

being and positive attributes (Seligman & Csikszentmihalyi, 2000). Recently,

research has begun to explore the affect and motivation of employees before burnout

develops with a continuum between fully engaged workers to those with burnout. By

framing burnout as the disengagement from previously meaningful and important

work, work engagement can be seen as the opposite of burnout. Vigour or energy is

considered opposite to exhaustion, dedication is opposite to cynicism, and absorption

is opposite to the loss of professional efficacy (Maslach et al., 2001). Engagement is

therefore the state of mind where work is seen as interesting, fulfilling and

worthwhile and where the individual is prepared to invest time and energy in their

job and separate from burnout (Bakker et al., 2006; Schaufeli et al., 2002). Work

engagement also represents an individual who is full of energy, fully involved in

their work and confident of their professional capabilities (Maslach et al., 2001).

The continuum has also been conceived to describe the phased development

of burnout, with the progressive onset of exhaustion then cynicism and finally the

loss of professional efficacy (Golembiewski, Boudreau, Munzenrider, & Luo, 1996).

However, recent European research has taken the view that work engagement is a

separate, although close related construct. Using the Maslach Burnout Inventory

(MBI, Maslach, Jackson, & Leiter, 1996) as a base, the Utrecht Work Engagement

26

Scale (UWES, Schaufeli et al., 2002) was developed with the components of work

vigour, work dedication and work absorption. Whilst subsequent research has shown

that the UWES and the MBI are separate and highly correlated in teachers (Hakanen,

Bakker, & Schaufeli, 2006), company managers and executives (Schaufeli, Taris, &

van Rhenen, 2008) and in hospitality workers (Pienaar & Willemse, 2007), it should

be noted that in the original study (Schaufeli et al., 2002), the fit indices of the

measurement models were only just acceptable (Browne & Cudeck, 1993). Indeed,

the best fitting model showed that Burnout was better as only exhaustion and

cynicism, with Work Engagement comprising work vigour, work dedication, work

absorption and professional efficacy. Closer examination of the other factorial

analyses of Burnout and Work Engagement showed similar results among Dutch

executives and employees (Schaufeli & Bakker, 2004; Schaufeli et al., 2008). It is

necessary to further explore the factorial structure of the burnout-work engagement

to establish whether one or two factors better describes the construct. The research to

date on engagement, alone and in relation to burnout, is not extensive and further

studies, including this thesis, are necessary to confirm these relationships.

1.2.4 Bringing together well-being, mental health, burnout and engagement

There is limited research that considers the similarities and differences of

well-being and mental health (as the absence of mental illness) (Keyes, 2002, 2005;

Ryan & Deci, 2001). As noted previously, work engagement as used in this thesis,

arose out of the research on burnout, so these constructs are reasonably understood as

opposite ends of a continuum that lies between high energy and identification

(engagement) and low energy and identification (burnout) for one‟s job. The

theoretical understanding of how the latent structures of well-being and mental

health/illness are related has been explored in the research of Keyes (2002, 2005).

27

Testing of the latent relationships found that well-being and mental health/illness

formed two separate but strongly related factors, as shown by better fit of the oblique

(related axes), rather than the orthogonal (unrelated axes) rotation of the factors in

the model (Keyes, 2005). Therefore, rather than the dichotomy of mentally healthy or

mentally ill, Keyes categorised individuals as flourishing, moderately mentally

healthy, languishing or having pure depression. Flourishing individuals have high

well-being (top third on well-being scales) and low mental illness; the moderately

mentally healthy fell in the middle third of the well-being scales; and the languishing

have low well-being (bottom third of well-being scales, yet without symptoms of

mental illness). Individuals could be further categorized as having a mental illness,

as pure depression, or in combination with low well-being, as depressed and

languishing (Keyes, 2002).

Within the stress literature, there is an acknowledgement that the stress

reaction is not necessarily negative, as shown by Selye‟s (1976) conception of

eustress and the Yerkes Dodson law (Le Fevre, Matheny, & Kolt, 2003). It is the

individual‟s appraisal or the stressor that makes it distressing or an exciting

challenge; as such, adaptive coping represents a positive response to stressors

(Folkman & Moskowitz, 2004). In addition, considering the likelihood and

consequences of future events allows an individual to be proactive about their goals

and well-being and applies coping to a future context, rather than being seen as on a

reactive skill (Aspinwall, 2005; Schwarzer & Taubert, 2002). As noted previously,

the process of a stressful situation links the event, the appraisal, the response and

outcome. Rather than seeing only negative response of distress and negative

outcomes on health and work performance, a balanced model of stress would include

positive reactions (for example, engagement and positive affect), known as „eustress‟

28

(Selye, 1976), so that the benefits of managing and overcoming difficult or

challenging situations can be acknowledged and build the individual‟s resources for

the future (Simmons & Nelson, 2001). Therefore, competent development that gives

the individual the skills and resources to manage and adapt to their environment,

respond in an appropriate and functional way to the situations and events that occur

in their lives will result in higher levels of well-being and mental health. Given the

prevalence of mental health problems (World Health Organization, 2001), it is

important to understand how well-being and mental health/illness, the person and

their environment interact and further, that interaction in the context of working

adults.

The developmental outcomes to be considered in the current thesis include

both positive and negative functioning, so that all the experiences of the individual

can be better understood. Acknowledging that life is neither all good nor all difficult

situations can bring together the relative balance that individuals find in their lives.

From the Personal Well-Being Index and other studies, this balance is often

positively skewed, with most people indicating that they are mostly happy. Happy

people have more positive self-reflection, less negative social comparison and

expressed less regret when their decisions do not turn as well as they expected

(Abbe, Tkack, & Lyubormirsky, 2003). They also respond and interpret negative

events with more positive strategies, reinforcing their beneficial affective responses

to the situations (Lyubormirsky & Tucker, 1998) and becoming better problem

solvers (Thoits, 1994). However, the consequences for individuals who have mental

health problems extend to their work and family relationships, leading to less

satisfaction in all domains. This thesis brings together the research outcomes often

used in the U.S.A., as well-being (life and job satisfaction and psychological well-

29

being) and mental health (the absence of depression, anxiety and stress) with

outcomes most often used in European research, burnout and work engagement. In

this way, understanding of the work-life interface can be expanded, as can the

relationships between these outcomes, which are less well researched. Keyes (Keyes,

2002, 2005) has analysed well-being and mental health, finding that these are related

by separate factors, whilst Schaufeli and colleagues (for example, Bakker et al.,

2006; Schaufeli et al., 2002) have explored burnout and work engagement, as

separate factors. As such, this thesis will bring together these outcomes in a way that

has not been reported previously in the literature.

1.3 Understanding the person, P, in the developmental equation

1.3.1 Generative dispositions and demand characteristics

The active participant, P, in the bioecological model is defined by

characteristics that assist and lead to the proximal processes occurring. These

characteristics are a generative disposition, physical and intellectual resources and

the individual‟s demand characteristics. The generative disposition is defined by the

individual‟s selective responsiveness to the social and physical environment, how the

individual engages and persists with complex tasks, and their belief systems such as

self-efficacy and locus of control that direct their behaviour, and which contribute to

the individual‟s ability to undertake more complex tasks over time. The individual‟s

physical and intellectual resources develop across the lifespan and can foster

development or place limits on how well an individual is equipped to deal with a fast

paced and rapidly changing world. Lastly, the demand characteristics show how the

individual relates to other people and whether the interpersonal relationships are

positive or negative (Bronfenbrenner & Morris, 1998, 2006). As noted previously,

dysfunctional relationship styles are found with depression and can lead to the

30

individual being rejected by their peers (Joiner & Metalsky, 1995). The current thesis

will define the active participant by first, their generative disposition and second, by

their demand characteristics.

1.3.2 Theories of the generative disposition of P, the person occupying and

managing multiple roles

Self-regulation is the basis of the how the generative disposition of the active

individual will be defined in this thesis, with the main driver of self-regulation taken

as dispositional optimism which is the generalized expectations for good future

outcomes (Carver & Scheier, 1998; Scheier, Carver, & Bridges, 1994). Rather than a

fixed response to all situations, adaptive self-regulation by competent individuals is

more flexible in the way that they approach the world, being both active and

selective about their environment. In a longitudinal study of children‟s temperament,

feedback from one‟s actions provided continuity of behavioural responses, with the

accumulation of consequences building the subsequent life path. Shy children

become shy adults, whilst angry children grew into angry adults (1989). The growing

and developing person represents a self-organizing system, which adapts and selects

the best available options across time that will allow desired goals to be achieved

(Csikszentmihalyi & Rathunde, 1998). As the future is unknown, being able to adapt

to uncertainties and make decisions without complete knowledge of all eventualities

can allow the individual be to happier with their choices and not regret their

decisions (Abbe et al., 2003).

Personality psychology has many ways to describe and analyse the

individual, all of which contribute to the understanding of how the individual‟s

characteristics can be stable and yet change over time (Carver & Scheier, 2000). This

thesis will primarily use the self-regulation view of personality (Carver & Scheier,

31

1998) to define the generative disposition, as self-regulation links with

Bronfenbrenner‟s conception of the active participant selectively responding to the

situations in their life and provides a way of capturing the dynamic nature of

navigating everyday life, between competing goals and roles. Whilst the Big Five has

been developed from a lexical approach to describe traits (John & Srivastava, 1999),

meta-analysis of the „happy personality‟ found that extraversion was too broad in its

conception to have a strong correlation to subjective well-being (DeNeve & Cooper,

1998). Similarly, whilst there is a relationship between the Big Five and Ryff‟s

construct of psychological well-being (Schmutte & Ryff, 1997), these are broad

groupings of relationships rather than specific points. For example, extraversion and

neuroticism are strongly predictive of the psychological well-being components, self-

acceptance and environmental mastery. However, the broad definitions of

extraversion and neuroticism do not allow specification of which particular facet of

the traits that is most relevant to achieving self-acceptance or environmental mastery

(Schmutte & Ryff, 1997). Ego-resiliency comes from the neoanalytic perspective and

focuses on how the individual adapts to external forces (Block & Kremen, 1996).

However, for the purposes of this thesis, the constructs are either too broad or too

narrow in their measurements to cover the actions of the active participant.

The self-regulation of behaviour proposes that an individual‟s behaviour is

guided by their goals, whether these are goals that being pursued or avoided. The

individual adjusts their behaviour based on feedback loops which assess their rate of

progress and the continued likelihood of success. The primary driver of this goal

directed behaviour is seen as dispositional optimism, which captures the individual‟s

beliefs and expectations about the future as positive and successful and goals as

achievable (Scheier & Carver, 1992). Self-regulation also fits with Bronfenbrenner‟s

32

conception of the active person, as the processes of goal directed behaviour are

similar to the actions of the generative disposition to be selectively responsive to the

environment and having attractive demand characteristics. Optimistic people have

been shown to persist longer at solvable puzzles, break off from unsolvable puzzles

more rapidly, are pleasant people to deal with and have better relationships with

other people (Armor & Taylor, 1998; Aspinwall & Brunhart, 2000).

In addition to the self-regulation model, the resources of the individual will

also include self-efficacy as the mastery component from Bandura‟s Social Cognitive

Theory, where behaviour is guided by the individual‟s expectancies and the

incentives that accrue when behaviours are performed (Bandura, 1986). Self-efficacy

is an important part of the expectancies of this model and emphasizes the

individual‟s agency toward desired outcomes (Bandura, 1997, 2001). The situation-

specific self-efficacy originally proposed by Bandura (1997) has been widened to

include coping self-efficacy (Chesney, Chambers, Taylor, Johnson, & Folkman,

2003) and general self-efficacy (Scholz, Gutierrez, Sud, & Schwarzer, 2002),

reflecting the usefulness of self-efficacy as a resource for the individual. Self-

efficacy has also been applied to understanding the adoption of health behaviours,

with this health behaviour model known as the Health Action Process model

(Schwarzer, 1992). Self-efficacy expectancies are used in combination with outcome

expectancies (similar to dispositional optimism) to influence the individual‟s

intention and action toward healthy behaviours. Individuals with greater self-efficacy

and outcome expectancies have an increased likelihood of maintaining and

implementing healthy behaviours (Schwarzer, 1992).

Self-regulation extends the health action process model as it takes the

outcome to a wider field, where the latter has health as the outcome, the former can

33

be applied to all life domains. It also acknowledges the dynamic and changeable

nature of life‟s goals. The general direction of life (to live a good life/to be successful

at work and at home) needs to be monitored with many sub-goals shifting in

importance depending on the season (in summer, allocate time to watering the

garden), career status (new job requires extra attention), and family stage (young

children need time). Each individual has their own set of circumstances and their

diverse individual goals as such will not be considered. Rather it is the underlying

processes of pursuing goals that will be the focus of this thesis as these will be

common across individuals.

Finally, the individual‟s gender will be considered through role theory (Kahn

et al., 1964), which is tied to the socialization of roles which in turn explain both role

salience and gender role attitude. How valuable a role is considered, that of a worker,

parent or within a marriage, along with a consideration of what roles are appropriate

for an individual are the result of both the individual‟s inclinations, learning

experiences and the prevailing cultural norms (Bussey & Bandura, 1999). The social

cognitive theory of gender development (Bussey & Bandura, 1999) combines the

perspectives of the influence of biological differences, how gender schemas are

developed (Bem, 1974; C. L. Martin & Halverson, 1981), gender identity as a result

of cognitive-developmental maturity (Kohlberg, 1966, cited by Bussey & Bandura,

1999) and the understanding of the differences that exist within genders as well as

between genders. Learning from observations leads to an understanding of valued

outcomes associated with gendered behaviours and with subsequent internalization

of the gender roles. Self and social sanctions follow to maintain valued behaviours

and avoid aversive behaviours, reinforcing stereotypes of what is normal for each

gender (Bussey & Bandura, 1999). The changes in employment patterns however

34

challenge these gender roles. For example, the attitudes to female employment are

vastly different in the current time compared to 30 or 40 years ago and there has been

a shift in the focus of who is likely to experience conflict between work and family

(or any non-work) roles. Initially, this was considered to be only a concern for

working mothers (Moen, 1992), whereas now it is recognised that both women and

men can be affected (Hill, 2005). The question that must be addressed here is

whether the social construction of gender influences the well-being and mental health

of working adults, regardless of the roles they fulfil or how gender is socialized.

1.3.3 Linkages between the generative disposition and positive affect, positive

psychology and resilience

By extending the ecological view of work-life issues (Grzywacz & Bass,

2003; Hill, 2005; Voydanoff, 2002) to include the personal characteristics of the

person who is balancing their work and non-work roles, Bronfenbrenner‟s

bioecological model is now being explicitly invoked (Bronfenbrenner & Morris,

1998). This addition is important to fully understanding how an individual negotiates

the various domains of their life and achieves good mental health and well-being.

Much of the research on the work-life interface does not include a detailed

account of how the individual differences are involved in managing the diverse roles

associated with the work-life interface (Frone, 2003). Yet where such research does

include individual differences, a broader understanding of the factors associated with

well-being is found. In a study of U.S. government workers, burnout in employees

has been shown to be negatively associated with job satisfaction, with burnout

predicted by increased organizational constraints toward customer relations and

lower self-esteem, self-efficacy, and emotional stability and an external locus of

control (Best, Stapleton, & Downey, 2005). The direct link between the individual‟s

35

positive disposition and their perceptions of lesser workplace constraints highlight

the importance of including individual differences in the study of work-life and well-

being. When problem-solving responses at work are explored, individuals who were

successful at reducing the stressors confronting them showed greater mastery and

self-esteem, with lower psychological distress than individuals who made

unsuccessful attempts or no effort to solve their problems (Thoits, 1994). These

findings highlight the importance of considering the individual as an active

participant within their lives, rather than a passive recipient of life stressors.

It is through the adaptive use of available resources that competent

development underpins the successful and positive outcomes that are achieved by the

developing individual (Bronfenbrenner & Morris, 1998; Yates & Masten, 2004). The

framework that guides the broad definition of optimal functioning is derived from

previous research, which includes principally the self-regulation of behaviour

(Carver & Scheier, 1998; Csikszentmihalyi & Rathunde, 1998) in conjunction with

models of action in health psychology (Bandura, 2005; Schwarzer, 1992; Schwarzer

& Taubert, 2002), the emerging study of positive psychology (Carlson, Kacmar,

Wayne, & Grzywacz, 2006; Carr, 2004; Emmons, 2003; Fredrickson & Joiner, 2002;

Grzywacz & Butler, 2005; Peterson & Chang, 2003; Seligman, 2002), and previous

resilience research (Kumpfer, 1999; Masten, 2001; Ryff, Singer, Love, & Essex,

1998). Rather than a fixed response to all situations, competent individuals are more

flexible in the way that they approach the world, being both active and selective.

Feedback from one‟s actions provides continuity of behavioural responses, with the

accumulation of consequences building the subsequent life path (Caspi et al., 1989).

The growing and developing person represents a self-organizing system, which

adapts and selects the best available options across time that will allow desired goals

36

to be achieved (Csikszentmihalyi & Rathunde, 1998). As the future is unknown,

being able to adapt to uncertainties and make decisions without complete knowledge

of all eventualities can allow the individual be to happier with their choices and not

regret their decisions (Abbe et al., 2003).

Self-regulation, the basis of dispositional optimism, focuses on feedback

loops, where the individual is motivated to take actions that reduce discrepancies

between their actual progress and their desired outcomes or goals. These feedback

loops can be are negative, discrepancy-reducing loops, whereas positive discrepancy-

increasing loops take the individual away from undesired outcomes (Carver &

Scheier, 1998). The ecological and life span view of human development involves

reciprocal or bidirectional relationships between the individual and their environment

(Bronfenbrenner & Evans, 2000; Csikszentmihalyi & Rathunde, 1998). This „loop‟ is

implicit in self-regulation, when individuals are motivated to move toward and focus

on desired goals and use their progress as the reference point in this process.

However, feedback loops can also be considered as a developmental or learning

response to contextual cues, rather than only as motivation toward action. As such,

positive feedback loops occur when behaviours that reinforce desirable behaviours

are maintained and strengthened, whilst negative feedback loops are likely to

dampen and inhibit behaviours that are detrimental to the individual (Lewis, 1995).

Whether regarded as motivation (negative loop, moving toward a goal) or as

reinforcement (positive loop, increasing desired action), feedback loops allow the

individual to achieve positive outcomes in their lives.

Successful problem solving at work leads to increases in mastery and self-

esteem in time (Thoits, 1994), whilst higher positive affect is linked to broad-minded

coping, followed by greater positive affect over time (Fredrickson & Joiner, 2002).

37

The latter effect is explained by the Broaden and Build theory of positive emotions

which contends that positive emotions, such as joy and interest, builds action

repertoires and enduring resources that are used in the future, which give rise to more

positive affect (Fredrickson, 1998). The Health Action Process model, which

includes the individual difference variables, self-efficacy and dispositional optimism,

has also increased the understanding of health behaviours in response to threats or

challenges to the individual‟s health (Schwarzer, 1992). In essence, the individual

will take actions that reduce the health threats to themselves, based on their sense of

competence to change their health outcomes, their expectations of having success at

changing their outcomes and their perceptions of the risks involved and the cost of

any such action would be (Schwarzer, 1992). In the model, expectations for change

and dispositional optimism achieve similar results, which Schwarzer acknowledges

and which links self-regulation and behaviours that lead to healthy outcomes.

Positive psychology involves the study of strengths and functional behaviour

rather than dysfunction and illness (Seligman & Csikszentmihalyi, 2000), which can

expand understanding of human development and the nature of well-being. As noted

previously, well-being in the work-life areas is often taken as the absence of negative

symptoms, such as depression and stress, rather than the presence of satisfaction and

happiness. Positive psychology can also offer new directions for interventions to

improve functioning. Strategies that increase gratitude and count blessings (Emmons

& McCullough, 2003) place a focus of developing strengths rather than overcoming

weaknesses (Hodges & Clifton, 2004) and personal strengths and practice acts of

kindness (Lyubormirsky, Sheldon, & Schkade, 2005) have been shown to improve

positive functioning and well-being. By including factors that lead to fulfilling goals,

engaging in challenging and stimulating work, and caring for others, well-being can

38

be understood in terms of flourishing and optimal functioning.

In addition, a resilience framework can set out the way that environmental

factors are filtered by personal responses (Kumpfer, 1999). Resilience is the process

and protective factors involved in good adjustment and development under adverse

conditions are more commonly used to understand child and youth development

(Masten & Reed, 2002). In later life, resilience has been defined as the „maintenance,

recovery, and improvement in mental and psychological health following challenge‟

(Ryff et al., 1998, p74). Based on the level of adversity or risk faced (low to high)

and the individual‟s level of competence or adaptation (competent or vulnerable),

when the level of risk or adversity increases from low to high, those with low levels

of competence move from being vulnerable to having maladaptive responses to

adversity. For those with high levels of competence, changes from low to high levels

of risk move these individuals from being competent and unchallenged to being

resilient to adversity (Masten & Reed, 2002). The perception of risk will also differ

between individuals.

The resilience framework proposed by Kumpfer (1999) has similarities to

Bronfenbrenner‟s bioecological model. The process of resilience involves the

stressor being framed by the environmental context (with the relevant risk and

protective factors), filtered by the person-environmental processes (perception,

reframing, and active coping) and interpreted by the individual‟s internal resources

(cognitive, emotional, behavioural, physical and spiritual) to produce the resiliency

strategies that lead to adaptation and positive outcomes (Kumpfer, 1999). By

specifying the components of the process, each can be explored as is the case with

the work-life interface. Of particular interest to the current research are the ways in

which the resilience process and the work-life interface overlap. The environmental

39

context contains risks and protective factors relevant to the individual and the

stressor, and relate to the demands and resources of the individual‟s work-life

situation. The internal self-resilience factors, cognitive, affective and behavioural can

be readily transferred to the active participant and the person-environment and

resilience strategies link to Bronfenbrenner‟s proximal process.

With regards to the individual differences that are amenable to personal

control, resilience is seen as „ordinary magic‟ as the use of these adaptive strategies

are available to all people, through nurturing relationships, safe and supportive

environments and personal resourcefulness (Masten, 2001). The importance of

positive cognitive styles to mental health is shown through Beck‟s models of

depression (Beck, 2002), whereas negative cognitive styles imply a vulnerability to

depression (Alloy et al., 1999). Optimism and pessimism are also implicated in the

self-regulation of goal-directed behaviour as optimistic individuals are more likely to

persist with solvable tasks whilst withdrawing from unsolvable tasks more readily.

Persistence toward goals has been shown to be dependent on the individual‟s

expectations of the likely outcome, such that optimistic individuals have more

positive expectations for the future (Armor & Taylor, 1998; Carver & Scheier, 2002).

In a study that examined effect of changing stressor levels on optimism over time,

increasing stressors among employee and spouse/mother roles reduced dispositional

optimism over time in some women. This finding highlights the complex and

reciprocal relationship between optimism and the individual‟s environment and

challenges the notion that optimism is a fixed trait (Atienza, Stephens, & Townsend,

2004).

1.3.4 Gender and the generative disposition of the active participant, P

The cornerstone of the bioecological model is the active participant. The

40

characteristics of the individual that lead to competence are the individual‟s gender

and behavioural disposition, their biopsychosocial resources and the reactions

elicited by their demand characteristics (Bronfenbrenner & Evans, 2000). An

individual‟s gender leads to particular developmental niches and pathways, due to

the historical context of their life and external societal norms and expectations

(Bianchi, Milkie, Sayer, & Robinson, 2000; Bronfenbrenner & Evans, 2000;

Bronfenbrenner & Morris, 1998). However, the individual‟s intrapersonal

characteristics allow the individual to construct their own life course. As noted

previously, the individual who has a generative disposition will use constructive and

persistent behaviours to engage with life, whereas a disruptive disposition will lack

control and has dysfunctional emotions and behaviours (Bronfenbrenner & Morris,

1998). In the current thesis, the influence of a resilient, adaptive and competent

personality on the interaction of working conditions and mental health will be

operationalised in the resources of a generative disposition in everyday life, as

dispositional optimism (Scheier et al., 1994), self-efficacy (Scholz et al., 2002) and

perceived control of time (Macan, Shahani, Dipboye, & Phillips, 1990). However,

before considering these individual difference variables, the fundamental difference

of the individual‟s gender will be considered.

1.3.5 Gender

Gender can proscribe the pathways of an individual‟s life and influences the

distribution of roles, as social role stereotypes can narrowly define relationships and

individual roles (Bianchi et al., 2000; Bussey & Bandura, 1999). Biological

stereotypes about gender can imply that one gender is more suited than the other to

certain roles, for example, that only women can be nurturing and only men can be

leaders. However, an extensive review of the many studies showed that rather than

41

polarity between the genders, there are more similarities than differences (Barnett &

Rivers, 2004). The biological differences between men and women, most obvious in

the ability to have children should not limit what roles either gender can occupy as

the differences within a gender on most abilities are greater than the differences

between genders for those abilities. Rather than stereotyping what is appropriate for

either gender based on gender alone, accepting the overlap between the genders

would pave the way for a greater equality in all areas of life (Barnett & Rivers,

2004).

The differences in how social roles are experienced by working adults will be

explored to understand whether these different experiences result in men and women

having different well-being and mental health outcomes. From the Australian Quality

of Life surveys, men were more vulnerable to low income, unemployment and lack

of partner reducing their personal well-being (Cummins, Woerner et al., 2007).

Research on the prevalence of depression found that women are more likely to be

depressed, as were individuals with less education, low income and poor health

(Andrews et al., 2001; Hawthorne et al., 2003). These poorer outcomes may be

associated with limited resources; for men, there is an inability to fulfil their social

role of breadwinner, whilst for individuals generally, and for women in particular,

the lack the opportunities to develop the resources that protect against affective

disorders. It would be expected therefore, that for individuals who are employed and

have sufficient income to meet their needs, gender would have limited influence of

well-being and mental health, despite the different experiences of working and

family conditions. Stereotypes of leaderships, occupations and breadwinners will be

explored before examining gender and parenting.

1.3.5.1 Gender and the work environment. When gender stereotypes are

42

applied to how men and women are considered in leadership roles of work teams,

studies using undergraduate students find that men are more favourably rated than

women as leaders as leadership emphasizes masculine traits. Men were expected to

be strong, whilst women had to be both strong and sensitive to be considered

effective and likeable leaders (S. K. Johnson, Murphy, Zewdie, & Reichard, 2008).

An early meta-analysis found gender differences only in laboratory situations, but

not in work settings, for leadership satisfaction, and no gender differences when

leader behaviour and employee satisfaction were rated (Dobbins & Platz, 1986).

Similarly, a series of meta-analyses on leadership effectiveness found that students

rated men as more effective, particularly for masculine (i.e. task orientated)

behaviours (Eagly, Makhijani, & Klonsky, 1992) but in work roles, both genders

were considered effective leaders (Eagly, Karau, & Makhijani, 1995). Interestingly,

this effectiveness was somewhat tempered by the level of leadership, with men

considered more effective in areas requiring technical skill (i.e. production

managers), whilst women were considered more effective where interpersonal skills

were more necessary (i.e. middle management) (Eagly et al., 1995).

When teams are based in working populations and task-related information

and knowledge of the competence of team members is established, it is the

individual‟s abilities, not their gender that is important to their leadership rating

(Powell & Graves, 2003). Organizational roles may be more salient to performance

and acceptance amongst the working population than simply considering

stereotypical gender roles, as the relationships are more established and not bound by

gender heuristics (Eagly & Johnson, 1990). When non-verbal behaviour is

considered in the interactions between high and low status employees within a

company, high status employees whether male or female, were positive and

43

supportive to the lower status employee, although this was expressed slightly

differently for either gender. Men used fewer interruptions and more facilitators (i.e.

responses such as „umms‟ that show interest and encouragement), whilst women

were open, confident and supportive (Hall & Friedman, 1999). Whilst gender

stereotypes can be a useful heuristic initially, the individual becomes more important

once they are known to their co-workers and the basis of leadership remains the

same, with different nuances.

Interestingly, when job satisfaction is compared between the genders, there is

a paradox as women are happier with their work than men as an overall rating and on

each facet of the work environment (A. E. Clark, 1997). Further, women valued their

relationships with their managers, the actual work they did and the hours they

worked, whereas men valued promotion, their pay and their job security as the most

important parts of their jobs. In this large sample of the British workers, women‟s

jobs were not objectively better than men‟s; rather women were more satisfied with

the same conditions, perhaps because their expectations of work were not as great.

The effect of higher education on job satisfaction supported this contention, as there

were no gender differences in job satisfaction among the better educated (A. E.

Clark, 1997). Different expectations and values about one‟s own business were also

apparent in recent research that compared the business satisfaction and success of

entrepreneurial university graduates in the US (Powell & Eddlestone, 2008). Firms

run by men were more successful, both in sales and performance than women and

men worked more hours per week than women, although both were equally satisfied

with their business successes. Women did not place value on business and sales

performance to judge the success of their businesses as men did although how

women did quantify their success was not explored by the study. Men however did

44

report increasing satisfaction with their business success as performance compared to

other similar firms improved, implying that external comparisons were important to

men. However, higher sales did not lead to satisfaction with business success and the

authors proposed that increasing sales may not have matched increased profits,

reducing perceptions of business success (Powell & Eddlestone, 2008). The central

part of the paradox appears to be differing values between the genders on what

constitutes „success‟, as it is possible that women don‟t need as much external

validation of themselves, whereas men may view their success through their

achievements in the breadwinner role (Burke & Nelson, 2001). This thesis will

explore how gender role attitudes may underlie this difference by including an

analysis of gender, job satisfaction and gender role attitudes.

For men who are employed in non-traditional occupations (for example,

nursing or primary school teaching), there was a tendency for a lack of acceptance

from their male friends for their choices, although their families may be largely

accepting of their choice (Simpson, 2005). However, the men could and did take

steps to reduce negative perceptions of their work by emphasising masculine aspects

(such as sporting roles for the primary teachers). Interestingly, the men who had

actively chosen their non-traditional work reported higher job satisfaction and greater

intrinsic reward from their work than when the men had been employed in more

„masculine‟ occupations (Simpson, 2005).

Women who work in male-dominated occupations face different challenges,

as they seek access to flexible work practices which allow them to pursue a career

and have family responsibilities. Rather than friends who do not support career

choices, it is the corporate ethos that is inimical to primary care responsibilities. For

example, in the large, corporate legal and accountancy firms, time spent maintaining

45

client relationships (usually after hours, at sporting events) and long working hours is

considered as „face time‟ and is considered crucial to career advancement (Thornton

& Bagust, 2007). If women are childless by choice, then they have less loneliness

and depression in later life than women who would rather have had children but did

not for whatever reason (Koropeckyj-Cox, 2002). Fairly or unfairly, parenthood in

women can be taken as disinterest in career development, leaving women in these

high status and very high income positions with the choice to postpone motherhood

or forgo it completely. Unless society considers success in more than monetary terms

and the firms change their view of the ideal employee as working very long hours to

the exclusion of all else, this area of gender inequality is likely to remain, until

perhaps the next generation does not accept the status quo (Thornton & Bagust,

2007). Ambition, however, is common in every generation and it is likely that a

small proportion of men and women will chose to work very long hours for the

income and status that these jobs bring and will accept the consequences of limited

opportunities for family responsibilities (Hewlett & Luce, 2006). For women then,

trading career success, a masculine role model, for becoming a mother, a feminine

role model, may be problematic for mental health in later life unless the individual

makes their own clear choice of their life path.

Among self-employed individuals, social roles gave a sense of purpose in

their work activities. In interviews with middle aged business owners, the men saw

their traditional breadwinner role as an important contribution to their family‟s

financial well-being. Being a good provider was important for both self-employed

men and women and how attached women were to their parental role influenced how

much work was changed to accommodate family needs. The women who strongly

identified with their parenting role changed their work schedules to look after their

46

children, whilst those who were more focused on their work role, had to make

arrangements that provided care for their children in their absence, such as reliable

babysitters or family to care for the children (Loscoco, 1997). When a couple has

flexible attitudes and behaviours toward gender-based roles, the results are mutually

beneficial and satisfying for both men and women (Barnett & Hyde, 2001; Loscoco,

1997; Milkie & Peltola, 1999). Although the roles that each gender is balancing can

be different, the resultant work-life balance is the same (Eagle, Miles, & Icenogle,

1997; Milkie & Peltola, 1999). The influence of demands and resources of the work-

life interface on both genders will be considered further in the sections on C, the

context of the individual‟s life.

1.3.5.2 Gender and parenting. In comparison with previous generations,

women have greater work opportunities and men have greater involvement with their

families (Bianchi et al., 2000; Burke & Nelson, 2001; Milkie & Peltola, 1999) and

share mutual appreciation of their employment needs (Eagle et al., 1997). The

presence of young children however can limit the priority of the work role for

women. When priority was given to family responsibilities, which are greater when

there are young children in the family, the mobility and career progression among

doctoral graduates was reduced, although women were more likely to chose jobs that

offered work-life balance then men. Having another parent to care for their children

allowed both genders to focus on their work roles (Kirchmeyer, 2006).

In a study of new mothers, most women felt that their role was as the primary

carer of the new child, whilst their partners provided a very valuable role of

providing for the family. This sharing of roles was for the most part not an explicit

discussion, rather something that they both agreed on, despite any financial strain

(Hand, 2006). Among mothers of children of different ages, most mothers recognised

47

that there was no one way to manage work and family roles and that different

families had different needs to meet. Finding a compromise between family and

work roles and therefore increasing the income of the household can be difficult

because there is no easy, neat solution that allows both roles to be fully expressed

(Hand & Hughes, 2004).

For employers, marriage and children highlight perceived differences

between men and women. Women, as mothers were expected to have more non-

work distractions and men, as fathers were expected to increase their efforts at work

to look after their families and this difference is reflected in an advantage in wages

for men across all types of jobs. In this way, women with family responsibilities do

not fit the „ideal worker‟ mould of the traditional breadwinner (Budig, 2002).

However, as noted previously, income over the level of meeting needs does not

guarantee well-being or mental health and may be problematic when the emphasis is

on materialistic goals.

When work performance is rated, conforming to gender stereotypes gained

more favourable ratings than when gendered expectations were ignored. Employed

individuals, when given vignettes of the performance of a supervisor, were more

critical of the planning and gave less rewards for males who took time off for family

matters (i.e. caring for a sick child) than for women, although both were rated lower

on planning evaluations when they had family to work conflict (Butler & Skattebo,

2004). Childcare and eldercare have additive effects on men and women‟s

satisfactions with work, although women with preschool children have the lowest

work-life balance and women with school aged children have the least satisfaction

with leave entitlements. Individuals of both genders with eldercare responsibilities

had less perceived organizational support, less work-life balance and less satisfaction

48

with their leave arrangements and their pay (Buffardi, Smith, O'Brien, & Erdwins,

1999). The detrimental effects of caring for elderly relatives may reflect a lack of

community awareness of what such care involves, whereas childcare needs are more

well-known and are more widely available.

1.3.5.3 Gender and house work. Sharing work and family roles within a

couple involve trade-offs for both men and women. Whilst women may do more of

the daily care of household and children than men (Bianchi et al., 2000; Liossis &

Noller, 2004), men work longer hours on average than women and more men than

women work very long hours (>50 hours) each week (Australian Bureau of Statistics,

2006a). How household tasks are divided remains a contentious issue, although

overall perceived fairness of household work and equity in the relationship tasks

reduces the impact upon the relationship (Coltrane, 2000).

The way that gender affects the combination of paid and family work can be

viewed in a number of ways. First, that women take on the ‟second shift‟ of family

work to accomplish both roles (Hochschild, 1997), although this carries the

implication that women have a far greater burden than men and men are not doing

their „fair share‟. Second, house work can be seen as an emotion-free transaction

between the partners, based on the power balance between them (i.e. their relative

resources, usually income), as a time differential (i.e. who works longer hours), or as

gendered social roles (e.g. that housework is „women‟s work) (Bianchi et al., 2000).

Whilst women did between two and three more hours per week than their husbands,

comparing these proposals using time diaries and large national samples, found that

time availability and relative resources change the proportions of time spent on

household tasks. As a woman‟s work hours and level of education increase and with

fewer children, she spent significantly less hours and her husband more hours doing

49

house work, which supports both the time availability and relative resource

viewpoints. Gender role attitudes have less effect however, than resources or time,

with egalitarian gender ideology not affecting men‟s hours at all and decreasing the

hours for women (Bianchi et al., 2000).

However, the third viewpoint considers that family work, of which household

labour is part is emotional labour and should be viewed as an expression of authentic

self-hood, rather than as just another chore to be done (Coltrane, 2000; R. J.

Erickson, 2005). In the study by Bianchi et al. (2000), the rational allocation of time

could not explain how the presence of children increased their mothers‟ non-work

hours. It is possible that the gendered identity associated with being a good mother or

father brings with it a set of behaviours that guides the enactment of roles to provide

for their children. Women then continue to invest more in family work to fulfil their

feminine and expressive identities (R. J. Erickson, 2005) and men would invest more

in the roles of breadwinner (Pleck & Stueve, 2001). Considering how fair the

partners feel their distribution of the family work, given the emotional meaning of

what each does may therefore be more important that simply adding up hours. In this

way, men‟s perception of themselves as breadwinners and the time that they spend at

work (for example, Loscoco, 1997; Maurer & Pleck, 2006) can be valued equally or

as fair recompense for the trade-off in working hours and additional family work

that women do.

For men and women who may have different roles, work different hours, and

do more or less household labour, the current research will explore whether the

perception of fairness of household labour, rather than hours per se, will lead to

gender differences in the well-being and mental health, and burnout and work

engagement of working adults. Gender will be further considered when the

50

differences in family roles are explored in the following section on Family

characteristics (Section 1.4.7). The next sections about the Person will discuss the

generative disposition (as dispositional optimism, self-efficacy and perceived sense

of control) and the individual‟s demand characteristics (as humour and social skills

and relationships).

1.3.6 Dispositional optimism

Whilst dispositional optimism is the expression of self-regulation, it is

important to consider why optimistic people have better outcomes. Dispositional

optimism is defined as the expectation of good outcomes for future goals (Carver &

Scheier, 2002; Scheier et al., 2002). The importance of these goals motivates self-

regulation, behaviour and the positive expectations of successful outcomes produce

coping patterns that ensure persistence until the goals are achieved (Aspinwall,

Richter, & Hoffman, 2002; Carver & Scheier, 2002; Diener et al., 1999).

Dispositional optimism for both genders is also associated with more active coping

strategies, better reconciliation with stressors, early recognition and disengagement

of intractable problems and focus on solvable situations (Aspinwall et al., 2002;

Iwanaga, Yokoyama, & Seiwa, 2004).

Dispositional optimism has been studied extensively in health-related

outcomes and less widely in work-related situations. In reviews of health-related

outcomes, optimism has been linked in many studies to faster recovery from breast

cancer and heart surgery (for example, see early reviews by Scheier & Carver, 1992;

Scheier et al., 1994). In a sample of women assessed after an abortion, optimism as a

part of a resilient personality (Marshall & Lang, 1990) reduced the women‟s stressful

appraisal of their situation, leading to less residual distress and greater well-being

(Major, Richards, Cooper, Cozzarelli, & Zubek, 1998). Women who were more

51

optimistic worried less about their perceived risk of breast cancer because they

believed that they had, in general, a lower risk of the disease (McGregor et al., 2004).

Menopausal women, who were more optimistic reduced their experience of

vasomotor symptoms (e.g. hot flushes), although not of somatic complaints (e.g.

headaches) after a fitness program for sedentary women (Elavsky & McAuley,

2009). After treatment for depression, patients with multiple sclerosis had increased

levels of benefit-finding about their illness, with decreases in depression mediated by

increased optimism and positive affect (Hart, Vella, & Mohr, 2008). Further, when

more optimistic individuals were involved in psychotherapy, they persisted longer

with therapy and their counsellors believed that they would show greater

improvement and used more task-orientated coping (Hatchett & Park, 2004).

Among doctors and nurses working with chronically and terminally ill

paediatric patients, greater optimism, as well as professional self-esteem (similar to

professional efficacy) was linked to the medical staff finding more meaning in their

working lives (Taubman-Ben-Ari & Weintroub, 2008). Further, among rural

adolescents, greater optimism was associated with a greater ability to control their

anger as well as the adolescents expressing less physical and verbal anger (Puskar,

Ren, Bernardo, Haley, & Stark, 2008). Greater dispositional optimism in Japanese

women is associated with more social support, less perceived stress and greater well-

being (Sumi, 1997). After the first year at college, more optimistic students had

larger friendship networks and had gained more social support from their peers on

campus. By using more positive reinterpretation and growth to cope with the

experiences, they also reported less stress and depression than their less optimistic

peers (Brissette, Scheier, & Carver, 2002). Similarly, optimism moderated the

relationship between undergraduates‟ perceived stress and their life satisfaction and

52

depressive symptoms (Chang, 1998). However, increasing stressors among employee

and spouse/mother roles can reduce dispositional optimism over time in some

women, highlighting the complex and reciprocal relationship between optimism and

the individual‟s environment (Atienza et al., 2004).

Research of optimism in the workplace is more limited. Including optimism,

as a personal resource, in the Job Demands-Resources model, showed that personal

resources partially mediated between job resources, such as autonomy, and work

engagement and reduced the impact of job demands, such as workload, that would

otherwise increase emotional exhaustion (Xanthopoulou, Bakker, Demerouti, &

Schaufeli, 2007). Optimism was also found to moderate the effects of time pressure

and organizational climate on mental distress over time, with women with high

optimism having less distress when time pressure was high and organizational

climate was poor. Among men in the same sample, being optimistic predicted less

exhaustion 12 months later (Makikangas & Kinnunen, 2003). In the workplace, as

with health and social relationships, dispositional optimism is a personal resource

that individuals can use to maintain and bolster their well-being and mental health.

Whilst not used in the current thesis, optimism and pessimism are also

explanatory styles, which reflect the attributions that an individual makes about the

causes of events in relation to themselves, the world and the future (Peterson, 1999).

Explanatory styles are pessimistic if a negative events are due to internal (self),

global (world) and stable (future) causes and are predictive of hopelessness and poor

functioning (Abramson, Metalsky, & Alloy, 1989; Needles & Abramson, 1990).

Among the participants of the Harvard study, pessimistic explanatory styles at age 25

years prospectively predicted poorer mental and physical health from age 45 to 60

years and greater mortality of the men (Peterson, Seligman, & Vaillant, 1988).

53

However, in the current thesis, the focus will be on dispositional optimism, rather

than explanatory style. The Attribution Styles Questionnaire (Peterson et al., 1982) is

a long instrument that measures explanatory styles and can be more cumbersome to

administer to participants than the short, more commonly and widely used Life

Orientation Test – Revised (Scheier et al., 1994).

Results from a study of Swedish twins raised together or apart found that

there is a moderate genetic component (23%) in the development of dispositional

optimism. The environment, therefore, in which children were raised was important,

as the shared family environment is a significant part of the optimism of the twins as

adults. More optimistic twins also reported less depression and paranoid hostility and

greater life satisfaction (Plomin et al., 1992). Authoritative parenting, by providing

experiences of mastery and understanding of life‟s rules and boundaries, fosters the

experiences that lead to effective self-regulation. The children of authoritative

parents as adolescents and college students were more optimistic which led to better

adjustment: greater self-esteem, less depression and better outcomes from their

studies at school and at university (Jackson, Pratt, & Pancer, 2005). Optimism was

seen as the way that the benefits of authoritative parenting could be translated into

healthy outcomes, giving the developing child the resources to manage their world.

How then does optimism operate? Dispositional optimism is important to the

current research as the link between positive expectations, persistence toward

favourable outcomes, and recognition of challenges allows working adults to be

proactive in managing their roles (Aspinwall & Taylor, 1997). When faced with

challenges of balancing work and family roles, an individual‟s behaviours toward

achieved the desired balance are firmly embedded in their expectations of the likely

outcomes (Armor & Taylor, 1998).

54

From the studies described above and extensive reviews of the effect of

dispositional optimism on coping with health problems and the effect on mental

health (Culver, Carver, & Scheier, 2003; Scheier et al., 2002), there are general

trends in the way in which optimists approach novel or challenging situations and

adaptively cope with their problems. Optimists are more likely to seek information

about the situation, be active in planning and coping, reframe their situation in a

more positive light, look for the benefits or „silver lining‟, use humour and finally

accept the reality of what has occurred to them (Scheier et al., 2002). This repertoire

of behaviours brings to mind the Serenity prayer (Aspinwall et al., 2002), in that an

optimist would try to change what can be changed, accept what can‟t be changed and

understand the difference between these situations.

Optimistic individuals believe that they are more likely to experience positive

events than negative events, with pessimistic individuals believing the opposite. Both

types of events were personal experiences with positive events such as getting a good

job offer, having good luck or being healthy and negative events such as

experiencing prejudice or crime, not getting a desired job or conflict with friends

(Lipkus, Martz, Panter, Drigotas, & Feaganes, 1992). However, optimism is not as

pronounced for future world events, such as possible future economic crises,

environmental problems than for personal events (Wenglert & Rosen, 2000). In this

way, optimistic expectations may be better expressed in areas where there is personal

experience rather than extending to broad and diffuse areas of where knowledge may

be limited. Realistic optimism is based on knowledge about oneself and about one‟s

environment and where actions are sensitive to environmental cues and responses

(Schneider, 2001). By accepting that there are fuzzy boundaries (and no absolutes) to

the accuracy of the knowledge about self and environment, realistic optimism can be

55

lenient in the positive reinterpretation of experiences whilst still seeking good

outcomes in the future (Schneider, 2001). Such strategic behaviour can protect self-

esteem and help overcome initial failures as the individual works toward later

success (T. Thompson & le Fevre, 1999). However, if the individual does not know

what competence involves, such as knowledge to pass exams or performance relative

to others, the individual is in effect unrealistically optimistic. Because of these

unknowns, the less competent can inflate their assessment of how well they would do

on the tasks under consideration (Erhlinger, Johnson, Banner, Dunning, & Kruger,

2008).

Over-optimistic estimates of performance can also be reduced when the task

is more realistic rather than hypothetical and the contexts are well-specified. In these

situations, individuals are reasonably accurate in their predictions (Armor & Sackett,

2006). Optimism is this way is not indiscriminate or unwarranted as predictions or

outcomes can be confirmed by events over time (Aspinwall et al., 2002). As such,

optimistic beliefs are bounded within reality, strategic in helping the individual to

achieve their desired goals and responsive to the unfolding situation (Armor &

Taylor, 1998; Aspinwall et al., 2002). There is the balance between persistence

toward goals and recognising when a goal is not achievable. The recognition of the

current situation, to find the „wisdom‟ in the serenity prayer to know the difference

between what can and cannot be changed, is crucial to flexible self-regulation.

Dispositional optimists disengaged from unsolvable tasks and spent more time on

alternative tasks where available, while persisting longer on solvable tasks they were

given. When there was no alternative task available, the dispositional optimists

disengaged faster from the unsolvable tasks than less optimistic study participants

(Aspinwall & Richter, 1999). Giving up can improve well-being and quality of life

56

where there is a meaningful alternative in which the individual can re-engage their

efforts (Wrosch, Scheier, Carver, & Schulz, 2003; Wrosch, Scheier, Miller, Schulz,

& Carver, 2003).

In conclusion, dispositional optimism is important to the current study of the

work-life interface because it provides a mechanism where by working adults can

manage the demands of their lives. By having positive expectations about their lives,

dispositionally optimistic individuals will be adaptable, flexible and responsive to

their situation, persisting in what they can do, retreating from the unsolvable

problems and taking steps to plan proactively increase their future resources to buffer

them in difficult or uncertain times. Recognition of possible futures allows the

individual to take action to secure the assistance of the people and materials to

manage these perceived future concerns or threats (Aspinwall, 2005; Aspinwall &

Taylor, 1997). By being pragmatic about the reality of the working-personal life

intersection, a focus on dispositional optimism will reflect the individual‟s self-

regulatory efforts. Such positive self-regulation will be important to optimal

functioning.

1.3.7 Self-efficacy, as coping self-efficacy

Models of health behaviours focus on the way in which individuals perceive

threats toward their health and what will motivate them into action. A number of

models are used in this field such as the Health Belief Model, the Theory of

Reasoned Behaviour and the Theory of Planned Behaviour, although these models

do not focus on individual difference in health actions. The model of relevance to the

current research is the Health Action Process Model in which self-efficacy is

considered central to perception of risk and prediction of action and self-efficacy

provides feedback to intention and action plans. When expectations of self-efficacy

57

and of likely outcomes increase, so do intentions to act on health action plans. Self-

efficacy and action expectancies, the equivalent to the general expectations of

dispositional optimism, increase the understanding of health actions (Schwarzer,

1992). In this way, actions and expectations can reduce risks of poor health and it

would be reasonable to extend this linkage to actions and expectations that could

reduce the risks from role demands. Similarly, optimism and self-mastery (conceived

as similar to self-efficacy) were found to be distinct constructs that, whilst they

overlapped, separately predicted the absence of depression in married professional

women (Marshall & Lang, 1990).

Self-efficacy indicates the confidence that men and women have in their

personal capabilities to achieve behavioural outcomes with high self-efficacy

equating to feelings of competence and effort and persistence toward goals

(DiBartolo, 2002; Ryff et al., 1998; Semmer, 2003). As noted previously, self-

efficacy has been understood in relation to health outcomes, with behavioural change

such as weight loss indicating how self-efficacy beliefs motivate the individual

toward adopting and maintaining behaviours that bring the desired outcome

(Schwarzer, 2001; Schwarzer & Renner, 2000). Self-efficacy is important to

successful transitions out of the military (Gowan, Craft, & Zimmerman, 2000) and

among teachers (Tang, Au, Schwarzer, & Schmitz, 2001), leading to reductions in

depressive symptoms in women professionals (Marshall & Lang, 1990) and

buffering of work-place stress and rigidity (Jex & Bliese, 1999; Jex, Bliese, Buzzell,

& Primeau, 2001; Jimmieson, 2000; Schaubroeck, Jones, & Xie, 2001). Parental

academic self-efficacy and aspiration are important for their children‟s own self-

efficacy and academic achievements (Bandura, Bardaranelli, Caprara, & Pastorelli,

1996).

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Although Bandura (1997) considered the construct as domain-specific,

general self-efficacy in men and women acknowledges the individual‟s broader sense

of competence across work and personal domains to handle unusual or difficult

situations (Scholz et al., 2002) in addition to balancing everyday life. Managing

difficult situations also relies on self-efficacy where one is capable and competent to

deal with whatever is required, whether coping with AIDS (Chesney et al., 2003) or

maintaining job satisfaction whilst working long hours and having a high workload

(Jex & Bliese, 1999). As part of a core self-evaluation, self-efficacy is also linked to

job and life satisfaction over and above the influence of job conditions (Judge,

Locke, Durham, & Kluger, 1998).

The role of self-efficacy in health behaviours and as a sense of competence

and outcomes of intended actions indicates that self-efficacy is an important

component of the adaptive and purposeful individual. Framing the measurement of

self-efficacy in the current research project through the lens of coping self-efficacy

reflects the need to manage and adapt everyday to the demands of work and personal

domains. This is a further link to the individual‟s demand characteristics, described

in Section 1.3.9. Daily life is a dynamic process of dealing with minor hassles,

maintaining routines and fulfilling role expectations. An individual that feels more

able to utilize their internal and external resources will maintain their well-being and

reduce any strain felt by not meeting those daily requirements.

1.3.8 Perceived control of time

Perceived control of time is the result of an individual‟s time management

behaviours, such as scheduling, setting goals and priorities and having a preference

for being organized, and the outcome of control is reduced tensions on the job and

increased job satisfaction (Macan, 1994) and is shown to be important in reducing

59

work-family conflict in full-time employees, who were also part-time students

(Adams & Jex, 1999). Locus of control was originally considered as a personality

trait and the way an individual viewed events, whether the cause of an event had an

internal cause, was due to powerful others, or a chance event. However, research has

shown that a sense of control should be considered within the context and

reinforcements of the situation (Fournier & Jeanrie, 2003).

Having control over one‟s job is central to the job demand-control model

(Karasek & Theorell, 1990), and the subsequent research has explored the effect of

decision latitude (Demerouti, Geurts, & Kompier, 2004), autonomy (Baard, Deci, &

Ryan, 2004) and control (S. C. Clark, 2002) in the workplace. Being able to control

the pace or one‟s work, how and when decisions are made and how skills are used

increases job satisfaction and reduces the incidences of burnout in employees

(Bakker et al., 2004; Grandey, Fisk, & Steiner, 2005; Karasek & Theorell, 1990;

Prottas & Thompson, 2006; Theorell, 2003).

A sense of control also links to the need for autonomy for general well-being

(Hahn & Oishi, 2006) and personal mastery (Lachman & Firth, 2004; Moen et al.,

2004). Dealing with stressors requires a sense of control over oneself and the

situation, with authority over decisions, the ability to use one‟s skills and the choice

of when to deploy those skills components of that process (Lachman & Firth, 2004;

Theorell, 2003). Having flexibility in when and where a role is enacted is part of the

resources that a role can generate, allowing individuals to feel that they can control

the timing of their activities (Greenhaus & Powell, 2006). In the study of self-

reported time management behaviours, setting goals and priorities, using the

mechanics of time management and a preference for organization were significantly

related to perceived control of time (Macan, 1994; Macan et al., 1990). In working

60

adults who were part time university students, increased perceived control of time

decreased the interference from work-to-home and from home-to-work and resulted

in better health and greater job satisfaction (Adams & Jex, 1999). Locus of control is

regarded as an important part of adaptive and competent life (Lachman & Firth,

2004), yet research on the perceived control of time is limited. Given the scarcity of

time reported anecdotally with regards to work-life balance, further research to

understand perceived control of time as part of a sense of control will be a useful

addition to the research literature.

In summary, the generative disposition can be seen through the self-

regulation that the active person interacts with their environment. The way in which

individuals pursue their goals, by using feedback loops to assess their progress

(Carver & Scheier, 1998) and using the positive affect generated by reaching their

goals to maintain their interest and reinforce their actions (Fredrickson, 1998) is

readily understood as a logical extension of the person themself. The dynamic nature

of self-regulation is implicit in the proximal processes driving development and the

role of the generative disposition in shaping those proximal processes. Dispositional

optimism, coping self-efficacy and control of time will capture the way that

individuals are working towards their goals, whether internal or externally motivated.

Succeeding at those goals will encourage persistence in the future toward goals that

are relevant at that time.

1.3.9 Theories of the demand characteristics of P, the person occupying and

managing multiple roles

The second component of Bronfenbrenner‟s active participant that will be

considered and measured in the current thesis is the individuals‟ demand

characteristics, as the ways in which the active participants cope, manage and

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interact with their environment. Demand characteristics will be considered as

humour (Abel, 2002; Lefcourt, Davidson, Prkachin, & Mills, 1997; R. A. Martin,

Puhlik-Doris, Larsen, Gray, & Weir, 2003) and social skills and support (Ferris,

Witt, & Hochwarter, 2001; van Ypern & Hagedoorn, 2003). These characteristics

have been shown to improve coping with life stressors through effective problem

solving and stronger interpersonal relationships and also are similar to the resilient

personality associated with personality traits (John & Srivastava, 1999), but more

specifically defined.

The behaviours associated with demand characteristics are viewed in the

current thesis as the ways in which individuals react and deal with other people in

their daily life. Adaptation and coping as ways to manage the increasing complexity

of life as one ages and matures and has more responsibility at home and at work.

Problem solving becomes an increasing complex activity as the individual gains

increasing experience with situations and relationships as they age. The adaptive

strategies that adults develop to cope and manage start in childhood and become

increasingly sophisticated in adulthood (Skinner, Edge, Altman, & Sherwood, 2003;

Skinner & Zimmer-Gembeck, 2007), with the ultimate aim of achieving a wise old

age (P.B. Baltes et al., 1998). Viewed in this way, coping with stressors is seen as

reacting to what has occurred (Folkman & Moskowitz, 2004), whilst forward

planning, looking for possible pitfalls and opportunities, and taking suitable

precautions is proactive coping (Aspinwall & Taylor, 1997; Schwarzer & Taubert,

2002). Coping is most often framed as the response to significant, catastrophic events

such as the loss of loved one, whilst everyday life is more of chronic stressors within

the home or workplace, which could be, within reason, thought of, foreseen and

planned for in some way. For example, it is difficult to prepare for a sudden accident

62

or loss, but easier to prepare for the daily routine and work commitments by

marshalling resources and planning to facilitate the smooth running of these routines

and commitments. By foreseeing likely or possible difficulties and challenges and

making appropriate plans, proactive coping brings to mind the adage, „a stitch in time

saves nine‟ (Aspinwall & Taylor, 1997).

For the purposes of the current thesis, the ability to cope and manage

interactions will be taken as humour and social skills. The first component of the

demand characteristics, humour is seen as a multi-dimensional construct of related

traits, such as making and appreciating jokes, laughing easily, being cheerful and

looking at the world positively, and lastly, using humour in response to stressful

situations (R. A. Martin et al., 2003). In the current thesis, the use of humour will be

limited to its use as a coping strategy.

Freud saw humour and joking as the ego‟s defense against distressing events,

replacing unpleasant affect with pleasant affect (Freud, 1995). These unconscious

ego defenses were considered rigid, under little volitional control and resulted in

anxiety when defensive behaviours were blocked, whereas coping strategies were

consciously used and more flexible in solving problems (Plutchnik, 1995). Vaillant

(2002) concluded that defense mechanisms could be graded maladaptive/immature or

adaptive/mature with the adaptive defense mechanisms being humour, altruism,

suppression, sublimation and anticipation. Mature defenses describe how individuals

„turn lemons into lemonade and not turn molehills into mountains‟ (p206). In the

context of the mature defenses, humour allows the individual to confront and face

uncomfortable situations in ways that make the situation less painful and easier for

the individual and others to deal with (Vaillant, 2000). Measuring defense

mechanisms is proposed for future editions of the Diagnostic and Statistical Manual

63

of Mental Disorders, to be added to Axis V to expand the information available on

client functioning (American Psychiatric Association, 2009).

Using humour to manage stress appears to rest with the cognitive shift in

perspective, which leads to a shift in affect (R. A. Martin & Lefcourt, 1983). Of

course, humour in response to a stressful situation is more acceptable than anger or

violence toward the stressor (person or object) (Lefcourt, 2002a)! Humour can also

act as an interpersonal tool, making the individual more pleasant company through

their jokes or humourous views, although humour can be used in a derogatory or

self-deprecating way that is hostile to others (Lefcourt, 2002b; R. A. Martin et al.,

2003). However, the focus in the current thesis will remain with humour as a coping

strategy, rather than as an interpersonal style. By recognising that humour is highly

adaptive, a pathology-based view of human functioning can be broadened to include

human strengths, ensuring that the value of humour will be more widely recognised.

The second component of the demand characteristics is the importance of the

relationships between the individual and those around them and the social support

and well-being that these relationships bring (Reis, Collins, & Berscheid, 2000).

Social support can be considered as instrumental (as in material aid), informational

(as in relevant solutions) or emotional (S. Cohen, 2004). As noted for depression,

poor interpersonal styles and social skills contribute to depressive symptoms whilst

social support is well known to buffer the individual from the effects of stressful

situations (S. Cohen & Wills, 1985). Interestingly, along with the social support that

derives from close family and friends, giving social support to those around one is

beneficial for the individual‟s well-being (Brown, Nesse, Vinokur, & Smith, 2003).

Social integration with people outside the strong ties of one‟s close relationships is

also important and these weak ties are important for social cohesion and a sense of a

64

civil society (Granovetter, 1973). The loss of weak ties fragments the community, as

shown in former communist East Germany where distrust of causal interactions for

fear of political betrayal undermined the casual goodwill and reduced social

integration under communism (Volker & Flap, 2001). A sense of trust within the

information that these weak ties can bring enables transfer of useful knowledge

within a business setting (Levin & Cross, 2004) whilst improving the likelihood of

finding jobs for women (Crowell, 2004) and finding gigs for rock and roll bands

(Reed, Heppard, & Corbett, 2004).

In summary, the active individual‟s demand characteristics are the processes

that underpin the actions and adaptations that an adult makes in order for their life to

function in a meaningful and purposeful way. Using the self-regulation model as a

basis for study allows for the inclusion of interpersonal actions, as the humour which

the individual uses and the social skills with which they deal with other people. More

positive interpersonal skills are expected to increase the well-being and mental health

of individuals, adding to the benefits of the active participant, outlined in the

previous section.

1.3.10 Humour

Including humour as a method that individual can bolster their personal

resources allows the many ways in which humour can aid positive mental

functioning to be explored. For example, humour is involved in stress relief (Abel,

2002; Seaward, 2004), in improving learning outcomes (Tamblyn, 2003), in

increasing positive affect (Larsen & Prizmic, 2004) and buffering the effect of

rumination in dysphoric individuals (Olsen, Hugelshofer, Kwon, & Reff, 2005).

Humour may not seem a „serious‟ component of psychological functioning but it

permeates everyday life. It is so commonplace in social interactions and managing

65

distress that humour appears to be almost invisible as a factor that should be included

in understanding how individuals successfully manage their work and family lives.

From Freud to current research, adaptive humour has been shown to smooth

interpersonal relations and buffer negative emotions.

After considering the growth and maturity of the participants of the Study of

Adult Development, Vaillant (2002) concluded that defense mechanisms could be

graded maladaptive/immature or adaptive/mature, with the adaptive defense

mechanisms being humour, altruism, suppression, sublimation and anticipation.

Mature defenses describe how individuals „turn lemons into lemonade and not turn

molehills into mountains‟ (p206). In the context of the mature defenses, humour

allows the individual to confront and face uncomfortable situations in ways that

make the situation less painful and easier for the individual and others to deal with

(Vaillant, 2000). For the participants of Vaillant‟s study, having mature defenses at

age 50 was the second strongest predictor (after non-smoking or early cessation of

smoking) of psychosocial health and being „Happy-Well‟ (literally happy and well)

at age 75-80. Those individuals who were classed as „Sad-Sick‟ (literally

sad/unhappy and sick /unwell) used very few mature defenses and had poorer

outcomes as a result (Vaillant, 2002). Similarly, among employees of organizations

undergoing major changes, those with higher levels of humour (as an adaptive

defense) were less likely to resist the changes that were occurring and have better

mental health (Bovey & Hede, 2001).

It is interesting to note that the description of humour as a defense mechanism

(American Psychiatric Association, 2000) are similar to the wording used in the in

the Coping Humour Scale (CHS, R. A. Martin & Lefcourt, 1983) and to the

affiliative, self-enhancing and aggressive styles of the Humor Styles Questionnaire

66

(HSQ, R. A. Martin et al., 2003). Among adolescents, there were significant positive

correlations between humour as a defense and affiliative and self-enhancing styles

and significant negative correlations with the aggressive style (S. J. Erickson &

Feldstein, 2007). Defense mechanisms are proposed for future editions of the

Diagnostic and Statistical Manual of Mental Disorders, to be added to Axis V to

expand the information available on client functioning (American Psychiatric

Association, 2009). By recognising that humour is highly adaptive, a pathology-

based view of human functioning can be broadened to include human strengths,

ensuring that the value of humour will be more widely recognised.

Following on from the psychodynamic viewpoint, humour can then be

considered as part of Bronfenbrenner‟s conception of the developing person‟s

demand characteristic. As such, humour influences the way that individuals interact

with the world around them and manage the situations they face. Humour represents

cognitive-affective reappraisals that makes situations less threatening and is therefore

important to stress relief (Abel, 2002), maintaining relationships and resolving

conflict (D. W. Johnson, 2003), reducing negative affect and increasing positive

affect (Larsen & Prizmic, 2004), resilience (Kumpfer, 1999) and enjoyment in life

(R. A. Martin, 2001).

Recent research on the interpersonal use of humour has highlighted the

adaptive and maladaptive aspects of humour as the individual uses humour upon

themselves or others which has greater predictive power and usefulness to research

(R. A. Martin et al., 2003), overcoming conflicting and ambiguous results about

humour‟s role in health and well-being in research (R. A. Martin, 2001). The

Humour Styles Questionnaire details the interpersonal use of humour. Adaptive

humour directed towards self is self-enhancing and is a humorous view of life and

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adaptive humour directed toward others is affiliative, fostering relationships,

reducing tension and providing amusement. Maladaptive humour directed toward

others, however does not provide amusement as it is expressed as sarcasm and

teasing and as humour that is intended to hurt and ridicule. When maladaptive

humour is used toward oneself, the humour is self-defeating and involves excessive

self-criticism to make others laugh and is linked to depression, anxiety, and hostility

(R. A. Martin et al., 2003). The distinction between adaptive and maladaptive can be

fine, however with care needed to avoid disrespectful humour taking hold within

relationships, changing affiliative humour into aggressive humour. In the workplace,

maintaining this distinction maintains team cohesion, promoting creativity and

problem solving, without causing offence or reducing managerial effectiveness

(Lyttle, 2007).

Humour, measured as sense of humour, moderated the perception of stress

among college students (Abel, 2002). Whilst women reported they had more

problems than men, there was no interaction between gender and humour on

perception of stress. Where there were few problems in the students‟ daily lives,

students with a better sense of humour perceived that they had less stress than did

those with a lesser sense of humour. The high humour group also reported using

more planning and problem solving to overcome their problems. These results

indicate that, despite having the same number of problems, the high sense of humour

group perceived that they were significantly less stressed than the students with a

lower sense of humour. Similarly, students with a stronger sense of humour used

positive action toward problems in everyday life, which is likely to have lead to the

perception that they have less stress in their life (Abel, 2002).

A study of the interaction between gender, humour (as the Coping Humour

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Scale) and blood pressure following stressful tasks (e.g. a Favourable Impressions

task) found that men and women had different patterns of responses (Lefcourt et al.,

1997). Women with greater humour had lower blood pressure across all tasks, whilst

among men greater humour was associated with higher blood pressure. The authors

proposed that the results reflected gender differences in the way that humour was

expressed; women used humour as a coping strategy to laugh at their efforts in

attempting the difficult tasks, whereas men were possibly more competitive and

joking, as a way to use humour was less appropriate or useful to moderate their stress

responses (Lefcourt et al., 1997).

Similar results were found among students solving puzzles, where women

with greater trait humour in the high stress condition had lower anxiety and greater

positive affect, whereas the men did not have the same relationship (Abel &

Maxwell, 2002). Using humour in social situations improved the enjoyment and

confidence that students had in their social interactions, as well as the length of

interaction with their peers, particularly when students had low levels of depression

(Nezlek & Derks, 2001). Humour has roles in group solidarity and courtship

(Weisfeld, 1993) with greater humour is associated with less depression, anxiety and

negative affect (Kuiper, Grimshaw, Leite, & Kirsh, 2004; Thorson, Powell,

Sarmany-Schuller, & Hampes, 1997) and greater levels of global and social self-

esteem and more positive affect (Kuiper et al., 2004). The benefits of humour can be

increased by deliberately increasing one‟s enjoyment from watching an amusing

film. Female students reported greater enjoyment and laughed and smiled more when

instructed to see the films in the funniest possible way and physiological responses

were also increased (Giuliani, McRae, & Gross, 2008). The results are consistent

with the use of humour to increase positive affect (Larsen & Prizmic, 2004) and

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indicate that deliberate positive cognitive reappraisals offer a way to manage difficult

situations (Giuliani et al., 2008).

In considering which of the measures of humour to use for the current thesis,

two measures were considered, the Coping Humour Scale (CHS, R. A. Martin &

Lefcourt, 1983) and the Humour Styles Questionnaire (HSQ, R. A. Martin et al.,

2003). There were strong relationships between the Coping Humour Scale and

positively, the affiliative and self-enhancing scales and negatively the aggressive

scales but not with the self-defeating scale (R. A. Martin et al., 2003). The CHS and

the HSQ also overlap in their associations with mental health and well-being. The

CHS was chosen as it was shorter (7 items) and gave similar outcomes to the much

longer HSQ (32 items). As the focus of this thesis on adults managing their everyday

lives, measuring humour as coping, rather than interpersonal use, becomes more

appropriate in this case. As adaptive humour is ubiquitous to positive psychological

outcomes, the current research will investigate the extent and nature of the influence

of humour as a coping mechanism on well-being and the work-life interface.

1.3.11 Social skills and relationships

Relationships give rich meaning of life as the need to belong is a fundamental

human motivation. Satisfying, long term relationships provide positive experiences

and emotions, which protect against mental distress (Baumeister & Leary, 1995),

depression (Lin, Ye, & Ensel, 1999) and ill health (S. Cohen, 2004) and underpin

happiness and life satisfaction across cultures (Haller & Hadler, 2006). Social

support can operate directly, through membership of social networks and indirectly,

through the buffering of stress by providing functional support to deal with stressors

(S. Cohen & Wills, 1985). In this way, there are interconnections between perceived

social support, supportive relationships and supportive networks that benefit the

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individual in the way that they cope with threats or challenges in their lives (Pierce,

Sarason, & Sarason, 1996). For low income families in the US, greater social support

reduced their perception and actual experience of economic hardship and reduced the

need to seek help from outside means, such as pawning their goods. This support

allowed these individuals and families to manage their everyday needs without

falling into further hardship (Henley, Danziger, & Offer, 2005).

As an extension of this conception of social support, the roles that an

individual occupies then provide social capital which is another resource that the

individual can draw upon in challenging times (Greenhaus & Powell, 2006; Hobfoll,

2002), with more social roles also protecting the individual from ill health (S. Cohen,

2004). Because social relationships involve more than just the individual,

relationships act as powerful drivers for development. Social relationships change

over time and are dependent of life stage and age (Moen, 2003; Reis et al., 2000).

For example, parent-child relationships, marriage (or long-term commitments), long

term friendships and working relationships can all promote close personal bonds and

relationships that will lead to greater happiness and more competent development

over time (Reis et al., 2000).

Of interest to the current research is the manner in which individuals interact

with the people around them to explore the effects of the active participant‟s demand

characteristics. Interpersonal skills are implicated in the development and

maintenance of depression. Excessive reassurance seeking in mildly dysphoric

individuals create negative interpersonal situations with others which can intensify

depressive symptoms (Joiner & Metalsky, 2001), whilst young college men with

dysphoria and poorer social skills were more likely to be rejected by their room

mates (Joiner & Metalsky, 1995). The room mates of depressed students were also

71

more likely to become depressed themselves, indicating that poor interpersonal skills

can make depression contagious (Joiner, 1994), while students who were highly

dependent on others were more likely to become more depressed when they had

more interpersonal stressors (Shahar, Joiner, Zuroff, & Blatt, 2004). In the opposite

way, the individual who has better social skills can improve their job performance in

situations where organization support is limited (Hochwarter, Witt, Treadway, &

Ferris, 2006).

The availability of social support from one‟s partner and family has been

shown to reduce work-family conflict and moderate the effect of parental overload

among Indian employees (Aryee, Luk, Leung, & Lo, 1999). Among employees

working in remote Australian mining communities, isolation from family reduced job

and family satisfaction whilst community involvement and kinship support benefited

job and life satisfaction (Iverson & Macguire, 2000). Seeking social support helped

police officers reduce the psychological distress felt from their work, as did problem-

focused coping (G. T. Patterson, 2003). In the workplace, informal support from

managers and supervisors is critical to the implementation of family-friendly work

policies, as without the daily compliance and belief in these policies, conflict

between roles is almost inevitable (Behson, 2002, 2005; Dikkers, Geurts, den Dulk,

Peper, & Kompier, 2004; C. A. Thompson, Beauvais, & Lyness, 1999). In a daily

diary study, the most frequent cause of distress in the workplace was interpersonal

conflict, accounting for three quarters of the reported negative incidents, whilst

workload accounted for only a small proportion reported (Schwartz & Stone, 1993).

Similarly, the support from spouse, children, family and kin are keys to the resources

of the home domain, whilst a safe neighbourhood and supportive friends are

resources of the community (Voydanoff, 2005a). Interestingly, longitudinal research

72

found that giving instrumental support to friends, relatives and neighbours and

emotional support to one‟s spouse associated with reduced mortality among older

adults (Brown et al., 2003). Therefore, it is possible that it is the act of helping that

brings the benefits of being actively engaged and connected with people.

Feeling connected with other people, either at home, at work or in the larger

community, has the potential to be a protective factor to promote resilient

development (Donald & Dower, 2002), and is based on the how a person rates

themselves in relation to others, as a sense of belonging and assurance (Lee &

Robbins, 1995). It is not only the direct and strong relationships that provide social

support but the looser, weak ties of casual relationships within the community that

can assist the individual (Granovetter, 1973). As noted in the previous paragraphs,

poorer interpersonal skills limit interpersonal relationships and therefore the

opportunities for weak ties to operate and increase available opportunities. For

example among rock musicians, emerging artists used social networks to finance

their early efforts, gather fans and find gigs (Reed et al., 2004). Having trust in the

competence of these casual networks allows useful knowledge to be disseminated

throughout companies (Levin & Cross, 2004), whilst the absence of trust, as shown

in former Communist countries, diminished social integration as weak ties were a

liability rather than an asset (Volker & Flap, 2001). Weak ties are an asset because

acquaintances move in different social groups and therefore have access to different

information than one‟s close friends, who are in the same social group. In this way,

acquaintances can become better and varied sources of job information (Krauth,

2004) as well as acting as conduits between different groups (Kavanaugh, Reece,

Carroll, & Rosson, 2005). Increasing the number of women in the working

population also increased their opportunities to access the information and

73

opportunities provided by weak ties (Crowell, 2004).

In the current research, interpersonal processes will be measured both as

social skills and social support. Social skills will be measured to capture the ability of

the individual to deal effectively with other people. By including the individual‟s

social skill in the study of the work-life interface, the component of the individual‟s

demand characteristics can be included to fully understand the individual‟s life. The

social support available from work colleagues and the support of managers for work-

life initiative will also be measured and will be discussed in the following section on

the context of the work-life interface.

1.3.12 Conclusion for P, the Person

Optimal functioning relies on the strategies that the active individual uses to

organise their life as defined by Bronfenbrenner (Bronfenbrenner & Morris, 1998,

2006) and indicated in the resilience framework (Kumpfer, 1999). In the current

thesis, self-regulation (Carver & Scheier, 1998; Scheier et al., 1994) is considered the

predominant way in which the individual will enact their lives. As the work-life

interface requires the individual to fulfil competing demands, problem solving,

emotional and avoidant styles of coping can be both helpful and dysfunctional for the

individual, depending on the context and the individual‟s appraisal (Folkman &

Moskowitz, 2004). As a way of action regulation, the choice of strategy relies on the

nature of the stressor (challenge or threat), to whom the stressor related (self or other)

and which of three needs, autonomy, relatedness and competence is being stressed

(Skinner et al., 2003). Optimism increases the attention that the individual pays to

challenges that are relevant to them and increases the use of proactive behaviours to

manage the challenges (Armor & Taylor, 1998; Aspinwall & Brunhart, 2000). The

needs that these coping strategies seek to maintain are essential for personal growth

74

and happiness and are shared across cultures (Baard et al., 2004; Hahn & Oishi,

2006). As noted previously, competence is the need to successfully meet the

challenges of life and have positive outcomes. Autonomy is the ability to choose

one‟s action and make one‟s own decisions and relatedness is the desire for

reciprocal and meaningful relationships (Baard et al., 2004; Ryan & Deci, 2000). It is

expected that in the multiple regression analyses of Study 1, that the characteristics

of a generative disposition and positive demand characteristics will be predictive of

greater well-being and work engagement, better mental health and less burnout.

The longitudinal nature of the current research project, explored in Study 2,

will be able to examine whether there is any causal link between individual

differences, the work-life interface, and well-being and mental health, and test the

relationships between engagement and burnout. In the work-life interface, both loss

and gain spirals of resources have been found, with exhaustion and work-home

interference having reciprocal, reinforcing relationships over time (Demerouti,

Bakker, & Bulters, 2004) whilst engagement at work increased professional efficacy

which in turn fostered perceptions of greater work resources (Llorens et al., 2007).

Whilst resilience in childhood and later life are well understood, resilience in adults,

in the context of their working lives is more often framed as coping with work stress.

The link between resilience and competence can be understood as the well adapted

child‟s functioning under high to low adversity, respectively (Masten & Reed, 2002).

As such, resilience could be seen as the actions of competent individuals when they

are challenged. The development of competence and optimal functioning is relatively

unexplored in adulthood, where work and life conditions are more likely to be

chronically challenging, rather than traumatic (Beasley, Thompson, & Davidson,

2003; Grzywacz, 2000). In this way, the current research will add to the literature by

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bringing the individual, as defined by their gender and generative disposition

(measured by their dispositional optimism, self-efficacy and sense of control) and

their demand characteristics (measured as their humour and social skills) into

understanding the well-being, mental health, burnout and work engagement of

working adults.

1.4 Understanding C, the Context for multiple roles

1.4.1 Theories and models of C, the Context for multiple roles

In the discussion of the bioecological theory, the elements of the context were

described by the roles, activities and relationships that the individual engaged in

within each setting (Bronfenbrenner, 1979). The theoretical underpinning of C, the

context, then will be taken as those theories that surround these roles, activities and

relationships with the understanding that each of these are also influenced at the level

of the microsystem, mesosystem or macrosystem. Roles, for example, can be

explored through Role Theory which can have several outcomes. The first outcome

is the salience of occupational or parental roles (microsystems), the second as

spillover between roles (mesosystem) and last as socialization of gender role

attitudes that arise from the cultural expectations of the macrosystem. As noted in the

discussion on the individual‟s demand characteristics, relationships can be explained

through social support and family systems. The activities within and between

domains can be explored through job conditions, family structure and recreational

activities. The theories about the work-life interface reflect the first and last

components; with much of the U.S.-led research based on the consequences of

occupational roles whilst the European research focuses on the effects of the

occupational activities such as the demands and resources available through a job.

The importance of relationships in the workplace is acknowledged in the European

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research, with the expansion of the Demand-Control model (Karasek & Theorell,

1990) to become the Demand-Control-Support (DCS) model (Van Der Doef &

Maes, 1999) to include the beneficial influence of the social support received from

managers and co-workers.

There are a number of models and theories around the work-life interface, of

which role theory (Greenhaus & Beutell, 1985; Kahn et al., 1964), spillover (Frone,

Russell, & Cooper, 1992a), ecological systems theory (Grzywacz and Marks, 2000),

the demand-resource models (Bakker & Geurts, 2004; Demerouti, Geurts et al.,

2004; Van Der Doef & Maes, 1999), and the work-life interface model (Voydanoff,

2002, 2005b) will be considered. Role theory has given rise to spillover, ecological

and work-life interface models and all these models of work and non-work settings

have common features of demands and resources, but emphasize different aspects of

role involvement and outcome. The following discussion will examine the divergent

views on the work-life interface, although these strands are becoming more similar

today.

Role theory (Goode, 1960; Kahn et al., 1964) has formed the basis for much

of the U.S. led research on the work-life interface with the expectations and activities

associated with occupational roles long being considered important to an individual‟s

well-being and mental health (Bronfenbrenner, 1979; Katz & Kahn, 1978).

Considerable research about work-life issues has followed from Greenhaus and

Beutell‟s (1985) definition of work-life conflict, as „a form of inter-role conflict in

which the role pressures from the work and family domains are mutually

incompatible in some respect‟ (p77). Inter-role conflict was seen as being time-

based, strain-based and behaviour-based, with sources of conflict coming from both

work and family roles and defined by working conditions and/or family structure.

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Specifically, that the time devoted to one role prevented participation in another role,

that the strain (measured as anxiety, fatigue or depression) resulting from one role

made it difficult to fulfil other roles, and last and least common, that the behaviour

required by one role was incompatible with the behaviour needed to fulfil another

role (Greenhaus & Beutell, 1985). There is an extensive and exhaustive research

literature on the sources of work-life conflict and decreased well-being that follows

many years of investigation (Bruck, Allen, & Spector, 2002; Butler, Grzywacz, Bass,

& Linney, 2005; Byron, 2005; Carlson, Kacmar, & Williams, 2000; W.J. Casper,

Martin, Buffardi, & Erdwins, 2002; Cinamon & Rich, 2005; Eby, Casper,

Lockwood, Bordeaux, & Brinley, 2005; Kinnunen, Vermulst, Gerris, & Makikangas,

2003; Kossek & Ozeki, 1998).

However, focusing only on conflict can obscure the beneficial aspects of

combining occupational and personal roles. Expanding role involvement and

commitment to enjoyable and meaningful activities, and through social relationships,

can increase an individual‟s time and energy (Marks, 1977). Role balance occurs

then when role quality and enjoyment meet and individuals experience more ease

and less strain in performing their roles (Marks & MacDermid, 1996). For example,

when the negative and positive sides of roles were considered together in a national

survey, the most positive outcomes such as better mental health and reduced alcohol

dependence were associated with lower work-family conflict and greater work-

family facilitation (Grzywacz & Bass, 2003).

Spillover between work and family lives combines both conflict and balance

views. Research by Frone and colleagues has established an integrated four factor

model of spillover that formed work-family balance, with the direction of influence,

either work-to-home or home-to-work, and the quality of influence, either conflict or

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facilitation, explaining the work-life interface (Frone, 2003; Frone, Yardley, &

Markel, 1997). This typology can be known by other names, for example, work-

family conflict described as negative work-family spillover and family-work

facilitation described as positive family-work spillover. The relevant designation has

the quality of the interaction, positive or negative, followed by the direction of the

spillover, either work-to-family or family-to-work (Grzywacz & Marks, 2000b;

Kinnunen, Feldt, Geurts, & Pulkkinen, 2006). As with the predictors of conflict,

facilitation of work is associated with work-domain factors, such as supervisor

support, and facilitation in the personal domain is associated with personal-domain

factors, such as spousal support (Frone, 2003; Grzywacz & Butler, 2005; Grzywacz

& Marks, 2000b).

The ecological system approach of Grzywacz and colleagues uses

Bronfenbrenner (1979) to address contextual factors of work-life and successfully

replicates the four-fold taxonomy of Frone and colleagues (Frone et al., 1997). The

person factors however are less well explored as the person is narrowly defined by

gender, neuroticism, and extroversion. Resources in both domains, such as decision

latitude or spouse support, reduced negative spillover and increased positive spillover

in either direction (Grzywacz & Marks, 2000b). Recent research has extended the

role theories to account for the demands and resources in each setting that lead to

conflict and facilitation (Voydanoff, 2002, 2004b) and further elaborate how

enrichment can occur (Carlson et al., 2006; Greenhaus & Powell, 2006). These

developments address shortfalls in previous conceptualisations of the work-life

interface and account for the both positive and negative aspects of working life.

In Europe, the major focus of the work-life interface has been the Demand-

Resource-Support model (Geurts & Demerouti, 2003; Geurts, Kompier, Roxburgh,

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& Houtman, 2003) as an extension of the Demand-Control-Support model of healthy

work (Karasek & Theorell, 1990). Sharing similarities with the model proposed by

Frone and colleagues (1997), interference (positive or negative) between work and

home is considered the result of the interaction of work and family characteristics,

which included both demands and resources within the domain. In a sample of postal

workers in Holland, for both genders the strongest association for negative

interference from work-to-home was with job demands, whilst job support predicted

positive interference for work-to-home (Demerouti, Geurts et al., 2004) and in call

centre workers, job resources reduced the impact of job demands and burnout

(Bakker, Demerouti, & Euwema, 2005).

There is a convergence of the ecological systems approach (Grzywacz &

Marks, 2000b), the European demand-resource-support approach (Bakker,

Demerouti, & Schaufeli, 2003; Demerouti, Geurts et al., 2004), and the expansion of

work-life conflict to enrichment (Carlson et al., 2006; Greenhaus & Powell, 2006) in

the conceptual models proposed by Voydanoff (Voydanoff, 2002, 2005b). The

ecological systems serve as the framework, with role theories providing the direct

and indirect linkages as demands and resources between the work-family interface

and the outcomes and stress theory giving guidance to the adaptive strategies that

may use be used (Voydanoff, 2005). Work demands are to be offset by family

resources, and vice versa, whilst boundary-spanning strategies are aimed at reducing

demands and increasing resources available to the individual. Whilst Voydanoff‟s

model is theoretical and as yet untested directly, many of the variables listed have

been empirically tested in other research and the model brings together a number of

theoretically similar but differently worded models from other researchers, such as

Frone, Grzywacz, Barnett, Geurts, Demerouti, and Bakker.

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The disadvantage of Voydanoff‟s model, however, is that the individual is not

deeply ingrained in the process, being only slightly described first by gender, social

class, and race and second as self-esteem and mastery associated with coping

strategies. The active participant is not accounted for and Voydanoff acknowledges

that such research is limited. Frone (2003) also notes that personal characteristics

have had only limited inclusion in the variables studied around the work-life

interface, and this lack hampers a full understanding of work-life issues.

Designing a research framework that incorporates both the active participant

with a broad and inclusive understanding of the work-life interface will address these

shortcomings and fill a gap in the work-life literature. Understanding the factors that

an individual can change themselves could inform future intervention programs. If

only workplace or organizational factors are important, then the individual is passive

and may have no recourse to changing their work-life interface. If as hypothesised,

individual differences are important to well-being of working adults, then the

participant can be active in managing and adapting their circumstances to best suit

their particular situation and needs.

1.4.2 Direction for the literature review of C, the context

From these theories, the work-life interface can be understood as a balance

between the demands and resources upon the individual and actioned by the

strategies that reduce demands and increase resources. Voydanoff (Voydanoff,

2005b) summarized the extensive research on the work-life arena to show that

demands and resources are both within each domain and span the boundaries

between domains. Much of the US research is based on the Michigan Organizational

Stress model (Kahn et al., 1964), whilst the European research perspective comes

from the Job Demand-Control-Support model (Karasek & Theorell, 1990) and the

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Jobs Demand-Resources (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001)

models. The latter European perspectives will be the framework for the workplace

factors used in this thesis, whilst the US research based on roles will be the

framework for the spillover between roles. The negative discourse of early research

on the work-life interface (Greenhaus & Beutell, 1985) has been expanded to include

the positive interactions (Greenhaus & Powell, 2006) between work and family

domains which will be considered here as the quality and direction of the spillover

between roles (Grzywacz & Marks, 2000b).

Across the different research paradigms, there is general agreement on what

constitutes the demands and resources of the work-life interface. Work demands

within the domain include work hours (paid and overtime), workload and job

insecurity, whilst the border-spanning demands include travelling away from home

for work and working at home. Family domain demands can include care for young

children or elderly relatives, household chores and responsibilities, whilst boundary-

spanning demands are the commute to work and family responsibilities that intrude

on the work day. Resources on the other hand are the things that make life function

more easily, such as autonomy at work, the support of supervisors and co-workers, as

well as support from family and relatives, the activities that give rewards for the

efforts that are made, such as pride in one‟s work or parental rewards. Boundary-

spanning resources include spousal employment, assistance from partner and family

with family responsibilities (Voydanoff, 2005b). The Conservation of Resources

theory (Hobfoll, 1989, 2001, 2002) views resources of primary importance to stress

and burnout processes. Ii is proposed that individuals will be distressed by the loss,

the threatened loss or the failure to gain resources after appropriate effort. Hobfoll

(Hobfoll, 2001) lists the resources that individuals value in their own right and as

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ways to accrue more resources over time which he proposed that individuals will

strive to do, to act as buffers in future challenging times. Examples of these resources

include sense of optimism, time for work, time with loved ones, positive challenging

routine, personal health, feelings of control over life, and the ability to organize tasks

(Hobfoll, 2001). In line with Bronfenbrenner‟s model, such actions to accumulate

resources would lead the active individual to interact with their environment in ways

that promote their competent development in the longer term.

The following review will highlight the factors that will be considered in the

analyses to be conducted in the thesis, with the emphasis on workplace resources.

From the theories outlined in the previous section, these factors will represent the

roles, activities and relationships that the individual has in their environment, to

capture Bronfenbrenner‟s Context. Working hours will be considered first as the

length of the working week is often considered as a major source of conflict for the

work-life interface. A discussion of the factors of the Demand-Control-Support

(Karasek & Theorell, 1990) model and the Demand-Resources model (Demerouti et

al., 2001) will follow, then affective commitment, managerial support and family

characteristics will complete this section. Finally, a consideration of multiple roles

and spillover will be given, including a comparison of job types, the importance of

roles (and some more about gender) and the influence of workplace and family

factors on spillover. The section finishes with a comparison of the different methods

of measuring and conceiving work-life balance and work-life fit which is

surprisingly not the same as work-family spillover.

1.4.3 Working hours and schedules

As noted in the section about gender and working, there are differing views

on working hours, such that long hours are a „work-life collision‟ (Pocock, 2003) or

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are „extreme jobs‟ of great allure (Hewlett & Luce, 2006). Time-based conflict

between work and family roles has been a major focus of research since the early

conception by Greenhaus and Powell (1985), where inadequate time for both roles

was considered to be a significant source of strain for working adults. This section

will cover the length of the working week in Australia, consider the impact of

preferences and choice for working hours and choice, commuting (as part of the time

committed to work), whether the choice to work long hours is more important than

actual hours as choice is about personal flexibility of time rather than being the

organization being flexible for their clients, which is client-focused but not

necessarily employee-friendly.

In Australia, the Australian Bureau of Statistics (2006a) report that the

average working hours had fallen slightly from 39.7 hours per week in 1985 to 39.3

hours in 2005 for men and similarly for women, from 29.4 to 29.0 hours per week.

The standard week is defined as 35 to 40 hours per week, with full-time work as

greater than 35 hours per week. The decline in working hours reflects the increase in

both men and women who are working part-time. For full-time workers, the average

working week increased from 1985 to 2005 from 41.3 hours per week to 43.2 hours

for men and 37.6 to 39.3 hours per week for women. However, it should be noted

that the length of the working week peaked around the year 2000, at around 44 hours

per week before falling to the 2005 levels (Australian Bureau of Statistics, 2006a). In

the work-life literature, there is an extensive focus on the individual‟s who work very

long hours, which is considered to be those who work more than 50 hours per week.

The ABS data reports that 30 % of men and 16% of women work very long hours

and these people are in occupations that have high levels of self-employment, as

professionals, tradespeople, or sole-traders; managers and administrators; employers;

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and employees in the mining industry. Longer working hours are also associated with

occupations or industries where part-time work is limited or practically difficult to

implement, such as for a self-employed person (Australian Bureau of Statistics,

2006a). It should also be noted that longer hours are usually associated with the

ability to earn greater incomes and more complexity than those who work less hours

(Barnett, 1998).

The debate about the effects of working hours on mental health and well-

being can be summarized as either a wholly negative influence, as a tragedy

(Relationships forum Australia, 2007) and a collision between work and family

(Pocock, 2003) or a more balanced understanding that working hours can be positive

and negative for the individual, labelling workers „conscripts or volunteers‟ (Drago,

Wooden, & Black, 2006, 2007) or wildly exciting, as an „extreme job‟ (Hewlett &

Luce, 2006). The differences may lie as much in the research paradigms of the

authors as consideration of the effects of work schedules. Whilst research has shown

that working schedules can be detrimental to mental health and well-being, it is

important to consider the role of the individual in choosing their occupation and

whether they take steps to take control of their situation and leave a job that is

counterproductive to their family responsibilities. The time demands of some jobs,

such as those in the multinational legal and accounting firms are well known and it

seems unreasonable to complain about work schedules whilst collecting an

enormously large pay packet. It is likely that for some people, the lure of wealth and

status could outweigh the appeal of close, personal relationships and they feel this is

an acceptable trade-off.

Whilst longer working hours are associated with more stress and feelings of

being overworked, compared to those working many fewer hours, this was often as a

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result of immediate job demands rather than ongoing situation of high job demands

(Galinsky, Kim, & Bond, 2001). Care should be taken when simplistically comparing

employees on less than 20 hours per week and those working over 50 hours per

week, as two points are missed. First that people who are working part-time may

have very different life situations and motivations than those working full-time.

Second and perhaps more fundamentally, merely measuring long hours does not

determine whether the person has chosen their hours or it is a requirement of their

employment.

Employees who believe that they can not change their schedules or have little

flexibility are more likely to feel overworked than those with flexibility in their

schedule (Galinsky et al., 2001). Analysis of Waves 2 and 4 of the Household,

Income and Labour Dynamics (HILDA) study (Drago et al., 2007) classified

individuals working over 50 hours either as volunteers (who preferred their long

hours) or conscripts (who preferred less hours) and then compared both groups with

those people working less than 50 hours per week over two waves of data collection.

People who were long work hour volunteers over time recorded the highest wages

and were more than four times more likely to be self-employed whilst public servants

were significantly less likely to work long hours for whatever reason (Drago et al.,

2007). Being promoted and being self-employed, however, gave mixed outcomes as

both were associated with voluntarily working longer and with being conscripted to

work longer. The extra responsibilities of one‟s own business as well as a new

position mean that extra hours can be a burden as well as a challenge. Debt was

associated with the conscript groups, perhaps indicating that „work and spend‟ is a

persistent cycle over time although a significant number of former conscripts with

debt do become volunteers for longer hours (Drago et al., 2007). Interestingly,

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women, either as parents or non-parents were less likely to work voluntarily or

involuntarily work longer hours over time (Drago et al., 2007) which confirms the

ABS data that only 16% of women work over 50 hours per week (Australian Bureau

of Statistics, 2006a). For mothers, combining their work, their partner‟s work and

family and children activities, it was the time pressure of these schedules and

achieving the necessary family flexibility to maintain these schedules that was

problematic rather than work hours per se (Baldock & Hadlow, 2004).

Is it reasonable to conclude that there a „time squeeze‟ happening?

Hochschild (1997) interviewed middle and upper management of a large Fortune 500

corporation and stated that for most, work felt like home and home felt like (hard)

work. Employees therefore preferred working long hours, as their home life was less

attractive than their work role. However, research from the Survey of Ohio‟s

Working Families tested this view in a community sample and did not support

Hochschild‟s proposition that workers unhappy at home would work longer hours.

Rather the survey found that demanding immediate supervisors but not

organizational policies predicted longer work hours, as did having a higher

education, working for a larger organization, being at a higher level of management,

having a professional occupation or being male (Maume & Bellas, 2001). In the

National Study of Families and Households in the USA, working hours for couples

was based on whether both spouses worked (for example, traditional or dual career

couples), employer demands, and the nature of the work involved (Clarkberg &

Moen, 2001). Traditional couples (husband full-time, wife at home) and dual-career

couples were more likely than neo-traditional couples (husband full-time, wife part-

time) to have schedules that they preferred. For this sample, the work was „pre-

packaged‟ and „institutionalized‟ such that jobs come with expectations of workloads

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and time commitments, particularly for professional and managerial appointments

that did not allow sufficient part-time work to satisfy employee requirements

(Clarkberg & Moen, 2001). Number of hours when considered alone, does not

provide sufficient information to determine if they are problematic as it is necessary

to understand more of the organizational context and the timing of working hours

before judging that.

There are also the individuals that choose to work very long hours, who work

in „extreme jobs‟. Whilst this is a very small proportion of working adults, there

seems to be many who are highly committed to their work for themselves or their

employers, earning very large incomes and thriving on the challenges that their work

brings (Hewlett & Luce, 2006) Rather than just consider hours, it is important to

consider the individual‟s preferred working hours. It is choice that is important, as an

aspect of control, as will be seen in the Job Demand-Control-Support model and the

Jobs Demand-Resources model that makes the difference with the effect of working

hours on mental health and burnout.

1.4.4 Demands and resources

Rather than discuss the separate elements of these two models of the

workplace, in this section the review will consider demands and resources together,

as this is way that this research is structured. It is interesting to note that European

research does not consider mental health outcomes, being concerned mainly with the

outcomes of burnout and work engagement, with the addition of ill health and the

intention to look for another job. From the perspective of research in the USA, rather

than use the terms, „demands‟ and „resources‟, factors of the workplace and family

are referred to as stressors and are applied largely to the work-life interface, rather

than the workplace alone, as is the case with a substantial portion of the European

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research that is outlined here. As noted in the review by Voydanoff (2005b), there is

a convergence of research to understand that stressors and demands and resources are

essentially one and the same thing.

The research for the Job Demands-Resources model mostly consists of

structural equation models where the demands and resources are the latent variables

and the indicator variables are the individual components such as job autonomy, skill

discretion and psychological job demands. The Job Demand-Control-Support (DCS)

model arose from early research about the effect of poor working conditions on

cardiovascular disease among Swedish and American employees (J. V. Johnson &

Hall, 1988; Karasek, 1979; Karasek & Theorell, 1990). The model has three axes:

first, the psychological demands of a job; second, the decision latitude available (i.e.

control); and third, the social support from those in the workplace. Job demands are

restricted to the psychological demands whilst control is limited to skill discretion

and decision authority. High demand, high control jobs were active and lead to

mastery, reducing perceptions of job strain and leading to reduced rates of heart

attacks (Theorell et al., 1998). Whilst the combination of five human sector work

groups (for example, health care and retail) found that there was no interaction

between demands and control, when health care, transport and warehouse employees

were considered separately there was an interaction present as predicted by the

model. Specifically, high job demands and low job control increased emotional

exhaustion and health complaints, whilst high demands and control lead to greater

job satisfaction among these employees (de Jonge, Dollard, Dormann, Le Blanc, &

Houtman, 2000).

When nurses were studied, fatigue was predicted by greater job demands, less

control and less social support with greater fatigue found in those nurses with higher

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job demands and low job control (van Ypern & Hagedoorn, 2003). Similarly, among

health care workers, greater job autonomy increased job satisfaction when

psychological job demands were high and increased job involvement when physical

demands were higher (de Jonge, Mulder, & Nijhuis, 1999). It is also interesting to

note that low autonomy blunted the effect of emotional demands on psychosomatic

health complaints (e.g. headaches), contrary to the expectations of the model. These

results suggest that being able to attribute lack of control over patient outcomes,

rather than being able to „cure‟ each and every patient may be protective for

healthcare workers (de Jonge et al., 1999).

A review of the Job Demand-Control-Support (DCS) model (Van Der Doef

& Maes, 1999), however found that there is limited support for the buffering effect

(job control and support reducing job demands) for psychological outcomes. Rather,

the strain (high demand, low control) and iso-strain (high demand, low control, and

low support) hypotheses of health impairment were supported in about half of the

studies and for cross-sectional studies rather than longitudinal studies (Van Der Doef

& Maes, 1999). The authors noted that is significant variation in the definitions,

measurements and samples which contributed to the lack of confirmation for the

DCS model. Expanding the extent of job demands to include organizational risk

factors (Akerboom & Maes, 2006) and adding physical and emotional job demands

(de Jonge et al., 1999) accounted for additional variance in the analyses of job

satisfaction, emotional exhaustion and somatic health complaints which indicates

that the factors that influence employee well-being may have initially been too

narrow.

The Job Demands-Resources (JD-R, Bakker et al., 2003; Demerouti et al.,

2001) model builds on, but is separate from the JDCS model by expanding the

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workplace characteristics that could be considered as demands and resources. In the

Demands-Resources model, demands sap energy, whilst resources build motivation.

Specifically, job demands require sustained physical and mental efforts that drain the

individual‟s energy and lead to burnout, especially emotional exhaustion. Resources,

on the other hand are the social, psychological and physical aspects of work that can

reduce the effect of job demands, help achieve work goals, and assist personal

growth and development which in turn increase the individual‟s work engagement

(Bakker et al., 2003; Demerouti et al., 2001).

The research based on the JD-R model is largely analysed with SEM, with

demands, resources and the outcomes used as the latent variables in the modelling.

Demands are mostly conceived as the job‟s workload, emotional demands, and

physical demands with resources measured broadly as social support from co-

workers, supervisor support, time control and organizational climate, with the

outcomes ranging from burnout, work engagement, ill health and absenteeism to

organizational commitment (Bakker et al., 2005; Bakker et al., 2003; Bakker et al.,

2006; Hakanen et al., 2006; Schaufeli & Bakker, 2004). In these analyses, the SEM

pathways confirmed the hypothesised relationships. Among call centre workers,

demands lead to poorer health and then to greater absenteeism whilst resources lead

to greater job involvement and less intention to leave their current job (Bakker et al.,

2003). Across four different occupational groups (i.e. insurance, occupational health

and safety, pension fund, and home-care), job demands increased burnout which

increased health problems, whereas job resources increased work engagement and

employees were more likely to stay in the jobs (Schaufeli & Bakker, 2004).

There were similar outcomes for Finnish teachers, with their increased work

engagement lead to greater organizational commitment (Hakanen et al., 2006). Using

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moderated SEM, in another sample if Finnish teachers, there were significant

interactions between student misbehaviour and the teachers‟ job resources (e.g.

control, social support, and organizational climate). For teachers with high levels of

resources, increasingly poor student behaviour has little effect on their work

engagement whereas teachers with less resources showed significant reductions in

the measure of teacher engagement (Bakker, Hakanen, Demerouti, & Xanthopoulou,

2007). For German teachers, exhaustion was also increased by poorer student

discipline, in addition to teaching more classes and having less social support from

family and friends. Engagement in this study was considered as career ambitions and

exertion at work and was greater with the support of the teachers‟ principal and

among younger staff (Klusmann, Kunter, Trautwein, Ludtke, & Baumert, 2008a).

Among university staff, job resources buffered the effect of job demands on burnout,

particularly where staff had greater levels of resources (Bakker et al., 2005). From

another perspective, resources can lead to more positive experiences such as flow (as

absorption, enjoyment and intrinsic motivation) at work. Among music teachers,

greater job resources, such as autonomy and social and supervisor support, led to a

greater balance between work challenges and the use of the individual‟s skills which

in turn increased the teachers‟ experience of flow in their work. Teachers who

experienced more flow also had students who were more likely to experience flow

during their music lessons (Bakker, 2005).

For Dutch police officers, excessive job demands led to burnout. Multi-level

analysis also found that individual-level burnout was predictive of belonging to a

work teams with greater burnout, with similar findings for work engagement. As

such, the social context of work teams is important to individual energy and

motivation, suggesting that either could be reinforced by the nature of the work team

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or environment (Bakker et al., 2006). Among Norwegian police officers, social

support reduced the officers‟ burnout whilst work-family interference increased their

burnout. Burnout was predictive of lower job and life satisfaction and poorer health

(Martinussen, Richardsen, & Burke, 2007). Unfortunately, the analysis article did not

consider mediation (Baron & Kenny, 1986) so it is unclear if the effect of burnout

mediated between work conditions and job and life satisfaction and health.

These studies use the Maslach Burnout Inventory (MBI, Maslach et al., 1996)

and similar findings arise in studies using the Oldenburg Burnout Inventory (OBI).

The MBI has three factors, exhaustion, cynicism, and professional efficacy and the

OBI has two factors, exhaustion and disengagement, which is similar to cynicism in

the MBI. Among German nurses, job demands (e.g. workload, shiftwork, and time

pressure) led to exhaustion and job resources (e.g. supervisor support, feedback, and

control) reduced the nurses‟ disengagement, with both exhaustion and

disengagement being negatively related to the nurses‟ life satisfaction (Demerouti,

Bakker, Nachreiner, & Schaufeli, 2000). Finally, in a diverse sample of European

employees, job demands lead to exhaustion and reduced the employees‟ in-role

performance whilst job resources reduced both exhaustion and disengagement with

disengagement leading to a reduction in extra-role performance (i.e. doing more than

is required for one‟s job) (Bakker et al., 2004).

From these studies, the importance of resources to the positive outcomes can

be seen. The resources available from the workplace maintain motivation and

prevent the loss of energy, buffering the demands upon the individual. Having

control of ones‟ activities and the ability to use one‟s talents, having the support of

supervisors and co-workers underpins engagement and good physical health. The

resources available from the workplace are an important buffer for the demands on

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the individual, and the role of individual characteristics is not generally considered.

Only in the article on resources and the experience of flow (for example, Bakker,

2005) does the author note that understanding the balance between demands and

resources would be better understood by including all available resources, both

personal and job, into the analyses. This underlines the importance of using the

framework of Bronfenbrenner‟s model as the basis for this thesis as the influence of

the person and their working conditions can be jointly considered.

1.4.5 Affective commitment

In the research detailed in the previous section, affective commitment is used

as a measure of organizational commitment. In this manner, affective commitment is

often treated as an outcome measure for the individual and is taken as a consequence

of the demands and resources of the workplace (for example, Grebner et al., 2003;

Hakanen et al., 2006; Martinussen et al., 2007). However, as the outcomes for this

thesis are broadened to include well-being, mental health, burnout and work

engagement, affective commitment will be considered as a contextual factor that

contributes to the well-being, mental health and engagement in work of the

individual. Affective commitment is a component of organizational commitment, as

the desire and attachment to membership in an organization (N. J. Allen & Meyer,

1990; Meyer & Allen, 1991). A meta-analysis found that it has been widely used in

research as both a predictor and outcome variable (Meyer, Stanley, Herscovitch, &

Topolnytsky, 2002). A sense of affective commitment to the organization has shown

to be important to increasing organizational productivity (Harter, Schmidt, & Hayes,

2002) and reducing intentions to leave an employer (W.J. Casper et al., 2002) while

being linked to greater job satisfaction and performance (Meyer et al., 2002). By

using affective commitment as a predictor, the current research expands the

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understanding of attachment to the workplace influences well-being and mental

health and maintains work engagement.

The engagement that an employee feels for their workplace is associated with

the informal support that they receive from their manager and co-workers. These

positive perceptions of intangible support, rather than tangible and instrumental

support, predicted greater affective commitment to the employer and reduced the

employee‟s activities to search for another job after 18 months in full-time

employees in a variety of occupations (C. A. Thompson, Jahn, Kopelan, & Prottas,

2004). Among employees of the Canadian civil service, greater levels of autonomy

and supervisor support increased affective commitment which substantially reduces

the employees‟ intention to leave their workplace (Ito & Brotheridge, 2005). Giving

employees more control demonstrates the value that the employer has for their staff,

reaffirming the staff‟s attachment to their workplace. Part-time retail employees had

greater affective commitment in jobs where they have more opportunities to learn

more job skills combined with higher levels of work flexibility and greater

communication of management decisions. The combination of these workplace

factors allowed part-time employees to feel that they were able to progress in their

careers which increased their commitment to their employer (Ng, Butts, Vandenburg,

DeJoy, & Wilson, 2006).

Being connected to the workplace is important whether employees are in an

office or using other working arrangements. Organizational connectedness is shown

to be important to the adjustment of workers, particularly men to the „virtual‟ office

(Raghuram, Garud, Wiesenfeld, & Gupta, 2001). Greater support from the

organization for personal commitments resulted in increased trust and affective

attachment in employees toward the organization in software workers (Scholarios &

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Marks, 2004) and more altruism among university employees (Jex, Adams,

Bachrach, & Sorenson, 2003). For temporary workers, affective commitment was

increased by organizational support from both the placement organization and the

organization with whom they were temporarily working. These workers were more

likely to continue their temporary work when they felt a connection with their

temping agency, perhaps because of continuing availability of quality employment

opportunities (Connelly, Gallagher, & Gilley, 2007). Given the aging of the

population and the changing nature of employment, understanding how the

workplace culture can influence well-being is important to the way businesses in the

future will attract and retain valued employees.

1.4.6 Managerial support of work-life issues

The importance of social support for both co-workers and supervisors has

been highlighted in the previous section on demands and resources. The focus of this

section will be on the role of managers in supporting the individual to manage their

differing roles. Managerial support of flexible work practices can be considered the

practical expression of an employer‟s attitude toward their employees‟ work-life

concerns as flexible schedules do not occur without the compliance and direction of

managers.

It is important to consider the benefits to companies who have family-friendly

policies for their employees. During the recent boom economic times, benefits

represented a way for employers to retain valued staff (who might otherwise leave

for more amenable employment) and avoid the costs of hiring and training new staff.

In the current climate of financial gloom, family-friendly benefits can become a

motivating force for employees to maintain productivity as employees are more

likely to support an employer who has supported them (Eisenberger, Huntington,

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Hutchinson, & Sowa, 1986).

Whatever the economic conditions, the long-term survival of any business

relies of having and keeping employees who are motivated, skilled and commited to

their employer (De Cierci, Holmes, Abbott, & Pettit, 2005). The Family Friendly

Index (FFI) was developed by the Family and Work Institute (Galinsky, Friedman, &

Hernandez, 1991) to quantify the benefits available to employees, for example,

flexible work arrangements, financial assistance, dependent care services and

management change in Fortune 1000 companies in the USA. The most common

reasons that companies gave for implementing work-family policies was to improve

morale and retention of staff and as a recruiting tool for new staff whilst cost was

cited as a substantial barrier to implementation (Galinsky et al., 1991). Clifton and

Shepard (2004) calculated that for the 108 companies that had matched financial

outcomes with ratings on the Family Friendly Index, the companies that score higher

on the FFI were more productive with a 10% increase in the index leading to an

increase in productivity of 2-3%. In research in the banking sector, Tombari and

Spinks (1999) found that flexible work arrangements had only positive effects for the

organization, increasing retention and commitment of employees, increasing work

performance and job satisfaction and increasing customer satisfaction. Interestingly,

family commitments accounted for only half of the reasons for taking up flexible

work arrangements with continuing education, beginning of retirement and personal

interests also important to employees(Tombari & Spinks, 1999).

These sound economic reasons for implementing work-life strategies,

however, can be limited by organizational barriers to change. Where there is

organizational inaction on work-life issues and organizational values and where work

output is valued over personal needs, employees are less likely to find balance

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between work and family domains (De Cierci et al., 2005). Managers are more likely

to recommend work-life programs for their subordinates when they have sufficient

knowledge of the programs and believe that the programs will bring positive benefits

to their company as well (W. J. Casper, Fox, Sitzmann, & Landy, 2004; Maxwell,

2005) whereas unsupportive managers could subvert company policies by not

implementing or implementing unevenly the company‟s policies (Starrels, 1992).

Supportive practices are also more likely to be found in smaller companies than

larger companies, possibly due to the owners and managers in these smaller

workplaces knowing the staff personally and being aware of their individual needs

(Bond, Galinsky, Kim, & Brownfield, 2005). Therefore, although there can be

barriers to the availability of work-family policies, there are benefits when these

opportunities to improve working conditions are taken.

When employees believe that their organization is family-supportive, the

employees who were in diverse occupations reported more positive outcomes, with

greater job satisfaction and organizational commitment, less work-life conflict and

less intention to leave their employer (T. D. Allen, 2001). Similar results were found

among university staff as healthy workplace practices, such as involvement in

decision making, staff recognition and work-life balance policies predicted greater

well-being and organizational commitment and less exhaustion and fewer turnover

intentions (Grawitch, Trares, & Kohler, 2007). It is not only parents who benefit

from family supportive workplaces as the affective commitment of all employees is

increased where these policies are available (S. E. Anderson, Coffey, & Byerly,

2002; Grover & Crooker, 1995), reflecting that family-friendly policies reflect a

sense of care for the employees.

To clarify whether the perception of support comes from an overall

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organizational position or is due to the actions of immediate supervisors, Thompson

and colleagues (C. A. Thompson et al., 1999) developed a work-family culture scale

that measured managerial support for family-friendly policies, the career

consequences of using family-friendly benefits and the time demands of the

organization. Having a manager who is actively supportive was predictive of

utilization of available benefits regardless of parental status or gender of the

employee and additionally, the positive work-family culture was predictive of

decreased turnover intention and less work-family conflict (C. A. Thompson et al.,

1999). Importantly, where there is a supportive work-life culture and supervisors

encouraged uptake of policies for flexibility, participation in those programs did not

lead to reduced opportunities for promotion and work hours were considered

reasonable by the Dutch employees. These employees also reported that there was

little interference between work and home (Dikkers et al., 2004), with similar results

found for American employees (Behson, 2005; C. A. Thompson et al., 1999).

Informal support for the individual has proved to be more important than just

having formal policies for flexible work practices, increasing job satisfaction and

commitment to the organization and reducing conflict between work and non-work

domains and intentions to leave the organization (T. D. Allen, 2001; Behson, 2005).

Similar results were found when employees were followed over 18 months, as

supervisor support was strongly predictive of greater organizational commitment and

less job searching at the later time (C. A. Thompson et al., 2004). Whilst perceived

organizational support is important to job satisfaction and affective commitment, the

positive work-family culture was strongly associated with reduced work-family

conflict among employed students (Behson, 2002). Managers who support family-

friendly policies provide a positive organizational culture that benefits both employer

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and employee alike.

As noted previously in the section on gender and work hours, taking

advantage of flexible hours challenges the notion of the ideal worker as the

breadwinner for whom work dominates life. The manager who is supportive of the

implementation of family-supportive has links with Karasek & Theorell‟s (Karasek

& Theorell, 1990) model of healthy work where active jobs have demands that are

matched by high levels of control and workplace support. The support component of

their model represents the support of the manager gives to solving work-life

problems as well as solving every day work problems. It is the practical support of

allowing employees to manage all aspects of their lives that makes managerial

support important to understanding the work-life interface, rather than a more

abstract mission statement from the corporate executives.

1.4.7 Family characteristics

Family characteristics that influence well-being and mental health have been

addressed earlier in this thesis. For example, the Personal Well-Being Index was

highest amongst married individuals whilst the lowest was amongst those individuals

without a partner (Cummins, Woerner et al., 2007). Additionally, the discussion of

the influence of gender includes consideration of gender and parenting and the

following section on the work-life interface will consider the impact of the role of

parents and care of children and elders. Family characteristics are discussed

throughout this chapter where studies include such detail in relation to the workplace

and this section will discuss the effects of marital quality, the presence of partners

and children, how parental workplace experiences affect children and their

employment expectations. Family in the current thesis is taken in the broadest sense

to include parents and their children, couples alone, adult children and their parents,

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adult siblings together, and extended kin relationships. The following section on

multiple roles and spillover will further discuss how work and family spheres

interact.

Interviews with dual-career couples (i.e. both partners have professional

occupations) found that couples coped together with the stressors of their jobs (Bird

& Schnurman-Crook, 2005). Strategies included sharing chores at home, talking to

each other, understanding and using humour to lighten their partner‟s mood and

encouraging their children to help with chores from any early age. Strategies to

manage family stressors included giving priority to their children, reducing

community involvement, changing expectations about housework and accepting

family differences (Bird & Schnurman-Crook, 2005). The combination of these

strategies would allow the individuals to successfully manage the competing

demands on themselves. The support and assistance from their partners could explain

why the presence of a partner is a protective factor for well-being, as shown in the

Personal Well-Being Index (Cummins, Walter, & Woerner, 2007). However, marital

quality can be reduced when wives are depressed. Rather than be supportive of their

partner‟s problems, depressed women were more dissatisfied with their husbands,

complaining about their children, division of housework, their spouse‟s stubbornness

and problems with sex. They also showed little affection, such as providing comfort

and help or physical contact toward their husbands (Coyne, Thompson, & Palmer,

2002).

Marriages (or commited relationships) last over time as the couples build a

strong sense of togetherness (whilst allowing for autonomy), shared coping with

crises, managing conflict, provided each other with emotional support and

importantly, shared laughter (Wallerstein & Blakeslee, 1995). Meta-analysis of

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marital quality and well-being found that the positive relationship between marital

satisfaction and well-being became stronger for longer marriages (more than eight

years) with men and women having the same outcomes. However, discord over time

had a stronger effect to decrease marital satisfaction and well-being in the long term

(Proulx, Helms, & Buehler, 2007). These studies contrast the effects of supportive

and non-supportive relationships with the greater benefits for long term health

derived from the partners being supportive.

The presence of children adds another dimension to the influence of marriage

on well-being and mental health, changing the dynamics and experience of the

relationship (Bradbury, Fincham, & Beach, 2000). Family responsibilities also

change involvement in the workplace, influencing decisions about jobs, based on the

needs of the family schedules (both partners and children as well) with gendered

expectations entering the balance. As noted previously in the section on gender,

gender roles still guide life choices with men seeing themselves as breadwinners and

women being more involved with their children (2006; Loscoco, 1997). Parenthood

increased the intrinsic value both men and women received from their work (M. K.

Johnson, 2005) and provided opportunities for psychological growth through

involvement in parental roles throughout the child growth and development

(Palkovitz, 1996). Preschool children with more negative temperaments were

associated with more challenging behaviours and more inattention at preschool and

with their mothers‟ reporting more daily hassles (Coplan, Bowker, & Cooper, 2003).

For children of all ages, when the parent and child have positive relationships,

parents report better well-being. However, parents who were concerned about what

their children are doing after school report less psychological well-being, measured

as positive affect, with more concern about the arrangements and friends of

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daughters rather than sons, regardless of age (Barnett & Gareis, 2006).

When marriages break down, children raised in unstable single parent

families have poorer outcomes as they are influenced by maternal characteristics and

the number of family transitions that occur (Fomby & Cherlin, 2007). In employed

Canadian mothers, single mothers felt as supported within their workplaces as

married mothers when working in similar position. However, single mothers reported

lower incomes, more family demands and more family interfering with work

(McManus, Korabik, Rosin, & Kelloway, 2002) which would be expected as they are

without another adult to share work and family responsibilities. The link between

economic security and family arrangements for children where single parents,

particularly mothers have limited education, less full-time employment and less

stability in housing led the children have more limited opportunities when the

children themselves became adults (Bianchi, 1995). Postsecondary education lead to

better jobs and little poverty among both single mothers and fathers, although single

fathers are more likely to be better off financially (Zhan & Pandey, 2004). Family

structure, as the presence or absence of a partner and or children, is an important

filter through which work and family situations should be considered.

Parents‟ experiences in the workplace also affect their children. Among

fathers of toddlers, fathers had better knowledge and more involvement with their

child when their wife worked and when the fathers did not feel stressed by their own

work (Corwyn & Bradley, 1999). For parents of adolescents, there was a significant

link from increasing parental work pressure to increased parental role overload to

increased conflict between the parent and their adolescent, which in turn lowered the

self-worth and increased depression among the adolescents. The role overload felt by

mothers particularly increased conflict with younger adolescents whilst fathers‟ role

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overload increased conflict with older adolescents (Crouter, Bumpus, Maguire, &

McHale, 1999). Mothers had less conflict with their adolescent sons, as fathers did

with their adolescent daughters, as opposite dyads were more accepting and flexible

in parent-child interactions (Fortner, Crouter, & McHale, 2004). There is a changing

dynamic in parent-child relationships across time that adds to the way that the work-

life interface is experienced.

The time required caring for children decreases as the children grow older,

being about half the time in adolescence from preschool ages (Wallace & Young,

2008). Among lawyers, the hours billed by mothers increased as her children became

older, becoming equivalent with women without children billing when their children

were adolescents. Whilst less time spent on house work and parenting activities

increased the productivity of the women lawyers, these factors did not impact on

male productivity as male professionals are more likely to have a stay-at-home

spouse. The productivity of male lawyers was higher in larger firms, with greater

workloads, and jobs with less job flexibility and when the men had greater career

commitment (Wallace & Young, 2008). When children became adults and prepare to

enter the workforce, their perceptions of the workplace were effected by their

parents‟ experiences of work. Young adults at university whose parents, particularly

their mothers, had experienced job insecurity were more likely to view the world as

unjust, to have more negative moods (i.e. anger and anxiety) and to do less well at

school (Barling & Mendelson, 1999). The negative outcomes from work demands

affect not only the individual worker, but their family as well, but work that is

interesting and meaningful provides benefits to both (Bryant, Zvonkovic, &

Reynolds, 2006). The number and age of children, which reflect parental and family

demands (Frone et al., 1992a) will included in the current thesis to reflect the

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changing influence of children on parents‟ working patterns, particularly that of

women. Comparison of the well-being and mental health of parents and non-parents

will also assess whether the child-free option creates better mental health.

1.4.8 Multiple roles and spillover

Implicit in the study of work-life conflict is that an individual has the multiple

roles and that these multiple roles are associated with role strain, due to a scarcity of

time and energy because of conflicting role demands (Goode, 1960). This premise

guided the early conception by Greenhaus and Beutell (1985) of work-life conflict as

role pressures that come from the time involved and the strain associated with

fulfilling the demands of multiple roles and the consequences of multiple roles

having differing behavioural standards (Frone, 2003; Geurts & Demerouti, 2003;

Goode, 1960; Greenhaus & Beutell, 1985).

That work-family conflict has been the focus of much research also highlights

that the belief that parents, and mothers in particular, would be more likely to have

conflicts between their roles than non-parents. Research, however, shows that both

parents and non-parents are appreciative on family-friendly benefits, showing

increased attachment to their workplaces (Grover & Crooker, 1995). Understanding

the work-life interface acknowledges that people are involved in many activities

beyond childcare, and that eldercare, volunteer activities and leisure pursuits can

occupy time and energy in the same manner as raising children can. Work-family,

work-nonwork, and work-life are all terms that can be used interchangeably,

acknowledging that many of the factors that effect parents also affect non-parents

(Geurts & Demerouti, 2003), such as working hours (Moen & Sweet, 2003) and

burnout (Maslach et al., 1996; Maslach & Jackson, 1981). In the Dutch research of

Geurts, Demerouti and colleagues, family, life and home are synonymous and

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parental status is usually not given for their samples (Bakker & Geurts, 2004;

Demerouti, Bakker et al., 2004; Geurts et al., 2003). By including both parents and

non-parents in the current project, the research can explore the well-being of all

working adults, regardless of parental or marital status and draw on research from the

work-family, work-non-work, and work-life interface.

Role salience of multiple roles with the value and commitment to

occupational, marital, parental and household roles are linked to the expectations

about the nature and content of roles. In turn, expectations are guided by the personal

relevance of the role, the expected standards required to perform the role and the

resources that an individual will bring to bear upon the role (Amatea, Cross, Clark, &

Bobby, 1986). Where resources of time and energy are considered to be limited, a

scarcity approach leads to the view that multiple roles can only lead to role strain and

conflict between the roles (Goode, 1960; Greenhaus & Beutell, 1985). However,

when time and energy are considered flexible, multiple roles are energy-expansive

(Marks, 1977) and the meaning and involvement come from role balance across roles

(Marks & MacDermid, 1996). Role salience and role balance can be combined in the

concept of „dual-centric‟ individuals, who have high commitment to both work and

family roles. These individuals enjoyed objective and subjective success in their

careers and subjective satisfaction from their lives (Galinsky, 2003). Contrary to

expectations for higher levels of management in the international companies

surveyed, women managers had not given up more in their family and personal lives

(such as marriage or having children) than women in lower levels of management.

Importantly, whilst two thirds of managers of either gender valued their work more

than their families or personal life, broadening one‟s focus led to greater personal

satisfaction, less stress and feeling more successful among the dual centric

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individuals who valued both domains (Galinsky et al., 2003). By including the

salience and involvement in roles, the relative importance and value that an

individual places on each of their roles may add to the understanding of the complex

relations between time allocation and the gender of the person filling the role.

1.4.9 Exploring the interactions between work and non-work domains

Following on from the early research by Greenhaus and Beutell (1985) that

explored the time, strain and behavioural conflict between work and family roles,

Frone and colleagues (Frone et al., 1992a) proposed that conflict was bidirectional

between work and family domains and that domain-specific antecedents should be

considered. This early research amongst married parents in the USA found that there

was a strong, positive reciprocal relationship between work-family conflict and

family-work conflict. Within either domain, stressors lead to greater distress (i.e. job

stressors significantly predicted job distress) whilst involvement reduced distress

(e.g. job involvement to job distress). The relationships found in the models were

similar for both genders and across racial groups although there was significant

relationship between job involvement and work-family conflict among white collar

workers, but not among blue collar workers (Frone et al., 1992a). In addition,

conflict between work and family occurred more often and more extensively than

family to work conflict, which indicated that the family boundary was more

permeable than the work boundary for parents. There was a small difference between

the genders with women experiencing somewhat more conflict overall than men

(Frone, Russell, & Cooper, 1992b). Further research among parents expanded on the

sources of conflict, finding that within-role distress, overload and time commitment

in the separate areas of work and family areas lead to work-to-family and family-to-

work conflict, respectively. Greater obligations toward a particular role led to

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reduced involvement in the second role, such that work-family conflict then led to

withdrawal from the family role, and family-work conflict lead to withdrawal from

the work role (Frone et al., 1997).

However, only focusing on the negative interactions neglects the positive

effects gained from work and family life. Rather than interrole conflict that lessens

the second role, interrole facilitation makes the second role easier to enact by

providing opportunities to gain skills and resources (Frone, 2003; Greenhaus &

Powell, 2006). Grzywacz and Marks (2000b) proposed that both positive and

negative spillover occurred between work and family roles, therefore specifying both

the quality (positive or negative) and the direction of the interaction (work to family

or family to work) to incorporate role strain and role accumulation. These four

dimensions were found to be distinct (for example, Aryee, Srinivas, & Tan, 2005;

Grzywacz & Bass, 2003; Grzywacz & Marks, 2000b; Kinnunen et al., 2006) with

fewer resources leading to more negative spillover and greater resources leading to

more positive spillover. For example, working long hours and pressures at work

increased negative work-to-family spillover and low spousal support increased

negative family-to-work spillover whilst having greater decision latitude at work

increased both positive work-family and family-work spillover. Gender did not have

consistent effects although women experienced more positive work-family spillover

than men (Grzywacz & Marks, 2000b). In Europe, this taxonomy is referred to work-

home interference or home-work interference and again is either negative or positive.

There were some differences to the research from the USA, although with similar

overall trends such as work demands influencing negative interference and work

support influencing positive interference although there was no effect for gender

(Demerouti, Geurts et al., 2004).

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Early research was centred on understanding the predictors and consequences

of the negative view of the work-life interface. When career men with working (i.e.

dual-career men) or non-working wives (i.e. traditional career men) were compared,

the dual-career men experienced more work conflict impacting on family conflict

and more work-family conflict than traditional career men but this did not reduce the

quality of their work life (Higgins & Duxbury, 1992). Among male executives,

increasing work-family conflict increased job stress and reduced life satisfaction

which in turn reduced job satisfaction. Job satisfaction was also reduced by fewer

promotions, greater ambition to advance within a company and longer time in the job

whilst working for a successful organization that supported work-family policies and

earning more money than in the past increased job satisfaction (Judge, Boudreau, &

Bretz, 1994). Depression was also more likely among married parents as work-

family conflict and family-work conflict increased (Frone, Russell, & Barnes, 1996).

Further, increased family demands, taken as having children under six years and

greater family-work conflict increased the use of family-supportive programs such as

flexitime and child care among employed parents (Frone & Yardley, 1996).

1.4.9.1 Comparing types of jobs. In a comparison of self-employed

individuals and organizational employees, the self-employed had more autonomy

and more schedule flexibility in their work and greater job satisfaction, but they had

greater negative work-family spillover and less family satisfaction. Being self-

employed involved a trade-off between work resources and the time required to

ensure the success of their business with self-employed men having greater time

involvement with their work than women and self-employed women having greater

time involvement with their families (Parasuraman & Simmers, 2001). When

independent contractors (self-employed but with no employees) were compared with

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business owners (self-employed with employees) and organizational employees in

the National Study of the Changing Workforce, there were similar results. Business

owners worked more hours and felt more demands from their work but again had

more autonomy than either independent contractors or organizational employees

although they did not have any more work-family conflict. Independent contractors

were between the business owners and organizational employees, working the least

hours, having the least job pressure but being no more satisfied with their work than

the employee group (Prottas & Thompson, 2006). There is a paradox for business

owners of both studies, in that they „should‟ be more stressed due to their greater

workload and time involvement, but they are not. It would appear that the

independence inherent in business ownership is rewarding over and above the

pragmatic considerations of running the business.

Within a large construction company in Australia, there were differences in

outcomes based on where the employees worked for the company (Lingard &

Francis, 2004). The sample consisted on mostly men, with the small number of

women working in the different worksites reporting few differences among the

outcomes. Men who were working at the construction site as compared to men in the

site office or head office, had poorer relationships with their spouse or partner and

their children, less satisfying leisure activities and helped less at home. When the

work outcomes were considered however, these on-site workers had greater sense of

professional efficacy, but more exhaustion and cynicism and less satisfaction with

their pay although there were no differences in men‟s job satisfaction between the

locations. The authors suggested that it is likely that the actual work the men did

gave them a sense of competence and satisfaction but the problems associated with

being on-site and lack of contact with head office lead to more cynicism and

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exhaustion and dissatisfaction with pay (e.g. they worked hard, but were not well

rewarded) (Lingard & Francis, 2004).

Across 126 occupations rated in the General Survey Study in the USA

(Dierdorff & Ellington, 2008), there were differences in work-life conflict

experienced and the behaviours of occupations were considered to be important to

these outcomes. Interdependence with co-workers and responsibilities for other

employees but not interpersonal conflicts, were associated with greater work-family

conflict, as the effort to establish and maintain the cooperation with other people or

roles was considered more taxing. Police detectives, fire fighters, and family and

general practitioners had the highest levels of work-family conflict with the greatest

levels of interdependence and responsibility, whilst taxi drivers, insurance adjusters

and bank tellers having the lowest work-family conflict (Dierdorff & Ellington,

2008). It would seem that maintaining long term relationships requires additional

effort and involvement in the work domain which could leave less time and energy

available for the non-work domains. For example, taxi drivers and bank tellers have

more fleeting and superficial relationships with their customers or clients, compared

to general practitioners whose work revolves around a deeper understanding of their

patients‟ concerns.

1.4.9.2 Importance of roles. The value of roles and the interference that may

arise between work and family roles was explored among employed MBA students.

Vignettes varied the role salience and role pressure around two incompatible role

demands; an important work project and an important family birthday which were

scheduled for the same time on a weekend (Greenhaus & Powell, 2003). If work had

low role salience, the family activity was chosen regardless of the level of family

salience, whilst high work role salience meant that the work project would be chosen

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more often. Overall, the majority of people chose the family activity over the work

activity (Greenhaus & Powell, 2003). The use of vignettes may be somewhat

artificial as in the real world, scheduling is not likely to be immutable and an

individual‟s past experiences with choosing between work and family activities (and

the outcomes of those decisions) will influence how they perceive their current

choices. Role involvement, which is similar to role salience reduced married parents‟

distress (Frone et al., 1992a). Among married accountants who had low career

involvement, higher levels of work-family conflict increased both their intentions to

leave the profession and the likelihood of leaving. However, where they had high

career involvement, high levels of work-family conflict had the opposite effect of

lessening their intentions to leave (Greenhaus, Parasuraman, & Collins, 2001).

It would appear that career involvement colours the interpretation of work-

family conflict, such that higher involvement sees work as a less conflicted place,

whereas lower involvement would see work as more of the problem and where

leaving would remove that problem. In recent research on parents and non-parents in

a small national (US) firm, individuals with high family role salience maintained

their job satisfaction and held steady job distress as family concerns increased their

interference with work whilst individuals with low family salience had decreased job

satisfaction and increased job distress in the same situations. Also, higher family

salience buffered women from loss of job satisfaction in situations of greater family-

work interference (Bagger, Li, & Gutek, 2008).

However, mothers with a traditional gender role ideology experienced more

family distractions at work and for all mothers, greater workload lead to more

distractions at home (Cardenas, Major, & Bernas, 2004). Mothers also have lower

job satisfaction over time if their job is perceived to interfere with their home role.

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Although fathers did not have the same association between work and family role,

for both genders work-family conflict reduced job satisfaction (Grandey, Cordeiro, &

Crouter, 2005). Work-family conflict can arise where there are differences between

the individual‟s values and expectancies of their roles and the demands upon them

(Perrewé & Hochwarter, 2001). Among couples, being valued by a partner and also

being valued by one‟s employer led to greater motivation toward work and family

activities, less work-family conflict and less exhaustion (Senecal, Vallerand, &

Guay, 2001). When working for IBM overseas, having a spouse or partner reduced

the family-work conflict as being valued by their spouse or partner made their work

role easier (Hill, Yang, Hawkins, & Ferris, 2004). Role salience and ideology

provide a way for the individual to experience their work and family lives and will be

part of the current research.

1.4.9.3 Individual factors. The influence of the person on the work-life

interface is mostly measured by the facets of the five factor model, rather than

specific individual differences. For example, extraversion and neuroticism were

included in the study variables for the MIDUS study. Extraversion and older age

increased positive work-family spillover and decreased negative spillover (Grzywacz

& Butler, 2005). Additionally, personal growth a component of psychological well-

being (Ryff, 1989) was also associated with increased positive work-family

facilitation. Alongside the influence of personality, substantive complexity, job

demands and social skills associated with work increased positive work-family

spillover although the substantive complexity of work was also associated with

increased negative work-family spillover (Grzywacz & Butler, 2005). In a nationally

representative US study, positive personality traits (extraversion, agreeableness,

conscientiousness, and openness to experience) were associated with increased both

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types of positive spillover and less of both types of negative spillover. More of the

variance of spillover was accounted for by the personality variables than by gender,

work hours, marital status, parental status and education together. Neuroticism was

associated only with increased negative work-family and negative family-work

spillover (Wayne, Musisca, & Fleeson, 2004). Similar findings came from employed

Dutch fathers, where those fathers who were lower in emotional stability (i.e. higher

in neuroticism) experienced more burnout and depression when there were high

levels of work-family interference. Fathers who were lower in agreeableness also had

lower levels of marital satisfaction with increased levels of work-family interference

(Kinnunen et al., 2003). The general trends of the personality traits in reflected in

Indian research where men were more optimistic than women in managing conflict

between roles and therefore had greater positive work-family spillover than women.

However in India, the greater importance of the family role over the work role lead

to higher levels of family involvement reducing positive work-family spillover

among participants (Aryee et al., 2005).

1.4.9.4 Workplace and family factors. Work and family demands and

resources, as outlined in the previous section have similar effects on the interaction

between work and family domains. Among employed mothers, perceived

organization support lead to greater affective commitment, but it did not change the

interference between work and family activities (W.J. Casper et al., 2002). The work-

home culture of an organization can be construed as approving toward activities

outside work where there are high levels of support from the organization,

supervisors, and co-workers and little hindrance, in terms of negative career

consequences from using family-friendly policies or unreasonable time expectations.

Parents of young children in these approving workplaces used more of the available

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flexible work arrangements and as a consequence, experienced less time- and strain-

based work-home interference than employees in workplaces with less positive work

home cultures (Dikkers et al., 2004). Similarly, in a study comparing five

individualistic countries including Australia, greater perceptions that the organization

is family supportive reduced all facets of work interfering with home and home

interfering with home and increased job, family and life satisfaction. In addition,

interference between work and home was separately and negatively linked to job,

family and life satisfaction (Lapierre et al., 2008). When family demands interfered

with their work, job performance was reduced significantly more for private sector

employees who perceived low organizational support whilst there was little reduction

in job performance among employees who perceived high organizational support for

their concerns (Witt & Carlson, 2006).

Work load, measured as working hours and flexibility, can be increased by

being highly commited to work (e.g. checking emails from home, working on days

off and staying at work after normal business hours). Along with negative affectivity,

this personal initiative toward work however lead to increased job stress and work-

family conflict, over and above gender and marital status, although the effect of

personal initiative on work-family conflict is stronger for women than for men

(Bolino & Turnley, 2005). For child care workers and bus drivers, work-home

interference partially mediated the relationship between workload and depression and

health complaints whilst for medical residents and a larger heterogeneous group of

employees, the relationships was fully mediated (Geurts et al., 2003). A daily diary

study of the influence of daily workload on affect found that increasing workload

increased negative affect spilling over to the family domain, increasing work-family

conflict. The study found that it was not just the hours that the individuals worked,

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rather the chronic and specific workload on a given day that lead to increased

negative affect with the consequence that the individual withdrew from their family‟s

activities later in the day (Ilies, Schwind, Johnson, DeRue, & Ilgen, 2007).

When working hours alone were measured, longer hours increased negative

work-family spillover which led to increased psychological distress and greater

exhaustion among Finnish workers. Interestingly, working hours were not linked to

positive spillover in either direction, with positive work-family spillover decreasing

psychological distress and exhaustion (Kinnunen et al., 2006). Across blue and white

collar workers in the USA, longer hours and involuntary overtime were linked to

lower work-life balance. However, there was greater work-life balance where high-

performance practices were available. These practices included offering pay

appropriate for performance, an understanding supervisor, making job training and

childcare services available, intrinsically rewarding work and the individual feeling

strong affective commitment toward their workplace. These high-performance

practices provide challenging and rewarding work and skills that allowed workers to

better balance their work and family lives (Berg, Kallenberg, & Appelbaum, 2003).

As noted previously about the influence of types of jobs and spillover, for individuals

working in new media enterprises (e.g. internet companies or web design), self-

employment offered a way to achieve autonomy and flexibility with work to which

they were greatly attached and involved. The flexibility was used to organize their

work and family routines to suit the individual‟s needs (Perrons, 2003). However,

employed women who experience higher work-family conflict are more likely to be

absent from work, as well as leave early and leaving early is also more likely where

there is greater work-family conflict and a large kinship network (measured as close

relatives in the immediate community). Interestingly, though, having a larger kinship

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was associated with lower family-work conflict (Boyar, Maetz, & Pearson, 2005)

which would indicate that kinship provides support and benefits that reduces the

demands of the family domain.

Support can come from both domains. As such, support from the workplace

and from family members provides resources to the individual, such that support

within a domain (i.e. from supervisors or from family) reduces the problems

experienced within that domain. Supervisor support reduced exhaustion, work

overload and intention to leave which then mediated the link to work-home

interference and psychological distress. Similarly, support from home reduced both

marital distress and intention to leave the marriage, as well as overload in the home

which in turn mediated the relationship to family-work interference and then

affective symptoms (Brotheridge & Lee, 2005). However for this study, it is not clear

from the results what the relationship between family-work interference and affective

symptoms means, as it is not apparent what a high score indicates whether it is

greater positive or negative affect. More clearly shown are the results based on a

sample of Dutch and American workers, where negative work-family interference

partially mediated the relationship between psychological job demands and lack of

workplace social support and the outcomes of increased exhaustion and decreased

job satisfaction. However, job control when combined with increased social support

increased job satisfaction (Janssen, Peeters, De Jonge, Houkes, & Tummers, 2004).

Similar results were found in comparing the effect of work demands and home

demands on interference between these domains and the effect on burnout among

Dutch workers. Work-home interference partially mediated between work demands

and burnout whilst family-work interference partially mediated the relationship

between home demands (measured as quantitative, emotional or mental demands)

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and burnout. The experience of interference was different for men and women,

however with home demands having a greater impact on burnout for women and

work-home interference having a greater impact on burnout for men (Peeters,

Montgomery, Bakker, & Schaufeli, 2005).

Finally, the presence of children and elders requiring care can alter the work-

life interface. Eldercare is different to childcare and is managed differently. Eldercare

was more difficult and impacted on well-being and family performance when the

care was in the home, without a family climate supportive of that caring and where

there was no one to share the concerns of eldercare. On the other hand, caring for

children in the home did not affect family performance and increased well-being for

the carer (Kossek, Colquitt, & Noe, 2001). Responsibility for children and elders

increased the family-work conflict, which reduced work-family fit for IBM

employees, although schedule flexibility aided women more than men in this regard

(Hill et al., 2004). Among dual-earner couples, only the presence of preschool

children predicted negative family-work spillover whereas positive family-work

spillover was predicted by the individual being satisfied with the arrangement for

household tasks, a sense of family cohesion and contributing to their partner‟s career

success (Stevens, Minnotte, Mannon, & Kiger, 2007). The effect of young children

was also found in two large representative samples of American adults (i.e. the

Midlife Development in the United States (MIDUS) and National Study of Daily

Experiences (NSDE)). In these studies, children under 6 years increased negative

family-work spillover and decreased positive family-work spillover of their parent,

women experienced more negative spillover than men and interestingly, age had a

curvilinear relationship with negative spillover, stable in the early to middle years of

adulthood and decreasing to the later years (Grzywacz, Almeida, & McDonald,

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2002). Age was also important for understanding the relationship between work

conditions and sleep quality as increasing age, depression, poor health and a lack of

positive family-work spillover was associated with poorer sleep among female health

care workers (Williams, Franche, Ibrahim, Mustard, & Layton, 2006). In addition to

the benefits to sleep quality, positive spillover between the work and family domains

was also associated with good to excellent general health and mental health among

MIDUS participants (Grzywacz, 2000).

In a review of studies conducted up to 1998, Allen and colleagues (T. R.

Allen, Herst, Bruck, & Sutton, 2000) presented the weighted mean effects of work-

family conflict on a number of outcomes which ranged from smaller effects sizes

(e.g. job performance (-.12), increased alcohol consumption and family satisfaction

(-.17)) to medium effect sizes (e.g. burnout (.42), depression (.32), stress (.41) and

family distress (.31)) (J. Cohen, 1988). Work-life conflict reduced job satisfaction,

affective commitment and job performance and increased turnover intention while

reducing life satisfaction, marital satisfaction and family satisfaction. Further, work-

family conflict increased general psychological strain, burnout, stress and family

distress and was associated with poor health, depression and increased alcohol

consumption (T. R. Allen et al., 2000).

A subsequent meta-analyses of 61 studies by Byron (2005) calculated the

weighted average correlations between work (range ρ = .12 to .65) and family (ρ =

.12 to .49) antecedents and the outcomes of work-family and family-work conflict.

Greater work-family conflict was associated with longer working hours, less social

support at work, less schedule flexibility and more job stress (both as overall stress

and as role overload). Greater family-work conflict was associated with more of both

measures of job stress and lack of job social support and schedule flexibility. This

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brings together the findings of previous research, that work-related factors were more

associated with work-family conflict with family whereas family-related factors were

more associated with family-work conflict (Byron, 2005). Long hours of family

work, greater family stress and conflict and less family support, not having a partner,

having more children, both as a total number and those living at home and having

older children increased family-work conflict with most of these factors having

similar effects on work-family conflict. However, gender did not have a strong effect

with men and women experiencing similar levels of work-family conflict. Byron

considered only one individual difference variable but found that individuals with

more adaptive coping skills experienced less work-family and family-work conflict

(Byron, 2005). A narrative review of work and family research came to the same

conclusions as these reviews, with similar themes emerging from the research (Eby

et al., 2005). However, there was no statistical analysis of the results which limits the

comparison of findings across the reviews.

From these early studies and later research, it can be seen that

conflict/facilitation, spillover and interference between roles can mediate or

moderate, either fully or partially between workplace and family factors and well-

being, mental health and work outcomes. It is interesting to note that whether the

interaction is labelled a conflict, an interference or negative spillover, the outcomes

are similar. It could be concluded that demanding workplaces or families lead to

increasingly negative interactions and poorer outcomes whilst resource-rich

workplaces and families lead to increasingly positive interactions and better

outcomes. By measuring spillover (Grzywacz & Marks, 2000b), this general trend

can be the focus of the current research. As such, negative spillover represents the

distraction and tiredness from the problems between work and home whilst positive

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spillover then represents the benefits derived from the work and home roles that can

be applied elsewhere. Negative spillover therefore narrows the individual‟s focus

while positive spillover broadens their focus by measuring the positive affective

experiences. By using a more general measure, participants can apply the items to

their own situation without exactly specifying something that may not apply to them.

1.4.10 Exploring work-life balance and work-life fit

There is no widely recognised definition for work-life balance with the

concept treated as self-evident (Frone, 2003; Greenhaus, Collins, & Shaw, 2003),

vaguely (Hyman, Baldry, Scholaris, & Bunzel, 2003) or as something quite different,

such as considering that (the absence of) work-family conflict equals work-life

balance (White, Hill, McGovern, Mills, & Smeaton, 2003). The popular conception

is of busy people juggling many demands or specifically, busy parents juggling

demanding careers and their own and their children‟s many activities (Fouard &

Tinsley, 1997). Campbell Clark defined work-family balance as „satisfaction and

good functioning at work and at home, with a minimum of role conflict‟ (Campbell

& Campbell, 1995, p751). Frone (2003) defined work-life balance in terms of

spillover, as „low levels of interrole conflict and high levels of interrole facilitation‟

(Frone, 2003, p145) whilst Kirchmeyer considered balance as having sufficient

energy, time and commitment for satisfying experiences across all life domains

(Kirchmeyer, 2000). Voydanoff considered work-life balance as effectively

performing in both domains as there were sufficient work and family resources to

meet work and family demands (Voydanoff, 2005b). Voydanoff also defined work-

family fit as occurring when first, demands and abilities and second, needs and

supplies were matched between work to family (Voydanoff, 2005b) which is similar

to Barnet‟s conception of work-family fit as the ease with which individuals meet

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their work and family goals, given the demands upon them (Barnett, 1998).

Perhaps the most sensible conclusion comes from Greenhaus and colleagues

(Greenhaus et al., 2003) that work-life balance can be considered as a noun, as what

has been achieved, as a verb, as the daily balancing of various roles, or as an

adjective, as balanced or unbalanced. It is likely that work-life balance is more of a

verb, describing the daily proposition of managing and balancing different roles with

the quality of that balance being an adverb, as the success or satisfaction with these

daily activities rather than a constant state that a noun would imply or the adjective

describing that noun.

Work-life balance is measured in a number of ways. First, there is work-life

balance as the individual‟s satisfaction with their work-life balance (Clarke, Koch, &

Hill, 2004). This rating allows the individual to subjectively rate their own

experience of the relative levels of conflict and facilitation that they experience, in a

manner similar to Frone‟s and Campbell Clark‟s conceptions of work-life balance.

Second, following Kirchmeyer‟s definition, work-life balance can be taken as the

balance between the time, commitment and satisfaction for the work and family

domains (Greenhaus et al., 2003). However, the calculations for the time balance

requires the individual to estimate of hours spent in either role and this estimation

could be subject to biased recall. In addition, the formulae for calculating the

balances are quite complex. For work-life balance, Voydanoff measures both

resources and demands of the work, family and community domains (Voydanoff,

2004a, 2004b, 2005a) although these demands and resources are considered as how

they affect the separate components of conflict and facilitation between work to

family and family to work. Work-life fit can be measured as a scale (Barnett et al.,

1999) or as a single item (Clarke et al., 2004) and both are similar, in that the focus is

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on the ease or difficulty of the management of different roles.

To better understand the varying definitions of work-life balance and to

further explore work-life fit, the current thesis will measure spillover as the

directional and quality of the interaction (e.g. negative work-family spillover) in

relation to work-life balance and work-life fit. In this way, any overlap or points of

difference between the definitions can be identified, which will allow future research

to be more focused in its use of „work-life balance‟.

1.4.11 Conclusions of the Context of the work-life interface

By including factors of the work-life interface that are the individual‟s

assessments and perceptions of their working and personal lives, the current research

project will examine how these factors influence well-being over time rather than in

the usual cross-sectional relationships. Where work pressure, work-home

interference and burnout in Dutch workers are considered in a cross-lagged analysis,

work-home interference emerged as a cause and outcome of work pressure and

exhaustion, suggesting reciprocal relationships between the constructs in the nature

of a loss spiral involving interference, exhaustion and work pressure (Demerouti,

Bakker et al., 2004). The loss spiral contrasts with the „broaden and build‟ theory

which states that when an individual successfully manages stressors, they build a

repertoire of coping strategies to use in the future, therefore increasing their well-

being and positive affect, which can be used to deal with future stressors

(Fredrickson & Joiner, 2002). The current research will include both positive and

negative spillover of the work-life interface to examine causal influences on well-

being and it is expected that positive spillover will enhance functioning, whilst

negative spillover will be detrimental to functioning.

The benefits of multiple roles have come from understanding the facilitation

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and enrichment that multiple roles in the work-life interface (Frone, 2003; Grzywacz

& Butler, 2005). The role facilitation view factors in both the work and non-work

domains that are resources, such as the skills and experiences learnt in one domain

that assist in the other domains rather than only considering the demands upon the

individual (Grzywacz & Butler, 2005). Facilitation leads to role enhancement, where

performing multiple roles can have positive effects on psychological functioning for

both men and women (Barnett & Hyde, 2001; Galinsky et al., 2003; Geurts &

Demerouti, 2003; Milkie & Peltola, 1999). Multiple roles can contribute to well-

being through experiencing success that develops self-efficacy (Gowan et al.., 2000),

buffers difficulties in other roles (Gowan et al., 2000; Greenberger, O'Neil, & Nagel,

1994; Voydanoff & Donnelly, 1999) and decreases financial strains as the additional

income adds to the family‟s economic prosperity (Moen, 1992). Mutual support

within the relationship toward work and parental roles (Greenberger & O'Neil, 1990;

Marks, Huston, Johnson, & MacDermid, 2001) and the successful involvement in the

wider community (Barnett & Hyde, 2001) are additional benefits.

The view of role participation as enriching has recently been formalised as

the instrumental pathway as the resources developed in role A directly influencing

role B and the affective pathway as resources in role A directly and indirectly

influence role B through affect balance and role performance (Greenhaus & Powell,

2006). One of the benefits of this proposal is that personal factors are included in the

analysis as an integral part of the model. Combining with role salience and

commitment (Amatea et al., 1986), this perspective of enrichment allows for a deeper

and richer understanding of multiple roles to be gained. Enrichment, due to multiple

roles, may also explain the findings that executive men and women, „dual-centrics‟

who rated themselves as equally committed to both work and family roles. Dual-

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centrics were subjectively happier and objectively as successful at work as those

executives that were focused principally on work, „work-centric‟, or through their

home role, „family-centric‟ (Galinsky, 2003; Galinsky et al., 2003). The concept of

the dual-centric man or woman fits with role enhancement, with equal commitment

to multiple roles at work and at home can assist achieving work-life balance (Burke

& Nelson, 2001; Greenberger & Goldberg, 1989; Greenberger et al., 1994).

Working adults have many influences upon their well-being. The factors

within themselves, in their environment and the resultant interactions and processes

combine to form each individual‟s experiences. The partial and overall contributions

of each component of optimal functioning in different environmental contexts can be

assessed (Semmer, 2003).

The healthy workplace with a balance of resources and demands, defining

optimal functioning in addition to coping under pressure (Nelson & Simmons, 2003;

Turner, Barling, & Zacharatos, 2002), incorporating individual differences in

temperament, disposition (Frone, 2003) and humour (R. A. Martin et al., 2003) with

family (J. M. Patterson, 2002) and community resources (Voydanoff, 2004c) will be

defined for a broad but parsimonious research program. It is expected that from that

the resources available to the individual, through their workplace will also be part of

the significant influences on them. Specifically, having jobs to which they are

attached and that allow them to use their skills and abilities, are expected to be

associated with firstly, greater work engagement and less burnout and more generally

with better well-being and mental health.

1.5 T, the time frame over which multiple roles develop and occur

The current study will investigate the relationships between „subjects in

activities that require initiative and reciprocal interaction with their environment…

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on an everyday basis for an extended period‟ (Bronfenbrenner, 1995, p628). The

perceived balance in one‟s life is most likely a verb that describes the dynamic

processes of matching up the rhythms that occur daily, weekly, monthly, yearly and

across decades (Bronfenbrenner, 1995; Moen, Waismel-Manor, & Sweet, 2003). The

rhythms of business (such as the financial year, completing work projects and

meeting customer and competitive needs), career (developing, maintaining, and

winding down career aspirations), lifespan (forming and growing families and caring

for the aging) and individual growth and development (from adolescence to later

years) are intertwined to provide the dimensions of each person‟s life. In the same

way that depth and richness in music can appear from individual sound waves, each

of these rhythms or cycles come together to make the depth and complexity of

everyday life and have varying levels of salience depending on the age of the

individual.

Using the ecological framework can provide greater power to the study of

well-being and the work-life interface by accounting for all the influences on the

individual (Bronfenbrenner, 1995). There are three theoretical perspectives that will

be discussed as the influence of time on development. First, the Conservation of

Resources theory proposes that individuals will protect, maintain and increase the

resources that they value over their life, becoming distressed when these are lost,

threatened with loss, or not gained as expected. Second, life span theory describes

how resources are deployed across the life span to reflect how different lifestages

have differing requirements, and third, the life course perspective describes how

social roles are intertwined over a lifetime.

The Conservation of Resources (COR) theory (Hobfoll, 1989, 2001, 2002)

was proposed to understand the individual‟s response to stress. However, COR

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theory can also be applied to the life span, as it proposes that individuals are

motivated at all times to gain and protect things or resources that they value.

Resources are broadly defined as such things that are valued for themselves (e.g.

personal characteristics) or are valued as a way to obtain valued resources (e.g. social

support) and categorised as object resources, resourceful conditions (e.g. marriage,

job conditions), personal characteristics, energetic resources and social relationships

(Hobfoll, 1989, 2002). Stress and burnout occur when resources are lost, threatened

with loss or not gained as expected after investment of time and effort. As resources

are highly valued, resource losses become more salient than similar resources gains.

Of interest to the influence of time on individual development is the notion

that resources change over time, with loss and gain spirals of resources occurring.

Where individuals lack depth in their resources to cope with stressors and challenges

in their lives, they are more likely to suffer losses immediately and in the future as

there are few reserves for them to draw on, leading to depletion and a loss spiral of

their resources. However, where the individual has greater resources, they are more

capable of producing successful outcomes to difficult situations that will increase

their resources, leading to a gain spiral in resources. In essence, initial losses are

followed by later losses, whilst initial gains are followed by further gains (Hobfoll,

2001, 2002). The theory also proposes that resources occur together, as resource

caravans, such that individuals with greater optimism were more likely to have

greater self-efficacy, better social support and higher well-being, whereas fewer

personal resources are associated with greater stress and more losses over time

(Hobfoll, 2002). Resource caravans and positive and negative spirals that accumulate

over time are similar to the drift hypothesis of low socioeconomic status and drug

use (Fox, 1990; Miech, Caspi, Moffitt, Wright, & Silva, 1999) and the dynamic

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linkages between personal characteristics and well-being (Shmotkin, 2005). Also

relevant to this thesis is that individuals will seek to gain valuable resources when

they are not in stressful situations. By investing in themselves, their relationships and

circumstances, individuals can build up their resources and buffer themselves from

possible future losses (Hobfoll, 1989, 2002). In this way, individuals maximise their

gains and minimise their losses, and proactively address possible future problems

(Aspinwall & Taylor, 1997; Hobfoll, 2001). By including a mechanism for the

accumulation or degradation over time, Hobfoll‟s Conservation of Resource theory

can provide the method for demonstrating the reciprocal relationships that drive

development over time.

Life span theory (P. B. Baltes, Lindberger, & Staudinger, 2006) shows that

these different rhythms or phases can be understood and accomplished by the way in

which resources are allocated across the life span. In the years up to early adulthood,

the individual‟s resources are allocated to growth and building adaptive capacity in

areas such as education, careers, relationships and parenthood. As adulthood

progresses, resources are directed increasingly toward maintenance and resilience, by

managing challenges and recovery from losses to maintain adaptive functioning,

until old age when resources are necessary to regulate losses, when recovery or

maintenance is no longer possible. Particularly for older adults, using the process of

selection, optimization and compensation (SOC) for age-related changes and deficits

lead to successfully managing their aging abilities and lives (P. B. Baltes et al.,

2006). SOC is similar to self-regulation in that feedback loops allow monitoring of

progress, but SOC is different, in that it deals with losses, whereas self-regulation

explains how goals are pursued.

Taking the perspective of the life course can also add to the understanding of

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the rhythms in the individual‟s life. Life course theory (Elder & Shanahan, 2006)

examines how lives are socially organized, as the intertwined roles, cycles and the

influence of the individual‟s age. Life cycles capture the procession of the

generations, from the socialization of newborns to their maturity, who then give birth

to the next generation, before growing old and eventually dying. The roles within the

life cycle have normative expectations of behaviours and commitments, whilst the

stability of the roles and their relationships can add to personal stability and direction

in life (Elder & Shanahan, 2006). Whilst the focus on reproduction and parenting in

the life cycle does not account for those individuals who chose not to have children,

it can be used to account for family demands on the individual, with childless

individuals having limited family stressors compared to the parents of pre-school

aged children (Frone et al., 1992a). However, elder care is another dimension that is

not explicitly part of the definition of the life cycle. Age of the individual in the life

course can be viewed in several ways. First as the life time or chronological age,

second as the social time, which reflects the family time or normative sequences of

life stages, such as leaving home or having children, and third, the historical time,

which corresponds to Bronfenbrenner‟s chronosystem. Taken together, the life-

course theory sees development as the „interlocking lives and developmental

trajectories of family members who are influenced differentially by their changing

world‟ (Elder & Shanahan, 2006, p679). The life-course theory echoes

Bronfenbrenner‟s bioecological model, of the active participant, as a dynamic whole,

shaping and being shaped by their environment, across time.

The strength of using Bronfenbrenner‟s developmental equation as the

framework of the current research is that it acknowledges that development occurs

over time, that there will be reciprocal relationships between variables over time, and

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that developmental effects accumulate over time. Bringing together the perspectives

of resource use and allocation and roles over the life span can illustrate how these are

intertwined. Individual actions have consequences over time, so personal

characteristics will be reinforced over time and lead to the psychological outcomes

seen at a later age. Conservation of Resources shows how and why the individual

would take steps to improve their situation and prepare for the future, life span theory

shows that the way that the allocation of resources depends of the demands of the

particular life stage, whilst life course theory highlights the importance of the

individual‟s social context in how resources will be used or challenged. The most

positive developmental outcomes for the active participant will result from preparing

and accumulating resources that can be deployed in roles and situations that reinforce

and increase those resources. The end result would be successful negotiation of the

roles and responsibilities of the working adult, leading to greater well-being and

mental health and a stronger engagement in their work. In the longer term, such

activities will lead to greater psychological maturity, where at the end of their life,

mature adults are healthy and fit, have an alert and vital mind, are maintaining

meaningful roles in their lives either in a continuing vocation or in new activities, are

maintaining relationships with their family and friends and their involvement with

the community and finally, they are effective and wise problem-solvers

(Csikszentmihalyi & Rathunde, 1998). Whilst the current thesis will not explore the

longer term outcomes, it will explore the shorter term relationships between the

individual, their work-life context and their well-being, mental health and

engagement in work, taking into account resource gain and loss, life stage and social

roles.

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1.5.1 Longitudinal studies from a developmental perspective

There are many longitudinal studies from developmental psychology that

show how Bronfenbrenner‟s bioecological model can illustrate change over time and

serve as a lifespan perspective on the current research. An excellent illustration of the

dynamic interaction of the person and their environment from childhood to adulthood

is shown in an early study by Caspi, Bem and Elder (1989). Children from the

Berkeley Guidance Study (born in 1928) were followed up at 30 and 40 years of age.

At ages 8 to 10, children were rated by their parents as ill-tempered (based on

severity and frequency of temper tantrums), shy (based on ease and reserve shown

with social contacts) or dependent (based on intense need for parental approval and

need for constant attention) (Caspi et al., 1989). Ill-tempered boys became ill-

tempered men who were more likely to be in low status jobs as adults and with more

and significant instability in their work histories. For males, early ill-tempered

behaviour became reinforced over time by lack of educational qualifications which

lead in turn to lack of occupational status and an accumulation of maladaptive

choices, whilst a strong positive, direct link from ill-temper to job instability

indicated that there is an interactive element in the outcome. Whilst ill-tempered girls

did not have the work instability of ill-tempered boys, they married later, were more

likely to divorce and become ill-tempered mothers (Caspi et al., 1989).

Shy boys became shy men and were rated as aloof, without social poise and

uncomfortable with changes in any roles in their lives. They showed delays in their

entry into the labour market, married later and were more likely to be divorced which

would reflect the description of them as likely to withdraw when frustrated and

reluctant to act when needed. Interestingly, shy girls became quietly independent as

older women and did not have a delay in becoming married, perhaps because being

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shy, becoming employed was not as desirable to them as a family role. Given the

traditional gender roles of that time, shy women had an option to construct a

satisfying life course which included having ambitions for their husbands which was

not available to their male counterparts (Caspi et al., 1989). In the group of

dependent children, the positive outcomes are reversed for men and women.

Dependent men were more likely to marry, stay married and to have happier

marriages and also enter the workforce and become fathers on time. Unfortunately,

dependent girls became women who lacked independence, aspiration, assertiveness

and were more self-pitying. Whilst these women married and had children earlier

than other women, they did not continue their education in later life, narrowing their

options and limiting the direction of their lives (Caspi et al., 1989).

The dynamic interaction between the person and their environment is clear in

both the cumulative continuity, where the personality is in an environment that

directly reinforces personality behaviour over time and in the interactional

continuity, where personality provokes a response from the environment that then

reinforces the behaviour patterns of the personality, mutually strengthening the

adaptive or maladaptive aspects of the personality (Caspi et al., 1989).

Bronfenbrenner‟s conception of the active person can be seen in the long term

outcomes (positive and negative) of early personality. Poorer developmental

outcomes were seen as ill-tempered boys and girls become ill-tempered men and

women, shy men suffered as they did not conform to the role of active men and

dependent women did not fair well as they could not mature beyond their

dependence on others. Better developmental outcomes were seen as dependent boys

matured into nurturing, family men and shy girls matured into quiet women with

loving families. Social roles allow what may be problematic in childhood to become

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adaptive in adults because those traits are now valued (dependence matured into

nurturing in men and shyness matured into quiet support of others in women) but

poor behaviour is never valued (bad temper will always provoke other people).

Whether the same would be true today, where occupational opportunities are greater

for women and men are able to opt out of employment to care for children is a matter

for speculation. Ill-temper would still be maladaptive but it is less clear-cut for

shyness and dependence as normative role paths for men and women are more

diverse and less restrictive today than in the past.

The influence of personality over time is also seen in the Study of Adult

Development, which has been the longest continuous study of individuals across the

lifespan. The Study comprised three groups: male Harvard graduates first

interviewed in 1938 to 1942; inner city, non-delinquent boys (beginning at age 14 in

1940); and women who were part of the Terman study of gifted children, and studied

from the early 1920s to 2000 (Vaillant, 2002). By interviewing the participants from

their adolescence or early adulthood through to advanced old age, Vaillant concluded

that whilst temperament may not change over time, character does change in

response to environment and maturation. In this way, character was important to how

adversity and challenges were managed, such that the individual could mature and

outgrow early problems which would then not limit their opportunities for

development. For many participants, a happy marriage was the difference between a

dysfunctional childhood and successful old age. Across the three groups of

participants, the results showed that factors within the individual‟s control at age 50

rather than factors that were less able to be controlled, such as childhood

temperament, parental characteristics, cholesterol and stress were associated with

successful and healthy aging at age 75 to 80. Study participants who used adaptive or

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mature coping styles (e.g. humour) to deal with the challenges in their lives, who did

not smoke or drink to excess and had a stable marriage were considerably more

likely to be classified as Happy-Well, rather Sad-Sick in old age (Vaillant, 2002).

Other studies show similar findings. Cheerfulness when entering college was

linked to greater job satisfaction, income and less likelihood of unemployment 19

years later in both men and women (Diener, Nickerson, Lucas, & Sandvick, 2002),

whilst the Nun Study showed that positive emotional biographies written by young

women when entering the nunneries in the 1920s predicted longevity 80 years later

(Danner, Snowdon, & Friesen, 2001). In the Berlin Aging Study, the application of

adaptive coping strategies known as Selection, Optimization and Compensation

allowed older adults to manage the loss of skills and resources that occurred with

age, with greater use of theses adaptive strategies predicting happier old age and

greater wisdom (P. B. Baltes, 1997). From the National Longitudinal Surveys of

Youth, individuals who had higher self-evaluations of control, self-esteem and self-

efficacy and who had less neuroticism started their working lives with more

education and job satisfaction, better pay and higher status jobs. After 25 years, these

individuals had increased their advantage over individuals with lower self-

evaluations whilst the individuals with low self-evaluations had increasingly more

health problems that interfered with their ability to work (Judge & Hurst, 2008).

Over the length of a person‟s life, their personal characteristics are a strong influence

on their well-being and mental health and also provide a strong influence on their

children‟s well-being and mental health.

Development of the individual also has influences on their children‟s

development. Women college graduates were followed from their 20s through to

their 50s to understand their development (Roberts, Helson, & Klohen, 2002) and the

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development of their adult children (Solomon, 2000). The rank order for the

women‟s of personalities was stable over time, with all women having better

psychological adjustment and being psychologically more complex in their 50s than

in their 20s. The women were also more dominant and less feminine than in their

earlier years (particularly at age 27) when gender differences were accentuated by

parental responsibilities. Later increases in dominance and reductions in femininity

were considered to reflect increasing involvement in work and the adult world that

followed the women‟s movement in the 1960s (Roberts et al., 2002). The outcomes

of their children were assessed when the women were aged 60. Linking the earlier

assessment of functioning at age 43 (when the children were adolescents) to the adult

children‟s functioning seventeen years later found that the mothers who were rated

by others as socially perceptive, empathetic, cheerful, humourous and able to see

problems clearly were the mothers of the best adjusted adult children whilst women

with poor interpersonal skills had children who were less well adjusted. Family

integration and cohesion, rather than marital satisfaction was significantly related to

child adjustment with the personalities of both mothers and fathers contributing to

the adult children‟s development (Solomon, 2000).

In another study of mothers and their children, Moen and Erikson (1995)

studied mothers first in 1956 and again in 1986 and studied their adult daughters in

1988. Whilst it may be difficult to untangle all the developmental influences over

time, analyses found several interlinked processes. First, the mothers‟ earlier

psychological and social resources (measured as mastery and social roles,

respectively) and modelling of coping with difficult situations promoted resilience in

their daughters in later life. Second, the daughters‟ experiences and active

participation in life directly built their own sense of mastery and could be used to

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overcome their mothers‟ limited social resources. Third, the increased occupational

opportunities available to the daughters to be upwardly mobile provided another

pathway to positive outcomes, as daughters were not bound by their mothers‟ limited

options of the past (Moen & Erickson, 1995). These studies illustrate the role of early

childhood experiences, as the observer of parental behaviour, combined with adult

experiences as the direct instigator of one‟s own behaviour and the changing cultural

expectations of normative roles that open new pathways to shape personal and

occupational development over time.

From these studies of the developmental outcomes, the importance of the

individual to how competently an individual functions over many years can be seen.

For example, in the Study of Adult Development, there was little difference between

the men between 25 and 45 years but after that time, the life paths of the men

diverged substantially. The individuals who had used negative explanatory styles as

young men had poorer physical and mental health, less satisfying relationships and

were more likely to have died at an earlier age than their more positive classmates

(Peterson et al., 1988). The positive personal characteristics that Bronfenbrenner

used to describe the active participant have been demonstrated in these diverse

studies as the driving force for competent development of the individual and for their

children. Such positive characteristics form resource caravans (Hobfoll, 2002) that

exist and mature together over time, acting to reinforce each other. Whilst the

longitudinal framework proposed for this thesis is much shorter than these studies,

being measured in months rather than years, it is expected that the longitudinal

modelling will show the underlying mechanisms by which these processes occur.

The framework for the modelling will be explored in the next section, based on the

four non-nested models that will be described there.

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1.5.2 Longitudinal studies from an organizational perspective

There has been considerable interest in the longitudinal effects of the

workplace and work-family interference and spillover on individual outcomes. In an

early study of Bell Telephone managers over 20 years (Howard, 1992), increasing

complexity at work provided opportunities for the men to develop their skills and

self-confidence which further increased their involvement in more challenging work

tasks. Similarly, whilst family responsibility increased family stress over time, that

responsibility lead to greater family involvement and later greater family satisfaction

in later life (Howard, 1992). There are many factors, as outlined in the previous

sections on the context of the work-life interface that can be included in the models.

The majority of recent European studies have used SEM to test their hypotheses and

in particular, a set of non-nested models to test causality within the models which

will be the focus of the analytic strategy of the longitudinal models for this thesis.

There is mixed evidence for reciprocal relationships between variables over time,

from support (for example, Demerouti, Bakker et al., 2004; Kelloway, Gottlieb, &

Barham, 1999)) to finding only causality and no evidence (for example, van Hooff et

al., 2005) of reciprocal pathways.

The four non-nested models used in SEM to test reciprocal relationships were

first demonstrated in research by de Jonge and colleagues (de Jonge et al., 2001) and

then by a number of other researchers (for example, Dikkers et al., 2004; ter Doest &

de Jonge, 2006; van Hooff et al., 2005). The set of longitudinal models provide a

method to simultaneously compare alternative models that may provide

understanding of the developmental process. The models are compared as first, the

base or stability model (synchronous correlations and auto-lagged (i.e. within

variable pathways over time), second, the causality model (stability model plus

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cross-lagged, i.e. between variable, paths from predictor variables to outcome

variables over time), third, the reverse causality model (stability plus cross-lagged

paths from outcome variables to predictor variables over time) and fourth, to the

reciprocal model (stability plus causality plus reverse causality). This allows for the

relative importance of each path to be examined, such that the influential, significant

paths over time can be understood. These models also have the advantage that all

variables are measured at each time period, separating the stability of variables from

any changes between variables that may occur over time.

In de Jonge and colleagues‟ initial study, Dutch health care workers in

hospitals and nursing homes were studied 12 months apart. The causality model was

the best fitting model, whilst the reverse causality and reciprocal models were no

better than the stability model at explaining the data. From Time 1 to Time 2, there

was a robust stability within the demands, autonomy and social support at work over

time and moderate stability within job satisfaction and motivation and exhaustion.

From the cross-lagged paths, job demands decreased and job social support increased

job satisfaction at the later time (de Jonge et al., 2001). This study was replicated

among Dutch residential health care workers over 12 months, with the causality

model again providing the best fit and parsimony for the data. Compared to the

stability model, the reverse causality model was not better fitting and although the

reciprocal model did improve fit, it was not better fitting. In these participants, social

support at work at Time 1 increased job satisfaction whilst decreasing exhaustion at

Time 2, with moderately strong stability within all variables over time (ter Doest &

de Jonge, 2006). The causality model was also found to provide the best fit among

Dutch police officers over 12 months, with strain-based work-home interference

increasing the fatigue and depressive symptoms at the later time. Interestingly, the

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reciprocal model had been the best fitting but as not used for the reason that the

additional paths were non-significant, indicating that best fit had been achieved by

overfitting the reciprocal model to the data (van Hooff et al., 2005).

The cross-lagged paths between the variables over time can indicate how

resources can be lost or gained over time (Hobfoll, 1989, 2001, 2002). In a three

wave panel study, Demerouti and colleagues (Demerouti, Bakker et al., 2004)

followed Dutch employment agency employees at six week intervals to assess the

effect of work pressure on work-home interference and exhaustion over time. Across

this shorter time period, the reciprocal model provided the best fit (Demerouti,

Bakker et al., 2004) rather than the causality model over longer time periods used in

the studies above. With the reciprocal paths, each variable had robust stability over

time and work pressure, work-home interference and exhaustion at Time 1

influenced the other variables at Time 2 and at Time 3 which indicates that poor

situations and outcomes reinforce each other over time. In this way, these loss spirals

were shown, first where work pressure (at time 1) increased burnout (at time 2)

which lead to greater work pressure (at time3) demands at an earlier time (e.g. work

pressure). Second, work-home interference (at time 1) increased work pressure (at

time2) which increased exhaustion (at time 3) (Demerouti, Bakker et al., 2004).

Positive gain spirals have also been shown, with the reciprocal models best

explaining the relationships between task resources, efficacy beliefs and work

engagement (Llorens et al., 2007). When Spanish students had sufficient task

resources (at time 1), they had increased self-efficacy (at time 2), their self-efficacy

(at time 1) increased both (perceived) task resources and work engagement (at time

2) and their work engagement (at time 1) increased their self-efficacy (at time 2)

(Llorens et al., 2007). From these paths, positive situations and outcomes reinforce

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each other, representing a gain in their overall resources. However, this study used a

smaller sample size and a short time period (three weeks) which raises questions

about generalisability. When personal and organizational resources and flow of

Spanish teachers were modelled over 8 months, the reciprocal model was also the

best fitting of the four models. Personal and organizational resources had a

moderately strong positive effect on the teachers‟ flow whilst flow had a moderately

strong effect on the perception of both resources at the later time (Salanova, Bakker,

& Llorens, 2006). When a longer time frame of 3 years was used to study Finnish

dentists, the reciprocal model was again the best fitting, as job resources and work

engagement and separately work engagement and personal initiative, providing

mutual reinforcement over time (Hakanen, Perhoniemi, & Toppinen-Tanner, 2008).

These three studies, with varying time lags show that positive situations and

resources show evidence of resource accumulation, unlike the Dutch studies where

causality models were supported. It may be that the Dutch studies, by including

measures of the negative aspects and outcomes of the workplace are capturing a

broader picture of the developmental process.

In other analyses using hierarchical multiple regression and hierarchical

linear modelling, the influences of workplace factors and individual factors can be

assessed more specifically. Among Finnish health care workers, the effect of job

demands and resources on work engagement was followed over 2 years (Mauno,

Kinnunen, & Ruokolainen, 2007). From the hierarchical multiple regression

analyses, it is interesting to note that their engagement in work was strongly

predicted by how engaged they were when initially surveyed over and above

demographics and job demands, resources and work structures. This highlights the

importance of measuring each variable at every time to understand the underlying

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level of a variable. For example, absorption at Time 2 was predicted by gender,

education, time demands, job control and self-esteem but these became non-

significant when the Time 1 absorption score is added. Similarly with dedication and

work vigour, where the Time 1 scores are the main predictor of the variables at Time

2, as only the presence of children remained as a predictor of work vigour and for

dedication, children, type of job contract, job security and job control remained as

significant predictors at time 2 (Mauno et al., 2007). Among Dutch police, multiple

regressions showed that reciprocal relationships existed between workload and work-

home interference over one year and again, the strongest predictor of the variables at

Time 2 were the variables at Time 1 (Dikkers et al., 2004). Hierarchical linear

modelling was used to show that for dual earner couples, changes in psychological

distress were associated with having dull and monotonous work, measured as time

pressure and role conflict increased anxiety and depression, regardless of gender,

over a period of two years (Barnett & Brennan, 1997). Among Canadian employees,

there were reciprocal significant paths between time and strain based work-family

and family-work conflict and the employees‟ stress and turnover intentions six

months later. The strongest paths were between the measures of strain across time

(Kelloway et al., 1999).

Both the longitudinal modelling and the regression analyses over time

indicate that there are effects over time between variables, whether limited to

causality, as the usual stressor-strain paths from predictor variables to outcomes or

more broadly as reciprocity, such that the longitudinal model captures the ongoing

interaction between person, context and outcomes. Among German workers, a

feedback loop involving depression, work-home interference and workplace stressors

provided the best fit of a series of models that tested lagged and synchronous effects.

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For these employees, work-home interference affected their well-being in the short

and longer term (Steinmetz, Frese, & Schmidt, 2008). Reciprocal relationships

between variables would fit with Bronfenbrenner‟s developmental equation where

the individual‟s current development forms the basis for ongoing, later development.

Using the set of four models as outlined above will provide the opportunity to test

these relationships.

It is interesting to note that there are strong links between the same variables

over time, suggesting that there is substantial stability in these constructs over time.

For measures of work conditions, it is perhaps not surprising that these should be

consistent as the individual‟s job description to be (reasonably or mostly) unchanged

between measurement times. For individual differences and well-being variables, a

degree of stability over time should also be expected as longitudinal studies have

shown that personality can be stable over time (for example, Roberts et al., 2002) and

the set point for well-being shows modest stability (Fujita & Diener, 2005) and

adaptation to life events (Lucas, Clark, Georgellis, & Diener, 2003) over long

periods of time. It also makes more logical sense to have causal paths linking the

variable at Time 1 and Time 2, rather than a correlation as a correlation implies that

causal attribution is bidirectional. It is difficult to understand how a later time can

have a causal influence on the variable at an earlier time, for example Demerouti at

al (2004) call this „temporal stability‟ but recent research now has the Time 1

variable predicting the Time 2 variable (Hakanen et al., 2008). Positive influences

represent gains in resources whilst negative influences would led to losses in

resources, which Hobfoll refers to as gain spirals and loss spirals respectively

(Hobfoll, 1989, 2002). The drift hypothesis represents a similar situation of the

downward spiral associated with poverty or low socioeconomic status and poor

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mental health. Among adolescents in New Zealand, growing up in poor

neighbourhoods and in families without education or high incomes provided limited

resources for them to overcome internalizing and externalizing mental illnesses.

Adolescents with conduct disorders were more likely to have problems with

educational transitions and not to complete their school studies which entrenched

their disadvantage as adults as their job options were limited by poor school

performance (Miech et al., 1999). Understanding the loss and gain spirals will add to

the understanding of how development occurs across time.

However in these studies, the individual is not well represented, often being

taken as age, gender and negative affectivity which are narrow conceptions of the

individual that does not provide meaningful information how the individual may

influence the interaction between workplace and well-being. In a study of bank

employees, Houkes and colleagues (Houkes, Janssen, de Jonge, & Bakker, 2003) did

include „growth need strength‟ and „upward striving‟ as individual difference

variables in some of the analyses but their results are limited by not including the

individual differences in the main analyses of the four models to test reciprocity.

Including more widely used measures of individual difference or personality and

including those variables in the models would allow the person to be included in a

realistic and meaningful way.

1.5.3 Conclusions for Time in the developmental equation

From the results of many studies in many diverse populations, competent

adaptive development across the lifespan is linked to the active and psychologically

mature individual who shapes their environment to achieve a happy and meaningful

life. Whether the maturity is called resilience, the good life or successful aging, the

best lives are flexible and exhibit self-regulation and positive relationships with their

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families and friends. This maturity also implies that competent development will take

into account that the individual will maximize the gains of resources whilst

minimizing any losses that occur across the lifespan (P. B. Baltes, 1997; Hobfoll,

2002). The outcomes that will be considered for this thesis are well-being, mental

health (taken as the absence of mental illness), burnout and engagement with work,

as work provides as central role in the lives of all adults. It is expected that the

influence of time will be shown in the longitudinal models as the presence of

stability of variables over time, in addition to the influence of reciprocal influences

between the variables over time.

1.6 Proposed research program

Bronfenbrenner‟s developmental equation provides the framework to specify

the Person and the Contexts in which working adults‟ psychological development

can be explored. The purpose of the current research is to identify the important

factors that lead to competent development such that future psychological and

workplace interventions can be better informed and targeted. The proposed research

program will have two studies that separate and explore the influences of the person

(as their generative disposition, gender and demand characteristics) and their work-

life context (as workplace and family factors and spillover between roles). Using

quantitative methods, the person and their work-life context can be understood in

detail and the most influential factors for the outcomes can be identified.

1.6.2 Study 1

Study 1 will have a cross-sectional analysis of the developmental equation,

using hierarchical multiple regression analyses (HMR) for each of the developmental

outcomes, of well-being, mental illness, burnout and work engagement. Predictor

variables will be the variables described in the literature review in this chapter: as the

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generative disposition, the individual‟s demand characteristics, workplace and family

factors, and spillover between work and family. Outcome variables will cover a

broad spectrum of psychological functioning.

The Person will be measured as first, the generative disposition, as

dispositional optimism, coping self-efficacy, perceived control of time, roles and

gender; and second, as the demand characteristics, as humour and social skills. The

Context will be measured by two blocks of variables: Work and Family variables of

working hours, affective commitment, skill discretion, job autonomy, job social

support, type of employment, family demands, number of children, marital status;

and as the Work-Family Interface variables of spillover (positive and negative)

between work and family, work-life balance and work-life fit. The developmental

outcomes will be Well-being, measured as life satisfaction and psychological well-

being, Mental Illness, measured as depression, anxiety and stress, Burnout, measured

as exhaustion, cynicism and professional efficacy, and Work Engagement, measured

as work vigour, work dedication and work absorption.

Hypothesis for Study 1: It is expected that aspects of the Person and Context

components of Bronfenbrenner‟s developmental equation will be significant

predictors of the developmental outcomes, measured as well-being, mental illness,

burnout, and work engagement. It is expected that individuals who have higher levels

of the generative disposition, more positive demand characteristics and greater

workplace resources will have more positive functioning. Specifically, for P, the

person, better outcomes were expected to be predicted by more dispositional

optimism, greater coping self-efficacy, more humour and better social skills. For C,

the context, more workplace resources and more positive spillover between roles and

less negative spillover between roles were expected to be associated with better

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outcomes.

1.6.3 Study 2

Study 2 will involve building longitudinal models, using structural equation

modelling (SEM) and will be conducted using a prospective panel study that extends

from the cross-sectional data of Study 1. The longitudinal analyses will have three

time measurements and allow the combination of the developmental outcomes to

achieve a broad understanding of the reciprocal relationships to drive development

over time, as evidenced by the gain or loss of resources. However, as including many

variables in SEM can lead to problems in the quality of the longitudinal analyses, the

most frequent predictors of the HMR in Study 1 will be identified and used in the

longitudinal modelling. The person and their work-life context will defined in this

way and combined with the outcome measures to be tested by a set of non-nested

models. The additional step of removing trivial pathways from the longitudinal

models will extend previous research and more enable influential pathways to be

more clearly identified, showing where gains and losses of resource occur.

Hypothesis for Study 2. It is hypothesized that the longitudinal modelling will

show evidence that there is stability in the variables over time and that there are

changes in variables over time which will be the result of gain and loss spirals of

resources. Gain and loss spirals are evident in the significant reciprocal relationships

between variables over the measurement times. Specifically, it is expected that the

greatest influence on a variable at a later time will be from the same variable at the

previous measurement times (i.e. the auto-lagged paths), which will be taken as the

stability of a variable over time. In addition to the stability of variables, it is expected

that there will be smaller but important contribution from cross-lagged paths, such

that personal and workplace resources will increase positive functioning over time

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and that the demands of negative spillover will increase burnout and mental illnesses

over time. These cross-lagged paths will represent the gain and loss spirals that lead

to the accumulation or loss of resources over time.

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Chapter 2, Study 1: Using hierarchical multiple regressions to explore the predictors

of well-being, mental illness, burnout and work engagement of working adults

This chapter will investigate the cross-sectional relationships between the

individual, their work and family roles and their well-being, mental health, work

engagement and burnout for Study 1. The analysis will be based on the framework of

Bronfenbrenner‟s developmental equation, D ∫ PPCT (Bronfenbrenner & Morris,

1998), where development, D, is the function of the proximal processes, P, between

the person, P, and their context, C, over time, T. The person and the context will be

examined in the following research, with the proximal processes implied from these

variables and time to be included in Study 2.

The theoretical and empirical research that supports these components has

been outlined in Chapter 1. In summary, D, the developmental outcomes are Well-

being, measured as life satisfaction (Diener et al., 1985) and psychological well-

being (Ryff, 1989), Mental Illness, measured as depression, anxiety and stress (S. H.

Lovibond & P. F. Lovibond, 1995), Burnout, measured as exhaustion, cynicism and

professional efficacy (Maslach et al., 1996), and Work Engagement, measured as

work vigour, work dedication and work absorption (Schaufeli et al., 2002). P, the

person, will be measured by first, the generative disposition, as dispositional

optimism (Scheier et al., 1994), coping self-efficacy (Chesney et al., 2003), control

of time (Macan, 1994), role salience (Amatea et al., 1986) and egalitarian gender role

attitudes (Moen, 2003). The person will also be measured by their demand

characteristics, measured as humour (as a coping strategy, R. A. Martin & Lefcourt,

1983) and social skills (Ferris et al., 2001). C, the context of workplace and family

factors and the spillover between work and family will be measured by working

hours, affective commitment (N. J. Allen & Meyer, 1990), skill discretion (Schwartz,

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Pieper, & Karasek, 1988), job autonomy (Voydanoff, 2004c), job social support (van

Ypern & Hagedoorn, 2003), type of employment, family demands (Frone et al.,

1992a, 1992b), number of children, marital status, and spillover (positive and

negative) between work and family (Grzywacz & Marks, 2000b). Consideration of

Time will be given in the longitudinal analyses to follow in Chapter 3 in Study 2.

This chapter will investigate the cross-sectional relationships between the

individual and their environmental context and their effect on the developmental

outcomes, providing the platform for the longitudinal modelling in the second part of

the study. Both the personal characteristics, which are resources that the individual

can use in challenging times and workplace resources, such as skill discretion are

considered likely to be important to the outcomes of working adults (Hobfoll, 2002;

Voydanoff, 2005b). These analyses will explore the relative influence of the active

participant (P, the Person) and a supportive context (C, the Context), arising from

work and family conditions and spillover of the work-family interface to understand

the predictors of D, the developmental outcomes, well-being, mental health, burnout

and work engagement.

2.1.1 Hypothesis for Study 1.

It is expected that aspects of the Person and Context components of

Bronfenbrenner‟s developmental equation will be significant predictors of the

developmental outcomes, measured as well-being, mental illness, burnout, and work

engagement. It is expected that individuals who have higher levels of the generative

disposition, more positive demand characteristics and greater workplace resources

will have more positive functioning. Specifically, for P, the person, better outcomes

were expected to be predicted by more dispositional optimism, greater coping self-

efficacy, more humour and better social skills. For C, the context, more workplace

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resources and more positive spillover between roles and less negative spillover

between roles were expected to be associated with better outcomes.

2.2 Methods

2.2.1 Participants

2.2.1.1 Recruitment. Volunteers were recruited from the alumni of a

university and from the administrative staff of a large public hospital. The university

alumni were contacted through the alumni‟s monthly e-magazine, with the first

alphabetical half of the alumni‟s email list (N = 9000) being targeted. This email list

is arranged alphabetically by first letter, for example, as [email protected],

[email protected], [email protected], ensuring a random selection of alumni.

The article appeared in the alumni e-magazines sent out in late August and late

September 2006, with 207 members of the alumni volunteering for the survey. The

response rate for these two calls to volunteer was 2.22%.

The administrative staff (N = 450) at the public hospital were contacted

through their managers and invited to take part in the research project. The hospital

made specific arrangements for staff to have access to the SurveyMonkey website

from their computers at work rather than having to rely on their home computers. 10

and 20 days after the initial contact, the staff were again emailed asking for them to

volunteer, with about half the staff taking part (n = 268, 59.6% response rate).

The substantially better recruitment at the public hospital highlights the

problems of internet research. As will be explored in the internet survey

methodology, it is necessary for calls to action to be vouched for by a trusted or

reputable third party (e.g. immediate supervisor, rather than bulk email) and it is

necessary for the calls to action to be close in timing (T. Anderson & Kanuka, 2003;

Hewson, Yule, Laurent, & Vogel, 2003). The alumni association was however

mailto:[email protected]



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limited in its ability to send more frequent reminders of the survey, as the size of

their mailing list meant that it is more likely that the e-magazine would trigger spam

protocols for the recipients‟ Internet Service Providers, leading to the emails not

being received as intended. Most participants (90%) completed the surveys within

the first day of receiving the emails asking for action, perhaps indicating that once an

email is off the page, it disappears out of conscious thought. The emails sent to

participants to initially asking for volunteers and the emails sent at Times 2 and 3

asking for involvement in the second and third waves of data collection. Future

research should be based on emails sent by known person, at 10 day intervals, with

three calls to action in total.

2.2.2 Internet survey development

The surveys were hosted by SurveyMonkey (www.surveymonkey.com), an

online survey tool which is based in the USA and which meets the European Union‟s

Safe Harbor convention for privacy of digital information. Access to the data of the

surveys was through a username and password which was further protection of the

participants‟ data.

After the scales to be used were collated in a word document, the type of

formats that were appropriate for each scale or item was decided. For example, the

choice may be between scales that used on a Likert rating scale („Matrix of choices

(only one answer per row allowed)‟) or for an item that required an open ended

question („Single textbox‟). A multi-item scale such as the Life Orientation Test-

Revised (Scheier et al., 1994) using a Likert rating scale would be constructed in

three steps. First, the stem question (“Please indicate how much you agree or

disagree with the following statements”) is entered, second, the items of the scale are

entered on separate lines and each item is numbered and third, the Likert rating scale

http://www.surveymonkey.com/

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is entered on separate lines (for example, 1 strongly disagree, 2 slightly disagree, 3

neither agree nor disagree, 4, slightly agree and 5 strongly agree). For items such as

ages or hours worked each week, a simple text box was sufficient. Using the

templates available within SurveyMonkey, the survey was constructed by inserting

the appropriate question into the desired template, eventually building to the

complete survey. The survey was checked for typos, spelling mistakes,

inconsistencies in numbering or wording and the time taken to complete the survey

was estimated. As a further check, the link to the survey was emailed to supervisors

and colleagues and the correction process was repeated several times until the survey

was deemed correct.

Once the survey was checked as satisfactory, the original survey was

duplicated within SurveyMonkey to create another identical survey. Data from the

two participant pools could be separately collected and identified to calculate the

response rates. Each of the surveys has a unique URL (for example,

http://www.surveymonkey.com/s.asp?u=111873790210 for the alumni participants at

Time1 and http://www.surveymonkey.com/s.asp?u=439312440696 for the hospital

participants at Time 1). As a survey is duplicated, a new URL is generated for that

new survey. As the longitudinal analyses used the same variables at each time period,

the Time 2 and Time 3 surveys were duplicates of the Time 1 surveys, to ensure

consistency over time.

2.2.3 Internet survey methodology

The current research will used a newer method of data collection for the

cross-sectional and longitudinal analysis. Volunteers will be recruited through the

internet to ensure the largest possible sample for the planned analyses, particularly

the structural equation modelling in Study 2. Although internet surveys offer the

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promise of obtaining large, diverse samples at low cost, reasonably quickly and

relatively easily when compared to more traditional methods, such as postal surveys

(T. Anderson & Kanuka, 2003), it is important to consider if a survey conducted

through the internet would reach sufficient individuals to warrant the use of this

method. The numbers of internet users continues to increase in Australia and across

the world, as individuals go online to socialise, to purchase products and services, to

do their banking and to organise their travel arrangements (Hewson et al., 2003).

From the 2006 Australian census data, 64% of households in Queensland, and 63%

of Australian households have access to the internet, with similar rates of access in

major cities (64%) and regional areas (59%) close to the cities (Australian Bureau of

Statistics, 2006b). Internet use occurred mostly at home, and mostly for private or

personal use, educational purposes or for business purposes among highly educated

individuals or high income earners. Most people using the internet daily (50%) or at

least weekly (41%) (Australian Bureau of Statistics, 2007). Therefore, access to the

internet at work or at home, demographics or familiarity with the internet should not

be a barrier to obtaining a wide variety of participants for the proposed study.

It was important to consider whether the data collected from internet surveys

was valid when compared to the more traditional pen and paper surveys. There were

several issues that may influence the data generated by an online survey. First, would

the scales produce the same outcomes in either format? Two recent studies indicated

that format was not as important as could be expected (Gosling, Varize, Srivastava,

& John, 2004). Among university students tested on the Occupational Personality

Questionnaire, most of the subscales in particular for conscientiousness, had

comparable results between the two formats. Being able to choose the format led to

some differences but the authors proposed that this was due more to the scales

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involved than the format per se (Meade, Michels, & Lautenschlanger, 2007).

However, no differences were found when employees of a large multi-national

company took part in a large scale comparison of paper and pen questionnaires and

on-line surveys. The two survey formats yielded similar results for the assessment of

the organizational climate across 16 international workplaces, indicating that results

both formats could be combined in future (De Beuckelaer & Lievens, 2009). For the

purposes of the present research, this also indicated that internet research would give

similar outcomes to the traditional pen and paper surveys.

The second concern was that the samples from the internet would not be

representative of the population in general. Recruitment of general surveys through

the Web found that there was greater variation in age and education, both being

greater, than in samples taken from undergraduate students (Birnbaum, 2004). In a

comparison of participants in web-based surveys and the participants of articles in

the Journal of Personality and Social Psychology in 2002, participants of the Web

surveys had a better balance between the genders as more men participated, came

from a broad range of socio-economic backgrounds and were somewhat older,

although the racial disparity found in traditional research was also found on the web

(Gosling et al., 2004). As the current research is concerned with working adults,

being able to have a sample with older participants and with a better gender balance

will enhance the representativeness of the sample to be used.

The third concern for internet research is maximising the response rate in the

survey. Calls to volunteer for the current research were sent to specific email lists,

either through an alumni e-magazine or through hospital staff list, rather than be

generally available on a website, which allowed known groups to be contacted.

General web surveys can generate large, diverse samples on web sites with

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substantial traffic and interest (Park, Peterson, & Seligman, 2004) but surveys can

languish where there is no way to direct potential participants to the website or

survey (T. Anderson & Kanuka, 2003). Calls for volunteers in emails generally lead

to higher response rates than to general web-based surveys (Hewson et al., 2003).

The rate of responses were increased further in several ways: first, that the request

for volunteers was sent by a third party who was known and trusted by the potential

volunteers; second, the calls to volunteer were sufficiently urgent, without being

overly persistent; third, the participants see the research as worthwhile and

rewarding; and fourth, that the participants were assured their answers were

confidential and anonymous (T. Anderson & Kanuka, 2003; Hewson et al., 2003). In

this way, the email requests were seen as a worthwhile activity that was more likely

to be acted on, which increased the response rate for the survey.

The final concerns related to the administration of the surveys. The problem

of multiple submissions, where individuals complete the surveys more than once,

were overcome by suitable configuration on the on-line survey (Birnbaum, 2004). As

the current study involved longitudinal data collection, the issue of correctly

identifying the participant over time must be considered to reduce possible attrition

of participants (Hewson et al., 2003). A simple code based on initials and birth date

was used in the current research to overcome this concern. The online format of

surveys did not negate the ethical considerations of protecting participants by

maintaining their anonymity and respecting their confidentially (Kraut et al., 2004).

Choosing a survey provider that met international standards and that did not retain

information on the individuals who participate was a crucial first step in this process.

The second step was to safeguard the participants‟ confidentially after they complete

the surveys. For the current research, any identifying information such as the

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person‟s individual code was stored separately to the email addresses needed to call

for participation in the later waves of data collection. By addressing these concerns

through careful design and administration of the survey, the benefits of online

surveys can be gained and a large, diverse sample of working adults can be obtained.

2.2.4 Measures

2.2.4.1 Demographics. To identify participants across time, whilst retaining

their anonymity, a code was developed from their initials and their date of birth, for

example, EB160569. Gender was coded as 0, male and 1, female. Age was a

continuous variable calculated by subtracting the current year of the study from the

year of their birth. Participants were asked how many children they had, from 0 to 6

or more, and in line with the Australian Bureau of Statistics, children were defined as

their „natural, adopted, step, or foster son/s or daughter/s‟, (Australian Bureau of

Statistics, 2006a). Lifestage was defined as 1, younger non-parents, aged under 40

years; 2, older non-parents aged 40 years and older; 3, parents with youngest child

under 6 years; 4, parents with children between 6 and 12 years; 5, parents with

children between 13 and18 years; 6, parents with adult children live at home; 7,

parents with no children living at home (Moen, Harris-Abbott, Lee, & Roehling,

1999). Family and parenting demands were then calculated as 1, no children

(Lifestages 1and 2); 2, adult children (Lifestages 6 and 7); 3, adolescent children at

home (Lifestage 5); 4, primary school children at home (Lifestage 4); and 5, young

children at home (Lifestage 3) (Frone et al., 1992a, 1992b). Marital status was first

coded as 1, single or never married; 2, married or living with partner; 3, separated or

divorced; and 4, widowed. However, as most participants were married or living with

a partner, marital status was further collapsed to 1, not living with a partner or 2,

living with spouse or partner. General health was assessed with a single item,

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„Compared to other people your age, how would you describe your usual state of

health‟, rated on a Likert scale of 1 poor, 2 fair, 3 good, 4 very good, and 5 excellent

(Idler & Benyamini, 1997).

2.2.4.2 Schedules, education, job conditions and income. The working hours

were assessed as a continuous variable by the item, „On average, how many hours do

you actually work, including any paid or extra hours that you put in beyond your

official work week?‟ The number of hours worked per week that the participant

would prefer to work was assessed by the item, „How many hours would you ideally

like to work each week, compared to hours you CURRENTLY work?‟ rated as 1, a

lot less hours; 2 a few less hours; 3 about the same hours; 4, a few more hours; or 5

many more hours (Moen & Yu, 2000). Participants were also asked to „please

indicate the major constraint for transferring from your current work hours to your

ideal hours‟. Spouse or partner‟s hours were assessed as a continuous variable by the

item, „If you have a spouse or partner and they are working, how many hours a week

do they work (e.g. 0, 15, or 45 hours)?‟ Participants were also asked how long it took

them to get to and from work each day with the item, „On average, how long does it

take you to get to and from your workplace each day? Please give the total of both

journeys in minutes. For example, 20 minutes in the morning and 20 minutes in the

afternoon equals 40 minutes in total for the day.‟

Educational background was categorised as 1, finished part or all of high

school; 2, trade or TAFE qualifications; 3, undergraduate tertiary qualifications

(degree or diploma); or 4, postgraduate tertiary qualifications (e.g. masters or PhD).

Objective job conditions were assessed by a series of items. Participants were

asked to indicate how long they „had been in their current position or business?‟ as a

continuous variable (in years), whether their job was „1, permanent, 2, contract or

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temporary, or 3, your own business‟, and whether their work was „1, full-time, or 2,

part-time‟. Participants were asked what industry they worked in (e.g. health, law,

entertainment, IT, banking) and to describe their job (e.g. registered nurse, lawyer,

dancer, software developer, financial planner). Further, participants were asked to

estimate how many people are employed in their workplace (Moen, 2003). The

participants‟ combined household income before tax was assessed by the categories:

1, under $30,000; 2, $30,000 - $59,999; 3, $60,000 - $89,999; 4, $90,000 - $119,999;

5, $120,000 - $149,999; 6, $150,000 - $199,999; 7, $200,000 - $249,999; and 8, over

$250,000.

2.2.4.3 Work-life fit, work-life balance, feeling busy and personal problems.

Work-life fit was assessed with a single item, „how easy or difficult is for you to

manage the demands of your work and your family/personal life‟ rated as 1, very

difficult; 2, moderately difficult; 3, moderately easy; or 4, very easy (Clarke et al.,

2004). Work-life balance was assessed by a single item, „all in all, I am satisfied with

the balance between my work and family/personal life‟, rated from 1, strongly agree

to 5 strongly agree (Clarke et al., 2004). Two items were developed by the author to

rate how busy the individual felt and how they felt about their personal life. How

busy they felt was rated by the item, „How busy are you at the moment, given all the

things that you do at work and at home?‟ rated from 1, life has lots of free time, 5,

life is full but not hectic, to 9, life is hectic all the time. Personal life was rated by the

item, „How would you rate your personal life at the moment?‟, rated from 1, no

problems at all, 5, starting to have concerns, to 9, more than I can handle.

2.2.5 Reliabilities and details of the measures

Each of the measures was chosen as they were widely used and had good

internal reliability in previous research. For all scales that were used, the negatively

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worded items were reverse scored before the scales were summed. The Cronbach‟s

alphas for the scales in the current analysis are given as a range, reflecting the

highest to lowest estimates of the scales internal reliability across the three

measurement times for Study1 and 2. The reliability of the particular scale at Time 1

is given in brackets after the range of reliabilities. Appendix I has the complete list of

the items for each of the following measures.

2.2.6 P, the Person: Generative disposition variables

2.2.6.1 Dispositional optimism. Dispositional optimism was measured with

the Life Orientation Test –Revised (LOT-R); Scheier, Carver & Bridges (1994), 6

items, sample items, „In uncertain times, I usually expect the best‟ and „If something

can go wrong for me, it will‟ (reversed), Cronbach‟s alphas = .831 to .859 (Time 1 =

.831). Items were rated on a Likert scale, 1 strongly disagree to 5 strongly agree.

Whilst the positively and negatively worded items have been used separately as

scales of optimism and pessimism, respectively (for example, Hatchett & Park,

2004), in the current sample, principal components analysis of the six items formed a

single factor (eigenvalue = 3.290) that accounted for 54.84% of the variance. The

scale was therefore used as a single measure of optimism, with high scores indicating

high levels of dispositional optimism.

2.2.6.2 Coping self-efficacy. Self-efficacy was measured by the Coping Self-

Efficacy scale (Chesney et al., 2003). The scale had 26 items that ask the participant

to rate situations, based on the following instructions, “when things aren't going well

for you, or when you're having problems, how confident or certain are you that you

can do the following…” sample situations are „keep from getting down in the

dumps‟, „talk positively to yourself‟, and „find solutions to your most difficult

problems‟. Ratings were based on a Likert scale from 1, I cannot do this at all, 4, I

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am moderately certain I can do this, to 7, I am certain I can do this. Cronbach‟s

alphas = .963 to .969 (Time 1 = .963). High scores indicated high levels of self-

confidence in managing challenging situations.

2.2.6.3 Control. Perceived control of time was assessed by the Perceived

Control of Time Subscale of the Time Management Scale (Macan, 1994), 5 items,

sample items, „I feel in control of my time‟ and „I find it difficult to keep to a

schedule because others take me away from my work‟ (reversed). However, the

alphas for the scales were extremely poor (Cronbach‟s alphas = .548 to .655, Time 1

= .655) and a single item, „I feel in control of my time‟ was used rather than the 5

item scale. High scores indicated a strong sense of being in control of one‟s time.

2.2.6.4 Role salience. Role salience was measured by the Life Role Salience

Scales (LRSS, Amatea et al., 1986), using the reward value and commitment toward

occupational, marital and parental role subscales (six subscales). Each subscale had 5

items and was rated on a Likert scale, from 1 strongly disagree to 5 strongly agree.

Occupational role reward value, sample item, „Having work / a career that is

interesting and exciting to me is my most important life goal‟, Cronbach‟s alphas =

.660 to .711 (Time 1 = .711). Occupational role commitment, sample item, „I expect

to make as many sacrifices as are necessary in order to advance in my work/career‟,

Cronbach‟s alphas = .780 to .791 (Time 1 = .780). The Occupational Role Salience

scale was calculated from the combined occupational role reward and commitment

subscales, Cronbach‟s alphas = .830 to .834 (Time 1 = .833).

Participants were asked about their parental and marital roles, using the same

instruction: „The next questions ask about parenting and marriages or committed

relationships. Please take „marriage‟ to include committed relationships of all types

and „parenting‟ to involve children of all ages, whether they are your own, adopted,

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step or foster children. Please answer the questions as they apply to you and how you

feel about relationships and parenting. You can tick N/A if you don‟t have children

or are not married or in a relationship at the moment, or the questions are not relevant

to you‟. Parental role reward value subscale had 5 items, sample item, „although

parenthood requires many sacrifices, the love and enjoyment of children of one‟s

own are worth it all‟, Cronbach‟s alphas = .799 to .826 (Time 1 = .826). Parental

role commitment subscale had 5 items, sample item, „it is important to me to have

some time for myself and my own development, rather than have children and be

responsible for their care (reversed), Cronbach‟s alphas = .706 to .890 (Time 1 =

.890). The Parental Role Salience scale was calculated from the two parental role

reward and commitment subscales, Cronbach‟s alphas = .784 to .916). Marital role

reward value had 5 items, sample item, „having a successful marriage is the most

important thing in life to me‟, Cronbach‟s alphas = .903 to .912 (Time 1 = .912).

Marital role commitment had 5 items, sample item, „I expect to commit whatever

time is necessary to make my marriage partner feel loved, supported and cared for‟,

Cronbach‟s alphas = .711 to .910 (Time 1 = .910). The Marital Role Salience scale

was calculated from the marital role reward and commitment subscales, Cronbach‟s

alpha = .840 to .935 (Time 1 = .935). High scores on each subscale indicated strong

agreement with the salience of that role.

2.2.6.5 Egalitarian gender role attitudes. Attitudes to gender roles were

assessed with the Egalitarian Gender Role Attitude scale (Moen, 2003), 4 items,

sample items, „it is usually better for everyone if the man is the main provider and

the woman takes care of the family‟ (reversed) and „a working mother can have just

as good relationship with her children as mother who does not work‟, Cronbach‟s

alphas = .710 to .737 (time 1 = .710). High scores indicate more egalitarian attitudes

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to gender roles.

2.2.7 P, the Person: Demand characteristic variables

2.2.7.1 Social skills. Social skills were measured with the Social Skills Scale

(Ferris et al., 2001), 7 items, sample items, „I find it easy to put myself in the position

of others‟ and „I am good at reading other people‟s body language‟ rated on a Likert

scale, from 1 strongly disagree to 5 strongly agree, Cronbach‟s alphas = .747 to .797

(Time 1 = .747). High scores indicate that the individual s able to manage social

situations.

2.2.7.2 Humour. Humour was measured by the Coping Humour Scale

(Martin & Lefcourt, 1983), 7 items, sample item, „I often lose my sense of humour

when I am having problems‟ (reversed) and „I have found that my problems have

been greatly reduced when I try to find something funny in them‟, rated on a Likert

scale, from 1 strongly disagree to 5 strongly agree, Cronbach‟s alphas = .773 to .820

(Time 1 = .773). High scores indicate humour is used to manage difficult situations.

2.2.8 C, the Context: Workplace conditions

2.2.8.1 Job autonomy. Participants rated how much choice they had in the

decisions at work (Voydanoff, 2004c), 4 items, sample items, „how often do you

have a choice in deciding how you do your tasks at work‟ and „how often do you

have a say in planning your work environment – i.e. how your workplace is arranged

or how things are organized‟, rated on a Likert scale on how often the items were

experienced in the workplace, rated from 1 never, 3 sometimes to 5, all the time.

Cronbach‟s alphas = .847 to .885 (Time 1 = .847). High scores indicate greater

choice about workplace decisions.

2.2.8.2 Skill discretion. Participants rated how much they were able to use

their skills and creativity at work (Schwartz et al., 1988), 6 items, sample items,

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„does your work require creativity‟ and „in your job, do you keep learning new

things‟, rated on a Likert scale on how often the items were experienced in the

workplace, rated from 1 never, 3 sometimes to 5, all the time. Cronbach‟s alphas =

.853 to .875 (Time 1 = .860). High scores indicate greater ability to use skills and

more opportunities to learn.

2.2.8.3 Job social support. How much the participant could rely on their

supervisor and co-workers was measured by the Job Social Support scale (van Ypern

& Hagedoorn, 2003), 4 items, with 2 items about supervisors and 2 items about co-

workers. The items were similar, substituting co-workers for supervisor in the items

about co-workers. Items were, „can you rely upon your immediate supervisor (co-

workers) when things get tough at work‟ and „if necessary, can you ask your

immediate supervisor (co-workers) for help‟, rated on a Likert scale on whether this

social support would be given, from 1 never, 3 sometimes to 5, all the time.

Cronbach‟s alphas = .846 to .879 (Time 1 = .846). Higher scores indicated that there

was more social support available from supervisors and co-workers.

2.2.8.4 Managerial support for work-life issues. Workplace support for work-

life balance issues was measured by the Managerial Support subscale of the Work-

Family Culture scale (C. A. Thompson et al., 1999), 11 items, sample items, „in the

event of a conflict, managers understand when employees have to put their families

first‟ and „higher management in this organization encourages supervisors to be

sensitive to employees‟ family and personal needs‟, rated on a Likert scale of 1

strongly disagree to 5 strongly agree. Cronbach‟s alphas = .866 to .901 (Time 1 =

.901). Higher scores indicated that immediate managers and supervisors were more

supportive of family issues.

2.2.8.5 Affective commitment. The emotional attachment to the workplace

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was measured by the Affective Commitment scale (N. J. Allen & Meyer, 1990), 6

items, sample items, „I would be very happy to spend the rest of my career with this

organization‟ and „I do not feel like „part of the family‟ at this organization‟

(reversed), rated on a Likert scale of 1 strongly disagree to 5 strongly agree.

Cronbach‟s alphas = .743 to .795 (Time 1 = .743). Higher scores indicated that the

individual was more affectively attached to their workplace.

2.2.9 C, the Context: The work-life interface

2.2.9.1 Spillover between work and family life. Spillover was measured by the

Work-Family Spillover scale (Grzywacz & Marks, 2000b) with four subscales to

reflect the two factor solution of spillover; the direction of influence (work to home

and home to work) and quality of interaction (negative or positive). Each subscale

had four items, rated on how often these items had been experienced in the previous

year, on a Likert scale of 1, never, 3 sometimes to 5 all the time.

Negative work-to-family spillover, sample items „your job reduces the effort

you can give to activities at home‟ and „job worries or problems distract you when

you are at home‟, Cronbach‟s alphas = .848 to .864 (Time 1 = .864). Positive work-

to-family spillover, sample items, „the things you do at work help you deal with

personal and practical issues at home‟ and „the things you do at work make you a

more interesting person at home‟. Removing item 4 improved the reliability of the

scale and only the 3 item scale was used, Cronbach‟s alphas = .755 to .792 (Time 1 =

.755).

Negative family-to-work spillover, sample items, „responsibilities at home

reduce the effort you can devote to your job‟ and „personal or family worries and

problems distract you when you are at work‟, Cronbach‟s alphas = .770 to .794

(Time 1 = .772). Positive family-to-work spillover, sample items, „talking with

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someone at home helps you deal with problems at work‟ and „the love and respect

you get at home makes you confident about yourself at work‟, Removing item 4

substantially improved the reliability of the scale and only 3 items were used,

Cronbach‟s alphas = .794 to .810 (Time 1 = .794). Higher scores for negative

spillover in either direction indicate more problems and tiredness from one domain to

the other. Higher scores for positive spillover in either direction indicate that one

domain supports better performance in the other domain.

2.2.10 Well-being, mental illness, burnout and work engagement

2.2.10.1 Life satisfaction. Life satisfaction was measured with the Satisfaction

with Life Scale (SWLS, Diener et al., 1985), 5 items, sample items, „in most ways,

my life is close to ideal‟ and „the conditions of my life are excellent‟, rated on a

Likert scale 1 strongly disagree, to 5 strongly agree. Cronbach‟s alphas = .878 to

.894 (Time 1 = .883). High scores indicated high satisfaction with life‟s conditions.

2.2.10.2 Psychological well-being. Psychological well-being was measured

by the 18 item version of Ryff‟s Scales of Psychological Well-Being (Ryff, 1989).

The short version was chosen because of time and space constraints in the survey

document. The scale had six subscales (of 3 items each, 18 items in total) and as the

subscales did not have adequate reliabilities, the scale was used as a single measure.

Cronbach‟s alphas = .820 to .839 (Time 1 = .820). The subscales were Autonomy,

sample item, „I have confidence in my opinions, even if they are different from the

way that most people think‟; Environmental mastery, sample item, „I am good at

managing the responsibilities of my daily life‟; Positive relations with others, sample

item, „people would describe me as a giving person, willing to share my time with

others‟; Self-acceptance, sample item, „when I look at the story of my life, I am

pleased with how things have turned out so far‟; Purpose in life, sample item, „some

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people wander aimlessly through life, but I am not one of them‟; and Personal

growth, sample item, „for me, life has been a continual process of learning, changing,

and growth‟. High scores indicated that the individual had a high sense of

psychological well-being in each of these dimensions.

2.2.10.3 Satisfaction with life domains. Single items were used to assess

satisfaction with various life domains. These items were „I am satisfied with my

work life‟, „I am satisfied with my family or personal life‟, „I am satisfied with my

relationship with my spouse or partner‟, „I am satisfied with my sporting,

recreational or non-work activities‟ and „I am satisfied with my or my family‟s

financial position‟. A single item was used to assess the perceived fairness of the

division of household labour, „in my relationship with my spouse or partner, I am

satisfied with the way that work (e.g. childcare, household chores, earning money,

yard work, car maintenance) is divided‟ (Clarke et al., 2004). All items were rated on

a Likert scale of 1 strongly disagree to 5 strongly agree. High scores indicated high

satisfaction with each domain.

2.2.10.4 Mental Illness. Depression, anxiety and stress were measured by the

short version of the Depression Anxiety and Stress Scale (DASS-21, S. H. Lovibond

& P. F. Lovibond, 1995). Each scale had 7 items, rated on a Likert scale of 0, didn‟t

apply to me at all, 2 applied to me to some degree, or some of the time, 4 applied to

me to a considerable degree, or a good part of time, or 6 applied to me very much, or

most of the time. Depression, sample items, „I couldn't seem to experience any

positive feeling at all‟ and „I found it difficult to work up the initiative to do things‟,

Cronbach‟s alphas = .865 to .867 (Time 1 = .867). Anxiety, sample items, „I was

aware of dryness of my mouth‟, and „I was worried about situations in which I might

panic and make a fool of myself‟, Cronbach‟s alphas = .813 to .860 (Time 1 = .813).

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Stress, sample items, „I found it hard to wind down‟ and „I was intolerant of anything

that kept me from getting on with what I was doing‟, Cronbach‟s alphas = .851 to

.861 (Time 1 = .861). From the published norms for the scales (S. H. Lovibond & P.

F. Lovibond, 1995), the scores for the normal range were as follows, depression (0 to

9), anxiety (0 to 7) and stress (0 to 14); for the mild range for depression (10-13),

anxiety (8 to 9), and stress (15 to 18); for the moderate range for depression (14 to

20), anxiety (10 to 14) and stress (19 to 25); and for the severe range for depression

(21+), anxiety (15+) and stress (26+). Scores on each scale in the mild or greater

categories indicate that the individual had one or more mental illnesses.

2.2.10.5 Burnout. Burnout was measured with the Maslach Burnout Inventory

– General (Maslach et al., 1996), using the three subscales of emotional exhaustion,

cynicism, and professional efficacy. Emotional exhaustion, 5 items, sample items, „I

feel emotionally drained from my work‟ and „I feel used up at the end of the

workday‟, Cronbach‟s alphas = .875 to .892 (Time 1 = .892). Cynicism, 5 items,

sample items, „I have become more and more cynical about whether my work

contributes to anything‟ and „I doubt the significance of my work‟, Cronbach‟s

alphas = .826 to .841 (Time 1 = .841). Professional efficacy, 6 items, sample items, „I

feel exhilarated when I accomplish something at work‟ and „in my opinion, I am

good at my job‟, Cronbach‟s alphas = .735 to .775 (Time 1 = .735). Scales were rated

on a Likert scale of 1 strongly disagree to 5 strongly agree. Higher scores indicated

greater levels of exhaustion and cynicism and greater feelings of professional

competence or efficacy.

2.2.10.6 Work Engagement. Engagement in work was measured with the

Utrecht Work Engagement Scale (Schaufeli et al., 2002), with three subscales

measuring work vigour, work dedication and work absorption. Work vigour was the

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energy the individual feels at work, 6 items, sample items, „when I get up in the

morning, I feel like going to work‟ and „at my work, I feel bursting with energy‟,

Cronbach‟s alphas = .807 to .815 (Time 1 = .815). Work dedication was the zest and

intrinsic rewards available from work, 5 items, sample items, „I am enthusiastic

about my job‟, „I find the work that I do full of meaning and hope‟, and „to me, my

job is challenging‟, Cronbach‟s alphas = .907 to .912 (Time 1 = .912). Work

absorption was the depth of involvement in work, sample items, „when I am

working, I forget everything else around me‟ and „it is difficult to detach myself from

my job‟, Cronbach‟s alphas = .761 to .796 (Time 1 = .790). Scales were rated on a

Likert scale of 1 strongly disagree to 5 strongly agree. Higher scores indicated that

the individual had greater energy, zest, enthusiasm and involvement in their work.

2.2.11 Procedure

Calls for volunteers were made in two places, a university alumni e-magazine

and by email to the administrative staff of a large public hospital. A second call for

volunteers was made through the university alumni‟s e-magazine the following

month, whilst for the hospital staff, second and third calls for volunteers were sent

via email 10 and 20 days respectively, after the first call for volunteers. Interested

volunteers clicked on the embedded link in the e-magazine or the email to be

directed to the survey, hosted by SurveyMonkey. To facilitate longitudinal data

collection, the last question participants were asked was to give their email address

so that they could take part in the second and third waves of data collection. Data

collection for Time 1 was conducted between late August and early November 2006,

with the longer period reflecting a delay in starting the hospital staff‟s data

collection. However, there were no major events or circumstances over this time that

would negate or skew the results or created any artificial differences.

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At Time 2 and Time 3, these email addresses were collected and arranged in

blocks of around 50 addresses. Rather than send all the emails at once, this step was

designed to avoid the emails being considered as spam by internet service providers.

Email addresses were always stored separately from any identifying data and there

was no link between the email addresses and any individual‟s responses to the

survey. To ensure that the emails calling for action at Time 2 and Time 3 were sent

correctly, the researcher‟s private email was included as the last in each block of

emails. In this way, each block of emails could be verified as sent. Further, to ensure

the privacy of participants‟ email addresses, the researcher‟s own email address was

in the main address line, with all participants‟ email addresses placed in the BCC (i.e.

blind copy) address line. As with the Time 1 call for volunteers for the hospital staff,

second and third reminder calls to action were sent 10 and 20 days after the initial

call to action. It was necessary to have this reminder as not all participants responded

to the first call. It would appear that once an email is off the first page of messages, it

is „lost‟ to the recipient. Sending reminders allows the participant to take part if they

were too busy or unable to do so in the first instance. Data collection for Time 2

occurred in February and March 2007, whilst data collection for Time 3 occurred in

May and June 2007.

At the end of each data collection period, the data was downloaded from the

SurveyMonkey website as an Excel file. This file was then converted into an SPSS

data file ready for analysis. Participant identification codes were constructed by

taking the individual‟s initials and their birth date, for example, EB160469.

Once the data was in the SPSS format, missing data, skewness and kurtosis

were assessed, scale reliabilities were calculated and then the scales were

constructed. Hierarchical multiple regression was conducted using the Time 1 data

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set whilst the longitudinal modelling used the panel data (the matched set of

participants) from Times 1, 2 and 3 and is reported in the next chapter.

2.2.12 Analytical strategy for the hierarchical multiple regression (HMR) analyses

2.2.12.1 Variables in each block. The variables will be entered in three

blocks, which reflect the Person and Context components of Bronfenbrenner‟s

developmental equation. The Individual Difference variables combine the measures

of the individual‟s generative disposition and demand characteristics, as well as their

age and gender. The variables are also entered such that the earlier variables are

presumed to be „causes‟ of the later variables (J. Cohen, Cohen, West, & Aiken,

2003). For example, a person (Block 1) „selects‟ a job and „has‟ a family (Block 2)

that have „effects‟ on their work-life interface (Block 3). As such, the blocks (as

shown in Table 2.1) are arranged in a way that will test first the effect of the

individual‟s characteristics to understand the underlying influence of the person, then

the effects of the contexts of their life, as first the work and family variables and then

the work-life interface variables. For simplicity, only the names of the variables,

rather than the scales are given in Table 2.1, as the scales are outlined previously in

the chapter. For all these scales, a high score denotes a high level of that variable,

either as more self-efficacy, greater job autonomy and more positive spillover, as

well as more negative spillover.

The outcomes were considered firstly as the positive outcomes of well-being

(as life satisfaction and psychological well-being), work satisfaction and work

engagement (as vigour, dedication and absorption in work) and secondly, as the

negative outcomes of mental illness (as depression, anxiety and stress) and burnout

(as emotional exhaustion, cynicism and professional efficacy). Again, a high score

indicated a greater level of the outcome, as greater life satisfaction, more emotional

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Table 2.1

Variables in each block as blocks are entered into hierarchical multiple regressions

Blocks in HMR Variables in block

1 Individual Differences Dispositional optimism, Coping self-efficacy, Humour,

Social skills, Perceived control of time, Egalitarian

gender role attitudes Reward and Commitment from

Occupational, Parental, and Marital roles, Gender, Age

2 Work and Family Affective commitment, Managerial support, Job social

support, Job autonomy, Skill discretion, Hours per

week, Preferred working hours, Family demands,

Children, Marital status, Education

3 Work-Life Interface Work-life balance, Work-life fit, Feeling busy,

Negative work-to-family spillover, Positive work-to-

family spillover, Negative family-to-work spillover,

Positive family-to-work spillover

exhaustion and more professional efficacy. Step 1 assessed the predictive value of

the Individual Difference variables; Step 2 added the assessment of the Work and

Family variables to the Individual Difference variables, whilst Step 3 added the

Work-Life Interface to these variables, so that all variables were assessed together.

2.2.12.2 Statistical considerations for the regression analyses. Consideration

must be given to the possibility that these broad ranging analyses will lead to Type I

errors being commited, such that spurious associations would be considered to be

important (J. Cohen et al., 2003). It would be simpler to minimise the number of

predictor and outcome variables in order to minimise the likelihood of errors but this

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would negate the purpose of the current research to understand the broad sweep of an

individual‟s self and context. As outlined in the literature review in Chapter 1,

different areas of psychology have focused on variables of particular importance to

each area, with the consequence that individual differences are not widely included

in organizational psychology and the role of work or family are not usually included

in health or personality psychology. Whilst these areas of psychology may by

necessity narrow the focus of their study, the purpose of this thesis was to combine

these diverse strands into a holistic understanding of the working adult, as the person

themselves and their surrounding context. In everyday life, individuals have many

different roles, many different attributes and different working conditions which the

individual does not separate but lives as a whole life.

To reduce the likelihood of Type I errors, the procedure outlined by Cohen et

al. (2003) was followed to ensure that the investigation-wise error rate was

contained. First, variables were entered as blocks in the hierarchical multiple

regression, second, the variance of that set of variables was tested by the F test using

the appropriate alpha level (α = .05), third, if the F test was significant, then the

significance of predictors within the set were tested and fourth, if the F test was not

significant, then the predictors in that set were not interpreted (J. Cohen et al., 2003).

By reducing the large number of predictor variables to three sets of variables and

adding the requirement of a set being significant before being interpreted, the

resulting F and t tests of the analyses were robust and protected against Type I errors.

In this way, error rates were contained for each outcome.

However, as there are multiple outcomes, it was also necessary to consider if

these could also lead to inflation of Type I errors. The significance of the alpha test

was taken „per hypothesis‟ (J. Cohen et al., 2003) and in the current research, this

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was the specific hypothesis related to the significant predictors of each of the

outcomes. It was important to note that the outcomes were not compared against

each other and there was no hypothesis that suggested that one outcome was more

appropriate than any other to explain developmental outcomes. For example, the

current research would not propose that life satisfaction was a better measure of well-

being than psychological well-being, that depression was a better measure of mental

illness than stress, or any other variation of such comparisons. Each outcome

represented a distinct measure of psychological functioning. Therefore, although

there were many comparisons, the analysis of each outcome was considered to be a

separate examination of the data.

Next, mediation and moderation were considered. It would be difficult to

establish mediation by the process used by Baron and Kenny (1986), where there are

significant relationships between the predictor (A) and the outcome (B), between A

and the mediator (M) and M and B and where the addition of M to the regression

reduces the relationship between A and B to non-significance (Baron & Kenny,

1986; Muller, Judd, & Yzerbyt, 2005). The difficulty here would be to determine

which of the variables in the block would be the mediator that could be tested in the

classic procedure. Where was a possibility of mediation occurring following the

addition of the second or third blocks of variables, a test for multiple mediators was

undertaken (Preacher & Hayes, 2008), using the bootstrapping method to test the

significance of the indirect paths through the mediators. Estimates of the indirect

effects and their 95% confidence intervals (CI) were also calculated with mediation

occurring when the CI did not include zero. However, the overall focus of the

analyses was on understanding how each block adds to the explanation of the

developmental outcomes. As such, the effects from the addition of the second and

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third blocks were seen by any changes to the beta weights of all variables in each

step. Any changes would the hypothesis for each specific outcome. First, did

individual differences predict that outcome, second, did the work and family factors

add to the outcome, over and above the individual differences, and finally, did the

work-life interface add again to the outcome, over and above individual differences

and the work and family variables? Given the number of variables involved in the

regressions, post hoc analyses of moderation was limited to the effects of the

significant predictor variables on the outcome variables. The question focused on

whether there was evidence of any moderation between the significant predictors to

simplify the analyses and minimise any possibility of Type I errors.

Finally, there was some evidence that there are a number of suppressor

variables in the regression equations. The suppressor variables were not directly

identified but their effects could be seen on other variables. Suppression was

indicated where the zero order correlations between the effected variable and the

outcome variables were less, rather than greater, than the beta weights or semi-partial

correlations between the two variables (i.e. either classical or cooperative

suppression) or the zero order correlation and the beta weight had opposing signs

(i.e. negative suppression). Whilst the presence of suppressor variables may appear

to confuse the understanding of the predictors of a particular outcome, they can be

useful in removing or tidying the variance associated with another predictor variable.

In each case, prediction was improved as the magnitude of the predictor was

increased by the presence of the suppressor variables, clarifying the relationships (J.

Cohen et al., 2003; Conger, 1974; Tabachnick & Fidell, 2001). As the predictor

variables were entered as blocks, a better description may be of suppressor situations,

rather than specific suppressor variables (Tzelgov & Henik, 1991). Cohen et al.

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(2003) note that more complex models were more likely to include suppression and

that suppression may underlie homeostatic mechanisms, indicating a more complex

explanation and understanding of psychological functioning.

2.3 Results

2.3.1 Data cleaning and screening

Before constructing the scales, the data was examined for missing data.

Participants who missed substantial portions of the survey were deemed to be non-

completers and removed from the potential data set. Similarly, duplicate entries were

removed. This occurred where the participant had begun, paused and returned to

finish the survey and the initial portion was retained by SurveyMonkey. After this

initial tidying of the data set, missing data within items of the scales were found to be

at random and were replaced by the item means (Tabachnick & Fidell, 2001).

The scales for the regression analyses were constructed and assessed for

normality, linearity and homoscedasticity. Normal distribution of the scores on the

scales was considered by their skewness and kurtosis, based on the comparison of the

ratios of the skewness statistic to its standard error and the kurtosis statistic to its

standard error to the z distribution. A z-score > 3.0 would indicate either skewed or

kurtotic distributions (Tabachnick & Fidell, 2001). However, another rule of thumb

for skewness is that when the skewness statistic falls within the range of -1 to +1, this

value indicates a normal distribution (Hair, Anderson, Tatham, & Black, 1998).

For kurtosis, only anxiety and depression showed positive kurtosis, indicating

that most participants had similar scores. For skewness, examination of the scales

found that depression and anxiety were highly positively skewed on both the z-score

calculation and the skewness statistic indicating that most participants reported lower

levels of these variables, whilst for stress, there is milder breach, as the z-score is

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greater than 3, but the statistic itself is less than 1. Comparison of the mental illness

scores to the published norms (S. H. Lovibond & P. F. Lovibond, 1995) showed that

most participants fell within the normal ranges for depression (normal range 0 to 9,

74.3%), anxiety (normal range 0 to 7, 76.4%) and stress (normal range 0 to 14,

74.9%) which would explain the positive skew of the data. Transformations for

depression and anxiety scales were considered but not pursued, as transformation did

not alter the patterns of results and to maintain interpretability of the results.

Interestingly, there was some negative skew (z > 3, Skew statistic < -1) for

dispositional optimism, egalitarian gender role attitudes, managerial support, job

social support, skill discretion, work vigour, work dedication and professional

efficacy. These results would indicate that participants were mostly optimistic, more

likely to view the genders as equal, receive support from managers and co-workers,

feel that they can use their skills at work, whilst feeling vigorous, enthusiastic and

competent about their work. However, as will be noted in the discussion about

sample size, it was considered that there were sufficient cases to absorb the influence

of these breaches of normality, particularly as the positive skew could be considered

to be mild breaches of skewness as the skewness statistic inside the range of ± 1

(Hair et al., 1998). Further checks of normality were given by examination of the

scatter plots of predicted values to residual errors and the normal probability plots

from the multivariate analyses also found that there were no concerns for normality

(Tabachnick & Fidell, 2001). For all the outcomes, the normal probability plots fell

along the straight diagonal, indicating that the distribution of actual data closely

matched a normal distribution. The scatter plots of predicted versus expected residual

errors were evenly distributed across the range, indicating that there were no

breaches of linearity or homoscedasticity in the regression analyses.

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Table 2.2

Retention of participants over time, with percentages of original sample of

participants

Participant group Time 1 Time 2 Time 3

University alumni 206 144 (69.9%) 112 (55.2%)

Hospital staff 264 146 (55.3%) 91 (34.5%)

Total 470 290 (61.7%) 203 (48.3%)

After the removal of multivariate outliers (n = 5), the final composition of

participants from the two possible pools of volunteers is shown in Table 2.2, with the

retention of participants across the three time periods. Attrition analysis found that

there were no significant differences between participants who completed all three

measures and those that dropped out after Time 1, based on age (F(1, 462) = 0.024, p

= .877), gender (F(1, 462) = 0.174, p = .677) or the hours worked per week at Time 1

(F(1, 462) = 0.121, p = .729). Also, those participants that were retained over time

did not differ on their preferences for shorter working hours (M = 2.20, SD = 0.76)

than those who dropped out (M = 2.33, SD = 0.84; F(1, 462) = 3.060, p = .080). The

retention of participants over time is reported here to show how participant numbers

changed, although the analyses for Study 1 is based only on the Time 1 participants.

2.3.2 Demographics

At Time 1, participants (N = 470, 78.9% female) ranged in age from 19 to 66

years (M = 38.90 years, SD = 11.05 years) and were mostly married or living with

their partner (n = 293, 62.3%) or single (n = 119, 25.3%). For the hierarchical

multiple regression, marital status was collapsed to either no partner (n = 177,

37.7%) or having a partner (n = 293, 62.3%). The participants represented a broad

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diversity of lifestages and parental and family demands. The parents among the

participants (n = 242, 51.5%) had between 1 and 6 children (M = 2.26, SD = 0.92),

with most having 2 (n = 126, 52.1%) or 3 children (n = 54, 22.3%). The most

frequent life stage were non-parents under 40 years (n = 183, 38.9%) with the other

life stages being reasonably evenly distributed. Family and parental demands (i.e. the

care associated with dependent children) were calculated from the life stages, from

the least or no parental or family demands (Lifestages 1 and 2, n = 231, 49.1%), to

the limited demands from adult children (Lifestages 6 and 7, n = 87, 18.5%), to some

demands from adolescent children (Lifestage 5, n = 31, 6.6%), to more demands

from children aged 6 to 12 years (Lifestage 4, n = 63, 13.4%), to the most demands

from children under 6 years of age (Lifestage 3, n = 58, 12.3%).

Participants reported that their education as: finished high school (n = 98,

20.9%), trade or TAFE qualification (n= 45, 9.6%), undergraduate tertiary

qualifications (n = 231, 49.1%) or postgraduate tertiary qualifications (n = 96,

20.4%). As could be expected, the alumni group, as university graduates, had

significantly greater levels of educational attainment than the hospital group,

F(1,473) = 116.445, p < .001. However, this disparity was not considered as

limitation as the data for both groups was pooled for the analyses.

The range of hours that the participants worked was from under 10 hours per

week to a maximum of 85 hours per week (M = 40.78 hours, SD = 11.95 hours).

Most participants worked full-time (88.30%), with their hours ranging from 30 to 85

hours (M = 43.73 hours, SD = 8.89 hours) in comparison to part-time workers who

worked less hours (M = 18.22 hours, SD = 7.46 hours). Across all participants,

working longer hours was associated with the desire to work fewer hours per week, r

= -.350, p < .001. Full-time workers showed a stronger preference to work fewer

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hours (M = 2.17, SD = 0.74) than their current working hours in comparison to part-

time workers (M = 2.91, SD = 0.94), where „3‟ equated to wanting to work „about the

same hours‟, F(1,466) = 44.101, p < .001. Interestingly, part-time workers also had

significantly greater family and parental demands (M = 2.80, SD= 1.64) than did

full-time workers (M = 2.14, SD = 1.43), F(1,473) = 10.150, p = .002. Taken

together, these results would indicate that those participants in part-time work may

have felt their hours about right for them, given their greater family and parenting

needs. There was considerable variation in the hours that the participants‟ spouses

worked, from not working to 120 hours per week (n = 342, M = 36.74, SD = 18.25).

Commuting time per day varied widely also from some participants working from

home to those participants with long commutes of 200 minutes in total each day (M

= 68.10 minutes, SD = 39.14 minutes).

Participants reported their gross household income in bands, with most

participants (87.6%) having incomes between $30,000 and $150,000 (n = 126,

$30,000 to $59,999; n = 124, $60,000 to 89,999; n = 103, $90,000 to 119,999; and n

= 59, $120,000 to $149,999). There were many different occupations and industries

represented among the participants. This was a strength of the study as this diversity

allows for a broad understanding of important factors that are applicable to many

working adults, rather than a narrow emphasis on one occupation alone.

Finally, participants‟ reported that on average, they were in good to very

good health (M = 3.42, SD = 0.88), that their lives were busy, being more than full

but less than hectic (M = 6.49, SD = 1.53, range 1 to 9) and that they felt that their

problems were below the level that would start to cause concern (M = 4.38, SD =

2.10, range 1 to 9). In addition, although the range included both ends of the scales,

on average participants rated both work-life fit and work-life balance near the centre

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of possible ratings. The fit between their work and family roles was at the midpoint

between the rating of moderately easy and moderately difficult (M = 2.51, SD = 0.74,

range 1 to 4) and their satisfaction with their work-life balance was at the midpoint

(M = 3.06, SD = 1.28, range 1 to 5). When considering satisfaction with non-work

domains, participants were slightly above average for their satisfaction with their

family life (M = 3.76, SD = 1.20, range 1 to 5), for how well household chores were

shared (M = 3.54, SD = 1.21, range 1 to 5) and about average for satisfaction with

their recreational activities (M = 3.05, SD = 1.31, range 1 to 5). Participants with

partners were mostly quite satisfied with their spouse or partner (M = 4.21, SD =

1.14, range 1 to 5).

2.3.3 Scale construction and sample size

To construct each of the measures, the necessary items were reverse scored

(indicated by * after the items, as shown in Appendix I) and all the scale items were

then summed. Cronbach‟s alphas were calculated for each scale and overall, the

scales had good to excellent reliability (i.e. alphas > .70), although the Cronbach‟s

alphas for three scales were problematic: Perceived Control of Time, Positive Work-

to-Family Spillover and Positive Family-to-Work Spillover. Perceived Control of

Time could not be improved by the removal of any one item and it was decided to

use only one item, “I feel in control of my time” in place of the scale as this item had

good face validity for the purpose of the research. The two positive spillover scales

were improved substantially by the removal of item 4 in both scales, leaving the

scale with 3 items. Cronbach‟s alphas for all scales at Time 1 are given on the

diagonal in brackets in Table 2.3, with the single items indicated by a dash, -.

The means, standard deviations and correlations between the variables used

in the hierarchical multiple regressions are shown in Table 2.3, based on the Time 1

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data set. There were many variables involved as the purpose of these regressions was

to understand Bronfenbrenner‟s developmental equation by bringing together the

different strands of research from individual differences in health to occupational and

organizational psychology. The rule of thumb, N ≥ 50 + 8 m, where m is the number

of independent predictors, allowed for calculation of a sample size (N) that will

detect moderate effect sizes with an alpha = .05 and adequate power (.80)

(Tabachnick & Fidell, 2001). The variables in hierarchical multiple regression were

entered in three blocks as the Individual Difference variables (14 variables), the

Work and Family variables (11 variables) and the Work-Life Interface variables (7

variables), which gives 32 independent variables. The sample size equation was

calculated as N ≥ 50 + (8 * 32) = 50 + 256, therefore N ≥ 306, which indicated that

there were enough participants to meet the criteria of power and effect size.

Tabachnick and Fidel (2001) also note that skewed variables (for example, for

depression and anxiety here) and small effect sizes require more cases for each

independent variable. The authors suggested using the formula, N ≥ (8 / f2) + (m – 1),

where f2 was the effect size (i.e. small, f

2 = .02, medium, f

2 = .15, and large, f

2 = .35).

Using the small effect size, this equation can be calculated as N ≥ (8/.02) + (32 – 1) =

400 + 31, therefore N ≥ 431. By either calculation, the Time 1 sample size (N = 470)

had sufficient power to detect small effect sizes (i.e. r = .10, Cohen, 1988), withstand

skewness in the variables and be robust in its design.

2.3.4 Means, standard deviations and correlations between the variables

The correlations are arranged as the individual difference variables, the work

and family variables, the work-life interface variables and finally the outcome

variables. The effect sizes for these correlations can be read as small (r = .10),

medium (r = .30) and large (r = .50) (Cohen, 1988).

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Perhaps the most intriguing finding was the limited significant correlations

between gender (variable 13 in Table 2.3) and the other variables. The only outcome

variable that gender had a significant relationship with was work satisfaction, with

women more likely to have greater satisfaction with their work then men. From the

other significant correlations for gender shown in Table 2.3, in addition to being

more satisfied with their work, women were more likely to have less education, to

feel busier, to report greater satisfaction with their work-life balance, to have more

egalitarian gender role attitudes and greater social support from their managers and

co-workers. Men in contrast were more likely to work longer hours, whilst preferring

to work fewer hours, to use more humour as a coping strategy and to have greater

family and parental demands than women. These relationships were further

examined in the multiple regressions to establish whether gender was a significant

predictor for the outcomes, when all the variables were considered.

There were many variables that could be examined for their correlational

relationships. The outcome variables (variables 33 to 44, in Table 2.3) were mostly

highly significantly correlated between themselves and in the expected directions.

For example, psychological well-being was significantly, negatively correlated with

depression and exhaustion, stress was positively correlated to cynicism and work

vigour and work dedication are positively correlated. Consideration of the predictor

variables is limited to working hours (variable 20), often cited as the major cause of

work-life problems (see for example, Pocock, 2003) and dispositional optimism

(variable 1), proposed here as the main component of the active individual and

adaptive self-regulation (see for example, Aspinwall et al., 2002). From the

correlations between the hours that an individual works and the other variables

showed that individuals working longer hours were (Results continued, p 189)

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Table 2.3

Correlations between the variables included in the hierarchical multiple regressions

Mean SD 1 2 3 4 5

1 Dispositional optimism 21.84 4.77 (.831) 0.551*** 0.308*** 0.239*** 0.343***

2 Coping self-efficacy 119.36 30.12 (.963) 0.400*** 0.323*** 0.387***

3 Perceived control of time 3.16 1.20 - -0.032 0.101*

4 Social skills 24.55 4.18 (.747) 0.244***

5 Humour 24.77 4.54 (.773)

6 Egalitarian gender roles 15.87 3.53

7 Occupational role reward 17.18 3.81

8 Occupational role commitment14.56 4.35

9 Parental role reward 16.54 7.68

10 Parental role commitment 15.67 8.49

11 Marital role reward 14.48 7.37

12 Marital role commitment 17.12 7.56

13 Gender 0.79 0.41

14 Age 38.90 11.05

15 Affective commitment 18.23 4.76

16 Managerial support 38.11 8.86

17 Job social support 14.87 3.61

18 Job autonomy 13.95 3.53

19 Skill discretion 20.85 4.83

20 Hours per week 40.78 11.95

21 Preferred work hours 2.26 0.80

22 Family demands 2.21 1.47

23 Children 1.17 1.31

24 Marital status 1.62 0.49

25 Education 2.69 1.02

26 Satisfaction with WLB 3.06 1.28

27 Work-life fit 2.51 0.74

28 Feeling busy 6.49 1.53

29 Negative WF Spillover 10.80 3.32

30 Positive WF Spillover 7.24 2.47

31 Negative FW Spillover 8.49 2.98

32Positive FW Spillover 9.66 2.99

33 Life satisfaction 16.64 4.77

34 Psychological well-being 70.65 8.89

35 Work satisfaction 3.46 1.21

36 Work vigour 21.23 4.51

37 Work dedication 17.94 4.76

38 Work absorption 16.05 4.17

39 Depression 6.19 6.68

40 Anxiety 4.70 6.01

41 Stress 10.93 7.89

42 Exhaustion 14.67 5.29

43 Cynicism 12.53 4.98

44 Professional efficacy 25.03 3.38 †p <.10, *p <.05, **p <.01, ***p <.001

Note. Cronbach‟s alphas given in brackets on the diagonal, single items indicated by -

183

Table 2.3 (Continued)

6 7 8 9 10

1 Dispositional optimism 0.072 0.080† 0.128** 0.088† 0.077†

2 Coping self-efficacy 0.136** 0.052 0.129** 0.028 0.056

3 Perceived control of time 0.115* 0.024 0.094* 0.019* -0.002

4 Social skills 0.106* 0.157** 0.171*** 0.030 0.014

5 Humour 0.099* -0.034 0.039 0.000 0.016

6 Egalitarian gender roles (.710) 0.066 0.144** -0.075 -0.020

7 Occupational role reward (.711) 0.604*** -0.195*** -0.179***

8 Occupational role commitment (.780) -0.153** -0.148**

9 Parental role reward (.826) 0.784***

10 Parental role commitment (.890)

11 12 13 14 15

1 Dispositional optimism 0.035 0.122** -0.063 0.158** 0.158**

2 Coping self-efficacy -0.015 0.072 -0.006 0.093* 0.098*

3 Perceived control of time -0.059 -0.054 0.019 0.050 0.093*

4 Social skills 0.018 0.075 0.085† -0.033 0.130**

5 Humour -0.068 -0.008 -0.113* 0.166*** 0.105*

6 Egalitarian gender roles -0.202*** -0.136 ** 0.220*** -0.045 0.054

7 Occupational role reward -0.084† -0.051 -0.033 -0.160** 0.128**

8 Occupational role commitment -0.086† -0.052 -0.052 -0.136** 0.219***

9 Parental role reward 0.352*** 0.361*** -0.040 0.269*** -0.015

10 Parental role commitment 0.316*** 0.401*** -0.018 0.170*** 0.033

11 Marital role reward (.912) 0.708*** -0.113* 0.042 0.052

12 Marital role commitment (.910) -0.037 -0.008 0.055

13 Gender - -0.105* 0.033

14 Age - 0.139**

15 Affective commitment (.743)

16 17 18 19 20

1 Dispositional optimism 0.242*** 0.277*** 0.202*** 0.238*** 0.030

2 Coping self-efficacy 0.203*** 0.334*** 0.300*** 0.226*** 0.023

3 Perceived control of time 0.264*** 0.294*** 0.199*** 0.048 -0.129**

4 Social skills 0.116* 0.098* 0.230*** 0.121** -0.002

5 Humour 0.119* 0.190*** 0.142** 0.174*** 0.006

6 Egalitarian gender roles 0.123** 0.139** 0.144** 0.173*** -0.016

7 Occupational role reward 0.066 -0.075 0.095* 0.149** 0.176***

8 Occupational role commitment 0.090† -0.037 0.166*** 0.214*** 0.203***

9 Parental role reward 0.043 0.040 0.143** -0.003 -0.088†

10 Parental role commitment 0.061 0.060 0.098* -0.018 -0.078†

11 Marital role reward -0.037 0.012 0.089† 0.076 -0.011

12 Marital role commitment 0.015 0.056 0.068 0.095* 0.021

13 Gender 0.067 0.102* 0.007 -0.003 -0.156**

14 Age -0.045 0.006 0.143** 0.083† 0.015

15 Affective commitment 0.370*** 0.271*** 0.355*** 0.328*** 0.075

16 Managerial support (.901) 0.480*** 0.360*** 0.145** -0.123**

17 Job social support (.846) 0.311*** 0.171*** -0.113*

18 Job autonomy (.847) 0.420*** 0.026

19 Skill discretion (.860) 0.084†

20 Hours per week - †p <.10, *p <.05, **p <.01, ***p <.001


184


21 22 23 24 25

1 Dispositional optimism 0.000 0.035 0.086† 0.080† 0.137**

2 Coping self-efficacy 0.071 -0.037 0.023 -0.019 -0.028

3 Perceived control of time 0.285*** -0.101* -0.045 -0.034 -0.086†

4 Social skills -0.048 0.031 -0.004 0.050 0.069

5 Humour -0.082† 0.073 0.107* 0.068 0.090†

6 Egalitarian gender roles 0.107* 0.001 -0.070 -0.058 0.021

7 Occupational role reward 0.065 -0.230*** -0.190*** -0.166*** 0.231***

8 Occupational role commitment 0.090† -0.138** -0.105* -0.150** 0.198***

9 Parental role reward -0.024 0.515*** 0.526*** 0.302*** -0.022

10 Parental role commitment -0.045 0.476*** 0.466*** 0.338*** -0.021

11 Marital role reward -0.035 0.139** 0.172*** 0.453*** 0.081†

12 Marital role commitment 0.004 0.132** 0.121* 0.473*** 0.062

13 Gender 0.147** -0.101* -0.069 -0.093* -0.160**

14 Age -0.113* 0.199*** 0.554*** 0.181*** -0.061

15 Affective commitment 0.118* 0.084† 0.114* 0.093* 0.006

16 Managerial support 0.164*** 0.026 -0.027 0.039 0.033

17 Job social support 0.164*** 0.008 -0.007 0.107* -0.052

18 Job autonomy 0.067 0.109* 0.123** 0.139** 0.142**

19 Skill discretion -0.022 0.036 0.030 0.052 0.313***

20 Hours per week -0.361*** -0.099* -0.005 -0.018 0.089†

21 Preferred work hours - -0.035 -0.046 -0.068 -0.117*

22 Family demands - 0.668*** 0.355*** 0.057

23 Children - 0.328*** -0.061

24 Marital status - 0.049

25 Education -

26 Satisfaction with WLB

27 Work-life fit

28 Feeling busy

29 Negative work-to-family spillover

30 Positive work-to-family spillover

31 Negative family-to-work spillover

32Positive family-to-work spillover

33 Life satisfaction

34 Psychological well-being

35 Work satisfaction

36 Work vigour

37 Work dedication

38 Work absorption

39 Depression

40 Anxiety

41 Stress

42 Exhaustion

43 Cynicism

44 Professional efficacy †p <.10, *p <.05, **p <.01, ***p <.001


185


26 27 28 29 30

1 Dispositional optimism 0.172*** 0.074 -0.009 -0.168*** 0.167***

2 Coping self-efficacy 0.294*** 0.190*** -0.060 -0.261*** 0.130**

3 Perceived control of time 0.578*** 0.497*** -0.449*** -0.487*** 0.023

4 Social skills -0.012 -0.040 0.169*** 0.017 0.211***

5 Humour 0.084† 0.024 0.078† -0.098* 0.187***

6 Egalitarian gender roles 0.134** 0.202*** -0.054 -0.187*** 0.121**

7 Occupational role reward -0.029 -0.035 0.020 0.111* 0.145**

8 Occupational role commitment 0.002 -0.040 0.024 0.036 0.174***

9 Parental role reward 0.011 0.014 0.048 -0.114* 0.002

10 Parental role commitment -0.006 -0.008 0.081† -0.075 0.038

11 Marital role reward 0.052 -0.071 0.067 0.055 0.002

12 Marital role commitment 0.078 -0.047 0.123** 0.074 0.005

13 Gender 0.099* 0.106* 0.010 -0.022 0.079†

14 Age -0.034 0.014 -0.036 -0.094* 0.117*

15 Affective commitment 0.169*** 0.074 0.036 -0.135** 0.327***

16 Managerial support 0.326*** 0.274*** -0.163*** -0.323*** 0.169***

17 Job social support 0.330*** 0.293*** -0.138** -0.363*** 0.130**

18 Job autonomy 0.196*** 0.119* -0.003 -0.130** 0.299***

19 Skill discretion 0.065 -0.019 0.067 -0.020 0.352***

20 Hours per week -0.231*** -0.238*** 0.229*** 0.250*** 0.048

21 Preferred work hours 0.447*** 0.338*** -0.303*** -0.318*** 0.049

22 Family demands -0.101* -0.161*** 0.183*** -0.027 0.046

23 Children -0.084† -0.124** 0.118* -0.065 0.101*

24 Marital status 0.004 -0.057 0.142** 0.040 0.043

25 Education -0.113* -0.175*** 0.122** 0.158** 0.153**

26 Satisfaction with WLB - 0.571*** -0.453*** -0.538*** 0.040

27 Work-life fit - -0.576*** -0.527*** 0.002

28 Feeling busy - 0.433*** 0.098*

29 Negative work-to-family spillover (.864) -0.022

30 Positive work-to-family spillover (.755)

31 Negative family-to-work spillover

32Positive family-to-work spillover

33 Life satisfaction

34 Psychological well-being

35 Work satisfaction

36 Work vigour

37 Work dedication

38 Work absorption

39 Depression

40 Anxiety

41 Stress

42 Exhaustion

43 Cynicism



186


31 32 33 34 35

1 Dispositional optimism -0.207*** 0.286*** 0.468*** 0.536*** 0.259***

2 Coping self-efficacy -0.297*** 0.413*** 0.545*** 0.641*** 0.235***

3 Perceived control of time -0.382*** 0.237*** 0.413*** 0.334*** 0.285***

4 Social skills 0.013 0.155** 0.250*** 0.337*** 0.120**

5 Humour -0.067 0.138** 0.251*** 0.332*** 0.105*

6 Egalitarian gender roles -0.105* 0.060 0.159** 0.223*** 0.174***

7 Occupational role reward -0.045 0.063 0.033 0.097* -0.005

8 Occupational role commitment -0.082† 0.032 0.048 0.096* 0.065

9 Parental role reward 0.049 -0.003 0.112* 0.063 0.101*

10 Parental role commitment 0.054 0.005 0.163*** 0.123** 0.052

11 Marital role reward 0.042 0.224*** 0.168*** 0.084† 0.037

12 Marital role commitment -0.001 0.258*** 0.250*** 0.192*** 0.028

13 Gender -0.008 0.056 0.056 0.040 0.148**

14 Age -0.107* -0.079† -0.006 0.051 0.036

15 Affective commitment -0.091* 0.018 0.152** 0.126** 0.461***

16 Managerial support -0.185*** 0.171*** 0.317*** 0.258*** 0.343***

17 Job social support -0.186*** 0.257*** 0.316*** 0.315*** 0.369***

18 Job autonomy -0.151** 0.163*** 0.327*** 0.328*** 0.359***

19 Skill discretion -0.203*** 0.155** 0.270*** 0.321*** 0.364***

20 Hours per week 0.019 -0.063 -0.060 0.026 -0.084†

21 Preferred work hours -0.097* 0.080† 0.137** 0.027 0.212***

22 Family demands 0.229*** -0.122** -0.008 -0.028 0.113*

23 Children 0.085† -0.088† -0.027 -0.004 0.095*

24 Marital status 0.061 0.160*** 0.166*** 0.077† 0.039

25 Education 0.055 0.054 0.035 0.151** 0.028

26 Satisfaction with WLB -0.300*** 0.267*** 0.421*** 0.278*** 0.397***

27 Work-life fit -0.367*** 0.190*** 0.255*** 0.211*** 0.208***

28 Feeling busy 0.300*** -0.116* -0.123** -0.045 -0.066

29 Negative work-to-family spillover 0.414*** -0.074 -0.250*** -0.264*** -0.351***

30 Positive work-to-family spillover 0.053 0.114 0.139** 0.154** 0.290***

31 Negative family-to-work spillover (.772) -0.110* -0.277*** -0.261*** -0.183***

32Positive family-to-work spillover (.794) 0.493*** 0.465*** 0.100*

33 Life satisfaction (.883) 0.637*** 0.282***

34 Psychological well-being (.820) 0.260***

35 Work satisfaction -

36 Work vigour

37 Work dedication

38 Work absorption

39 Depression

40 Anxiety

41 Stress

42 Exhaustion

43 Cynicism



187


36 37 38 39 40

1 Dispositional optimism 0.403*** 0.328*** 0.133** -0.436*** -0.298***

2 Coping self-efficacy 0.464*** 0.323*** 0.133** -0.523*** -0.319***

3 Perceived control of time 0.274*** 0.135** 0.016 -0.341*** -0.302***

4 Social skills 0.179*** 0.130** 0.052 -0.088† 0.039

5 Humour 0.206*** 0.166*** 0.058 -0.128** -0.115*

6 Egalitarian gender roles 0.183*** 0.193*** 0.085† -0.144** -0.109*

7 Occupational role reward 0.190*** 0.176*** 0.248*** 0.066 0.097*

8 Occupational role commitment 0.274*** 0.255*** 0.297*** -0.053 -0.008

9 Parental role reward 0.097* 0.072 -0.027 -0.087† -0.090†

10 Parental role commitment 0.079† 0.063 -0.015 -0.139** -0.116*

11 Marital role reward -0.002 0.066 0.000 -0.021 0.016

12 Marital role commitment 0.051 0.100* -0.011 -0.085† -0.017

13 Gender 0.019 0.042 0.048 -0.050 0.011

14 Age 0.247*** 0.168*** 0.128** -0.060 -0.158**

15 Affective commitment 0.361*** 0.486*** 0.359*** -0.131** -0.077†

16 Managerial support 0.289*** 0.254*** 0.102* -0.258*** -0.188***

17 Job social support 0.251*** 0.277*** 0.065 -0.274*** -0.171***

18 Job autonomy 0.406*** 0.437*** 0.317*** -0.200*** -0.115*

19 Skill discretion 0.399*** 0.718*** 0.463*** -0.173*** -0.127**

20 Hours per week 0.070 0.085† 0.083† 0.041 0.061

21 Preferred work hours 0.171*** 0.093* 0.064 -0.085† -0.024

22 Family demands 0.075 0.089† 0.052 -0.041 -0.095*

23 Children 0.198*** 0.148** 0.126** -0.087† -0.099*

24 Marital status 0.044 0.040 -0.018 -0.029 -0.042

25 Education 0.057 0.120** 0.063 0.064 -0.060

26 Satisfaction with WLB 0.242*** 0.173*** -0.006 -0.325*** -0.225***

27 Work-life fit 0.167*** 0.060 -0.087† -0.231*** -0.230***

28 Feeling busy -0.044 0.015 0.084† 0.121** 0.204***

29 Negative work-to-family spillover -0.287*** -0.159** 0.052 0.383*** 0.339***

30 Positive work-to-family spillover 0.250*** 0.362*** 0.282*** -0.075 0.063

31 Negative family-to-work spillover -0.344*** -0.251*** -0.133** 0.370*** 0.332***

32Positive family-to-work spillover 0.204*** 0.159** 0.024 -0.253*** -0.130**

33 Life satisfaction 0.395*** 0.314*** 0.154** -0.457*** -0.269***

34 Psychological well-being 0.459*** 0.344*** 0.133** -0.502*** -0.330***

35 Work satisfaction 0.447*** 0.548*** 0.280*** -0.305*** -0.180***

36 Work vigour (.815) 0.640*** 0.521*** -0.371*** -0.277***

37 Work dedication (.912) 0.603*** -0.289*** -0.180***

38 Work absorption (.790) -0.100* -0.011

39 Depression (.867) 0.554***

40 Anxiety (.813)

41 Stress

42 Exhaustion

43 Cynicism



188


41 42 43 44

1 Dispositional optimism -0.254*** -0.279*** -0.332*** 0.259***

2 Coping self-efficacy -0.386*** -0.314*** -0.311*** 0.294***

3 Perceived control of time -0.411*** -0.469*** -0.250*** 0.148**

4 Social skills 0.068 -0.022 -0.102* 0.141**

5 Humour -0.117* -0.151** -0.190*** 0.165***

6 Egalitarian gender roles -0.054 -0.155** -0.205*** 0.205***

7 Occupational role reward 0.130** 0.022 -0.040 0.156**

8 Occupational role commitment 0.021 -0.065 -0.158** 0.119*

9 Parental role reward -0.068 -0.164*** -0.099* 0.075

10 Parental role commitment -0.073 -0.148** -0.120* 0.071

11 Marital role reward 0.006 -0.032 0.006 0.018

12 Marital role commitment 0.038 -0.068 -0.058 0.026

13 Gender 0.015 -0.007 -0.027 0.014

14 Age -0.132** -0.092* -0.136** 0.127**

15 Affective commitment -0.062 -0.308*** -0.542*** 0.282***

16 Managerial support -0.241*** -0.431*** -0.380*** 0.182***

17 Job social support -0.201*** -0.356*** -0.338*** 0.201***

18 Job autonomy -0.094* -0.284*** -0.421*** 0.354***

19 Skill discretion -0.018 -0.138** -0.422*** 0.320***

20 Hours per week 0.134** 0.207*** -0.005 0.032

21 Preferred work hours -0.132** -0.331*** -0.173*** 0.075

22 Family demands 0.005 -0.040 -0.114* 0.101*

23 Children -0.068 -0.076 -0.169*** 0.080†

24 Marital status 0.022 -0.040 -0.060 0.014

25 Education 0.091* 0.057 -0.012 0.075

26 Satisfaction with WLB -0.402*** -0.534*** -0.295*** 0.122**

27 Work-life fit -0.360*** -0.477*** -0.206*** 0.061

28 Feeling busy 0.355*** 0.309*** 0.042 0.069

29 Negative work-to-family spillover 0.539*** 0.673*** 0.364*** -0.076

30 Positive work-to-family spillover 0.041 -0.124** -0.316*** 0.274***

31 Negative family-to-work spillover 0.406*** 0.384*** 0.269*** -0.167***

32Positive family-to-work spillover -0.136** -0.177*** -0.118* 0.177***

33 Life satisfaction -0.281*** -0.331*** -0.288*** 0.263***

34 Psychological well-being -0.320*** -0.359*** -0.392*** 0.339***

35 Work satisfaction -0.232*** -0.470*** -0.597*** 0.345***

36 Work vigour -0.241*** -0.480*** -0.589*** 0.463***

37 Work dedication -0.107* -0.312*** -0.670*** 0.479***

38 Work absorption 0.065 -0.096* -0.327*** 0.316***

39 Depression 0.653*** 0.442** 0.434*** -0.168***

40 Anxiety 0.644*** 0.377*** 0.272*** -0.153**

41 Stress (.861) 0.488*** 0.275*** -0.067

42 Exhaustion (.892) 0.567*** -0.165***

43 Cynicism (.841) -0.429***

44 Professional efficacy (.735) †p <.10, *p <.05, **p <.01, ***p <.001

Note. Cronbach‟s alphas given in brackets on the diagonal, single items indicated by –

189

(Results continued from page 181)

more likely to report more occupational role salience, to feel busier, to have less

work-life fit, to be less satisfied with their work-life balance and to feel they have

less control over their time. Longer working hours were also associated with less

social support from managers and co-workers and less support from managers about

work-life matters and less satisfaction with their family lives and their recreational

activities. Further, longer hours were associated with more negative spillover from

work to family domains, more stress and greater emotional exhaustion. These

associations can have detrimental effects for the individual and it was important for

the regressions to explore these relationships further.

Dispositional optimism was significantly correlated to many of the variables

highlighting the diverse influence of an optimistic view of one‟s life, such as greater

well-being, less mental illness, and more positive spillover but less negative spillover

between work and family domains. However, dispositional optimism did not

influence working hours or preferred hours nor work-life fit or balance, feeling busy

or family demands or occupational or parental role salience. As with gender and

working hours, the results of the multiple regressions will provide information on the

relative importance of dispositional optimism and the other variables, as predictors of

the well-being, mental health, burnout and work engagement outcomes.

2.3.5 Presentation of the results of the HMR

The results of the hierarchical multiple regressions are presented in the tables

that follow, showing the changes to B (unstandardized estimates), the standard error

(SE) of B, beta weights (β) and their significance at each step, with the unique

variance (i.e. the squared semi-partial correlations, sr2) of the significant predictor

variables at each step (Tables 2.4 to 2.15). The tables also report the F test for each

190

step, the change in variance (R2 change, i.e. ΔR

2) associated with that step, the final

variance, R2 and adjusted R

2 and the final F test for the regression. Where

participants had missing data, they were deleted listwise. As noted earlier in the

Results, the multivariate outliers (n = 5) were identified using the Χ2, p < .001

criteria for the Mahalanobis distance and were removed from the data set. The

variance that was uniquely explained by each significant predictor is shown as sr2 in

the tables, with the magnitude of the effect of a predictor taken as small (sr2 = .01),

medium (sr2 = .09) and large (sr

2 = .25) (J. Cohen et al., 2003). There were some

large effects, but most of the effects of the predictor variables were small to small-

medium. Effect sizes for R2 of the models were taken as small (R

2 = .02), medium

(R2 = .15) and large (R

2 = .35) (J. Cohen, 1992) which allowed consideration of the

variance added at each step (ΔR2) and the final variance explained by the model.

The results of the hierarchical multiple regressions show that placing the

individual first in the analyses highlighted the centrality of the individual to the

outcomes. Further, controlling for the „person‟ allows the effects of the workplace or

family responsibilities to be more clearly articulated, now that these are „free‟ of the

influence of the individual, as with adding the Work-Life Interface variables. For

most of the hierarchical multiple regressions, the addition of each set of variables

was a significant increment in the variance explained by the model. The first block of

the Individual Difference variables significantly predicted all of the outcomes with

the Work and Family and Work-Life Interface variables mostly adding significantly

to the outcome variables. Specifically, the addition of the Work and Family variables

did not significantly improve the variance explained for depression, anxiety and

stress and Work-Life Interface variables did not significantly increase the variance

explained for work dedication. Overall, the three sets of variables were sound

191

predictors of the outcomes, with a small number of the variables being the most

common predictors of all the outcomes. The summary of the beta weights for the

outcomes are shown in Table 2.18 and illustrate these „core‟ predictors which will in

turn form the basis for the structural equation modelling in Study 2.

2.3.6 Life satisfaction

The first hierarchical multiple regression was conducted to assess the

influences of the three blocks of variables, the Individual Difference, Work and

Family and Work-Life Interface variables on the individual‟s life satisfaction. The

results of the three steps are shown in Table 2.4. The ΔR2 for each block is shown

across the top line of the table along with its F test. The R2 for the regression model

was very large and significant, R2 = .555, F(32,399) = 15.535, p < .001. The adjusted

R2 was .519, which indicates that just over half of the variability of an individual‟s

life satisfaction was predicted by the combined variables. Individual Difference

variables explained 46.4% of the variance, with Work and Family variables adding

4.3% and Work-Family Interface adding another 4.8%, which were both small but

significant increments to the explanation of life satisfaction.

From Table 2.4, dispositional optimism, coping self-efficacy and perceived

control of time are the most significant, positive predictors of life satisfaction in the

first step with these remaining significant as the other blocks are added. The presence

of children had a negative significant effect on life satisfaction which has been

enhanced by the presence of suppressors (r =-.023, β = -.104), although this effect at

Step 3 only approached significance. At the final step, positive family-to-work

spillover, satisfaction with work-life balance, dispositional optimism, coping self-

efficacy, and, parental role commitment were the strongest predictors of life

satisfaction with small-medium effect sizes and with the rest of the significant

192

Table 2.4

Results for the three steps of hierarchical multiple regressions for life satisfaction

Variables added Step 1 Step 2 Step 3

In each block ΔR2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2

ΔR

2,

F test for ΔR2 .464, F (14,417) = 25.785*** .043, F(11.406) = 3.224*** .048, F(7,399) = 6.105***

Block 1

Dispositional optimism 0.215 0.047 .210*** .028 0.196 0.046 .191*** .022 0.185 0.045 0.180*** .019

Coping self-efficacy 0.044 0.008 .274*** .041 0.041 0.008 .257*** .033 0.027 0.008 0.168** .013

Perceived control of time 0.994 0.166 .245*** .046 0.801 0.174 .198*** .026 0.496 0.194 0.123* .007

Social skills 0.092 0.046 .081* .005 0.071 0.046 .062 0.085 0.044 0.075† .004

Humour 0.050 0.043 .048 0.045 0.043 .043 0.040 0.041 0.039

Egalitarian gender roles 0.136 0.052 .100* .009 0.081 0.052 .059 0.063 0.051 0.046

Occupational role reward 0.060 0.059 .047 0.048 0.059 .037 0.040 0.057 0.031

Occupational role commitment -0.076 0.051 -.069 -0.085 0.052 -.077 -0.061 0.050 -0.056

Parental role reward -0.041 0.039 -.066 -0.037 0.040 -.058 -0.034 0.039 -0.054

Parental role commitment 0.072 0.034 .127* .006 0.080 0.034 .141* .007 0.095 0.033 0.168** .009

Marital role reward 0.071 0.035 .108* .005 0.058 0.035 .088† .003 0.032 0.034 0.049

Marital role commitment 0.070 0.035 .109* .005 0.049 0.035 .076 0.017 0.034 0.026

Gender 0.655 0.453 .055 0.561 0.457 .047 0.387 0.446 0.032

Age -0.033 0.017 -.075† .005 -0.022 0.021 -.050 -0.006 0.021 -0.015

Block 2

Affective commitment -0.027 0.044 -.027 -0.014 0.043 -0.013

Managerial support 0.067 0.024 .121** .009 0.052 0.024 0.095* .006

Job social support -0.039 0.058 -.029 -0.058 0.057 -0.044

Job autonomy 0.126 0.060 .092* .005 0.111 0.058 0.082† .004

Skill discretion 0.098 0.043 .098* .006 0.086 0.043 0.086* .004

Hours per week -0.002 0.016 -.006 0.005 0.016 0.012

Pref work hours 0.248 0.248 .041 0.074 0.250 0.012

Family demands 0.021 0.181 .006 0.156 0.178 0.048

Children -0.378 0.225 -.103† .003 -0.382 0.217 -0.104† .003

Marital status 0.727 0.446 .073 0.466 0.434 0.047

Education -0.146 0.194 -.030 -0.126 0.187 -0.026

Block 3

Satisfaction with WLB 0.676 0.190 0.177*** .014

Work-life fit 0.029 0.323 0.004

Feeling busy 0.111 0.146 0.035

Negative work-to-family spillover 0.055 0.070 0.037

Positive work-to-family spillover -0.067 0.078 -0.034

Negative family-to-work spillover -0.085 0.068 -0.052

Positive family-to-work spillover 0.327 0.067 0.205*** .027

Total R2 = .555, Total Adj R

2 = .519, Final model, F(32,399) = 15.535***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001;

193

predictors having small effects. In summary, life satisfaction, as a global assessment

of subjective well-being, appeared to rest mainly with a supportive home life

(positive family-to-work spillover), satisfaction with the balance between roles, a

sense of optimism and competence and valuing an involvement with children.

Interestingly, many individuals, regardless of parental status strongly agreed that it

was important to be involved with their (current or future) children.

2.3.7 Psychological well-being

The second hierarchical multiple regression was conducted to assess the


Family and Work-Life Interface variables on the individual‟s psychological well-

being. The results of the three steps are shown in Table 2.5. The ΔR2 for each block

is shown across the top line of the table, along with the F test for the addition. The R2

for the regression model was very large and significant, R2 = .586, F(32,399) =

18.821, p < .001. The adjusted R2 was .570, which indicates that just over half of the

variability of an individual‟s psychological well-being was predicted by the

combination of the variables. Specifically, the Individual Difference variables had a

very large effect, explaining 51.6% of the variance, with Work and Family variables

adding 3.4% and Work- Life Interface adding another 3.6%, which were both small

and significant increments to the explanation of psychological well-being.

There are similarities between the significant predictors of psychological

well-being and life satisfaction although there is a predominance of Individual

Difference variables as the significant predictors of psychological well-being when

all variables were considered in the third step. From Table 2.5, half of the individual

Difference variables were significant, positive predictors of psychological well-

being at Step 1, with the strongest predictor, coping self-efficacy having a medium

194

effect size (sr2 = .080). Dispositional optimism and egalitarian gender role attitudes

were also significant and had small-medium effect sizes (sr2 = .027 and .022,

respectively), with the remaining significant predictors with small effects. Skill

discretion and education were the significant predictors from the Work and Family

variables, although with only small effects. At the final step, the significant

predictors were coping self-efficacy, positive family-to-work spillover, dispositional

optimism, social skills, parental role commitment, education, an egalitarian gender

role attitude, affective commitment and negative work-to-family spillover.

In summary, psychological well-being as the individual‟s assessment of the

mastery, direction and relationships in their lives, rested with their self-efficacy, or

competence to manage challenges, their optimism and ability to get on with others, a

supportive home environment, a good education to provide opportunities for more

interesting life experiences, involvement with children and a belief that both genders

should have equal opportunities in life. The facets of psychological well-being, for

example, environment mastery and positive relations with others, could then be seen

as consequences of these predictors, underpinned by the individual‟s belief that they

can manage the challenges that they encounter.

2.3.8 Satisfaction with work

The next hierarchical multiple regression was conducted to assess the


Family and Work-Life Interface variables on the individual‟s satisfaction with their

work. The results of the three steps of the regression are shown in Table 2.6, with

ΔR2 for each step across the top of the table. The R

2 for the regression model was

large and significant, R2 = .474, F(32,399) = 9.468, p < .001. The adjusted R

2 was

.431, which indicates that just under half of the variability of an individual‟s

195

Table 2.5

Results for the three steps for the hierarchical multiple regression for psychological well-being



2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2

ΔR2, F test for ΔR

2 .516, F(14,417) = 33.820*** .034, F(11,406) = 2.916*** .036, (F(7,399) = 5.079***

Block 1

Dispositional optimism 0.387 0.079 0.208*** .027 0.319 0.079 .171*** .017 0.309 0.077 .166*** .016

Coping self-efficacy 0.113 0.013 0.384*** .080 0.111 0.014 .379*** .071 0.089 0.014 .302*** .041

Perceived control of time 0.766 0.282 0.104** .008 0.790 0.297 .107** .008 0.368 0.334 .050

Social skills 0.267 0.078 0.129** .013 0.254 0.078 .123** .011 0.262 0.076 .127** .012

Humour 0.119 0.073 0.063 0.079 0.073 .042 0.076 0.071 .040

Egalitarian gender roles 0.390 0.089 0.158*** .022 0.340 0.089 .137*** .015 0.262 0.088 .106** .009

Occupational role reward 0.206 0.101 0.089* 0.134 0.101 .058 0.126 0.098 .055

Occupational role commitment -0.142 0.087 -0.071 -0.206 0.089 -.103* .006 -0.163 0.087 -.082† .004

Parental role reward -0.125 0.065 -0.110† .004 -0.112 0.069 -.098 -0.132 0.067 -.115† .004

Parental role commitment 0.125 0.058 0.121* .005 0.131 0.058 .126* .005 0.153 0.057 .148** .007

Marital role reward 0.065 0.059 0.054 0.034 0.060 .028 -0.005 0.059 -.004

Marital role commitment 0.136 0.059 0.116* .006 0.136 0.060 .117* .006 0.107 0.059 .092† .003

Gender 0.835 0.770 0.038 0.998 0.780 .046 1.061 0.766 .049

Age -0.001 0.029 -0.002 -0.013 0.036 -.015 0.011 0.035 .014

Block 2

Affective commitment -0.045 0.075 -.024 -0.021 0.074 -.012

Managerial support 0.055 0.041 .055 0.027 0.041 .027

Job social support 0.020 0.099 .008 -0.068 0.098 -.028

Job autonomy 0.122 0.102 .049 0.130 0.100 .052

Skill discretion 0.176 0.073 .097* .006 0.179 0.074 .098* .006

Hours per week 0.013 0.028 .018 0.037 0.027 .051

Pref work hours -0.277 0.422 -.025 -0.376 0.429 -.034

Family demands -0.341 0.308 -.057 -0.154 0.306 -.026

Children 0.172 0.383 .026 0.099 0.373 .015

Marital status -0.161 0.761 -.009 -0.465 0.747 -.026

Education 0.939 0.331 .108** .009 1.006 0.322 .115** .010

Block 3

Satisfaction with WLB 0.050 0.326 .007

Work-life fit 0.720 0.555 .059

Feeling busy 0.255 0.252 .043

Negative work-to-family spillover -0.313 0.121 -.117* .007

Positive work-to-family spillover -0.141 0.134 -.039

Negative family-to-work spillover -0.007 0.117 -.002

Positive family-to-work spillover 0.577 0.115 .199*** .025


2 = .570, F(32,399) = 18.821***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

196

satisfaction with work was predicted by the combination of the variables.

Specifically, the Individual Difference variables explained 19.0% of the variance,

with Work and Family variables adding 23.7% (both significant, medium effects)

and Work-Family Interface adding another small and significant increment of 4.6%,

to the explanation of work satisfaction.

Unlike life satisfaction and psychological well-being, the contribution to

satisfaction with work from Work and Family variables was greater than that of the

Individual Difference variables. After the addition of the Work-Life Interface

variables, the individual‟s feeling that they had control of their time (β = .228, p <

.001 in Step 1, to β = .082, p = .117 in Step 3) and their egalitarian gender role

attitudes (β = .126, p = .008 in Step1 to β = .058, p = .157 in Step 3) were no longer

significant predictors of work satisfaction. Using the multiple mediation process

(Preacher & Hayes, 2008), the possible mediation of the effect of egalitarian gender

role and perceived control of time were explored. There was no evidence of

mediation for egalitarian gender role attitudes or for perceived control of time, as the

coefficients for the mediation pathways were non-significant and the confidence

intervals included zero (i.e. the indirect paths were not different from zero).

Interestingly, the salience of the occupational role, measured either as role reward or

role commitment, was not a significant predictor of work satisfaction.

At Step 3, the most significant predictors of work satisfaction were higher

levels of affective commitment, satisfaction with work-life balance, skill discretion,

lack of negative work-to-family spillover, parental role reward, dispositional

optimism and gender. Affective commitment had a medium-small effect size (sr2 =

.052), whilst skill discretion (sr2 = .020) and satisfaction with work-life balance (sr

2 =

.021) are smaller. Of note for work satisfaction was that this is the only outcome

197

Table 2.6

Results for the three steps for the hierarchical multiple regression for satisfaction with work



2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .190, F(14,417) = 6.983*** .237, F(11,406) = 15.283*** .046, F(7,399) = 5.032**

Block 1

Dispositional optimism 0.040 0.014 .155** .015 0.024 0.013 0.094† .005 0.026 0.012 0.103* .006

Coping self-efficacy 0.001 0.002 .024 0.000 0.002 0.005 0.000 0.002 0.008

Perceived control of time 0.232 0.051 .228*** .040 0.171 0.047 0.168*** .019 0.083 0.053 0.082

Social skills 0.010 0.014 .035 0.001 0.012 0.004 0.001 0.012 0.004

Humour 0.009 0.013 .033 -0.003 0.012 -0.012 -0.010 0.011 -0.039

Egalitarian gender roles 0.043 0.016 .126** .014 0.029 0.014 0.084* .006 0.020 0.014 0.058

Occupational role reward -0.003 0.018 -.010 -0.006 0.016 -0.018 -0.002 0.016 -0.006

Occupational role commitment 0.009 0.016 .034 -0.025 0.014 -0.090† .004 -0.021 0.014 -0.077

Parental role reward 0.026 0.012 .166* .009 0.026 0.011 0.167* .008 0.024 0.011 0.154* .007

Parental role commitment -0.010 0.011 -.071 -0.018 0.009 -0.125† .005 -0.017 0.009 -0.121† .005

Marital role reward 0.019 0.011 .113† .006 0.011 0.009 0.065 0.009 0.009 0.053

Marital role commitment -0.014 0.011 -.088 -0.011 0.009 -0.066 -0.010 0.009 -0.064

Gender 0.429 0.140 .143** .018 0.299 0.124 0.100* .008 0.301 0.122 0.100* .008

Age 0.001 0.005 .010 -0.011 0.006 -0.098† -0.011 0.006 -0.102* .005

Block 2

Affective commitment 0.084 0.012 0.327*** .070 0.074 0.012 0.288*** .052

Managerial support 0.004 0.007 0.031 0.001 0.006 0.006

Job social support 0.031 0.016 0.092† .005 0.023 0.016 0.070

Job autonomy 0.025 0.016 0.074 0.023 0.016 0.068

Skill discretion 0.048 0.012 0.192*** .024 0.046 0.012 0.181*** .020

Hours per week -0.001 0.004 -0.014 -0.001 0.004 -0.007

Pref work hours 0.091 0.067 0.060 0.002 0.068 0.001

Family demands 0.051 0.049 0.062 0.044 0.049 0.053

Children 0.065 0.061 0.070 0.060 0.059 0.065

Marital status -0.178 0.121 -0.071 -0.130 0.119 -0.052

Education 0.020 0.052 0.017 0.034 0.051 0.028

Block 3

Satisfaction with WLB 0.207 0.052 0.216*** .021

Work-life fit -0.020 0.088 -0.012

Feeling busy 0.069 0.040 0.085† .004

Negative work-to-family spillover -0.046 0.019 -0.125* .008

Positive work-to-family spillover 0.038 0.021 0.076† .004

Negative family-to-work spillover -0.001 0.019 -0.003

Positive family-to-work spillover -0.031 0.018 -0.077† .004


2 = .431, Final model, F(32,399) = 9.468***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

198

for which gender is a significant predictor with women reporting higher work

satisfaction than men, following on from the significant correlation between gender

and work satisfaction. The influences of age (at Step 3, r = .060, β = -.102, p = .042)

and parental role commitment (at Step 3, r = .066, β = -.121, p = .056) were

enhanced by the presence of suppressor variables, although both variables are on the

cusp of being significant and only have small effects. In summary, work satisfaction

was linked to feeling attached and belonging to your workplace, having work that

used your talents and skills and work that does not intrude or take from the rest of

your life. Minimising the problems that spill over from work, valuing being a parent

(and enabling a sense of perspective to work), being optimistic and younger added to

the explanation of work satisfaction.

2.3.9 Work vigour

The next three hierarchical multiple regressions focus on the components of

work engagement. The first of these regressions will assess the influences of the

three blocks of variables, the Individual Difference, Work and Family and Work-Life

Interface variables on work vigour. The results of the three steps of the regression

were shown in Table 2.7, with ΔR2 for each step, along with their F test, across the

top of the table. The R2 for the overall regression model was large and significant, R

2

= .491, F(32,399) = 12.026, p < .001. The adjusted R2 was .450, which indicates that

just under half of the variability of an individual‟s work vigour was predicted by the

combination of the variables. Specifically, the Individual Difference variables

explained 34.7% of the variance, a significant large effect with Work and Family

variables adding 11.5% and Work-Life Interface adding another 2.8%, which were

both significant (medium and small effects, respectively) increments to the

explanation of work vigour.

199

Table 2.7

Results for the three steps for the hierarchical multiple regression for work vigour



2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .347, F(14,417) = 15.845*** .115, F(11,406) = 7.928*** .028, F(7,399) = 3.168***

Block 1

Dispositional optimism 0.143 0.047 .151** .014 0.119 0.045 .125** .009 0.116 0.044 .123** .009

Coping self-efficacy 0.038 0.008 .257*** .036 0.034 0.008 .227*** .026 0.027 0.008 .178** .014

Perceived control of time 0.296 0.169 .079† .005 0.083 0.168 .022 -0.114 0.192 -.030

Social skills -0.005 0.047 -.005 -0.018 0.044 -.017 -0.011 0.044 -.011

Humour 0.010 0.044 .010 -0.005 0.041 -.005 -0.009 0.041 -.009

Egalitarian gender roles 0.108 0.053 .086* .006 0.060 0.050 .048 0.028 0.051 .023

Occupational role reward 0.107 0.060 .091† .005 0.084 0.057 .072 0.092 0.056 .078

Occupational role commitment 0.197 0.052 .194*** .022 0.104 0.050 .103* .006 0.109 0.050 .107* .006

Parental role reward 0.035 0.039 .060 0.016 0.039 .027 0.007 0.038 .013

Parental role commitment 0.016 0.035 .030 0.008 0.033 .015 0.010 0.033 .020

Marital role reward 0.005 0.035 .007 -0.013 0.034 -.021 -0.016 0.034 -.027

Marital role commitment -0.001 0.035 -.002 -0.001 0.034 -.001 -0.009 0.034 -.015

Gender 0.572 0.461 .052 0.236 0.441 .021 0.352 0.440 .032

Age 0.098 0.018 .239*** .048 0.061 0.020 .148** .012 0.061 0.020 .149** .012

Block 2

Affective commitment 0.120 0.042 .128** .011 0.117 0.042 .125** .010

Managerial support 0.041 0.023 .081† .004 0.030 0.023 .058

Job social support -0.019 0.056 -.015 -0.045 0.056 -.037

Job autonomy 0.126 0.058 .100** .006 0.129 0.057 .103* .007

Skill discretion 0.152 0.041 .164*** .018 0.129 0.042 .139** .012

Hours per week 0.025 0.016 .068 0.031 0.016 .084* .005

Pref work hours 0.827 0.238 .149** .016 0.820 0.246 .148** .014

Family demands -0.013 0.174 -.004 0.093 0.175 .031

Children 0.376 0.216 .111† .004 0.327 0.214 .096

Marital status -0.219 0.430 -.024 -0.230 0.428 -.025

Education -0.161 0.187 -.036 -0.097 0.185 -.022

Block 3


Work-life fit 0.076 0.318 .012

Feeling busy 0.243 0.144 .081† .004

Negative work-to-family spillover -0.163 0.069 -.120* .007


Negative family-to-work spillover -0.199 0.067 -.132** .011

Positive family-to-work spillover 0.092 0.066 .062

Total R2 = .491, Adj R

2 = .450, Final model, F(32, 399) = 12.026***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

200

In Step 1, increasing age and higher levels of coping self-efficacy,

occupational role commitment, and dispositional optimism were the strongest

predictors of work vigour. Age (sr2 = .048) and coping self-efficacy (sr

2 = .036) had

medium-small effect sizes with the other variables having small effect sizes. The

addition of the Work and Family variables highlighted the importance of workplace

resources to feeling vigorous, as greater skill discretion, affective commitment,

working one‟s preferred hours and having autonomy at work were significant

predictors. The effects of age and occupational role commitment on work vigour

were not mediated by the workplace resources, as the coefficients of the indirect

paths were non-significant and the confidence intervals included zero.

At Step 3, the most significant predictors were higher levels of coping self-

efficacy, older age, working one‟s preferred hours, greater skill discretion, affective

commitment and dispositional optimism, a lack of negative spillover from family to

work and from work to family and more occupational role commitment. The effects

of working hours and feeling busy on vigour are enhanced by the presences of other

suppressor variables, although these were small and only close to being significant.

In summary, feeling vigorous at work depended on feeling confident in one‟s

abilities to manage challenges, being attached and belonging at work, working the

hours you want, feeling the future was positive and not having problems from home

intruding into work domain. In contrast to work satisfaction, perhaps older workers

were more vigorous as they are have accumulated more resources and are more

pragmatic about their jobs, using their past jobs for comparison, whilst being a

realistic about their current jobs.

2.3.10 Work dedication

The second hierarchical multiple regression about work engagement will

201

assess the influence of the three blocks of variables on work dedication. The results

of the three steps are shown in Table 2.8, with the ΔR2 at the top of the table, along

with the F test for each step. The R2 for the overall regression model was very large

and significant, R2 = .662, F(32,399) = 24.367, p < .001. The adjusted R

2 was .634,

which indicated that nearly two thirds of the variability of an individual‟s work

dedication is predicted by the variables. Specifically, the Individual Difference

variables explained 23.9% of the variance in work dedication, with Work and Family

variables adding 41.6%, which was a large and significant increase in the variance

explained, although the Work-Family Interface variables only added another 0.7%,

which was not a significant increase. Therefore, the third block of variables will not

be further considered as part of the results.

Although the Individual Difference variables of dispositional optimism,

egalitarian gender role attitudes, occupational role commitment and age are

significant in Step 1, the most striking predictors to work dedication are the Work

and Family variables of skill discretion and affective commitment. At Step 3, skill

discretion has a large effect (β = .565, p <.001, sr2 = .194) on work dedication, far

outstripping the other significant predictors, affective commitment, dispositional

optimism and education. In summary, work dedication which captured the zest that

an individual has for their work rested substantially on the ability to use one‟s

creativity and skills and to continue learning in one‟s job. Whilst optimism and

attachment to work are contributors, being able to express yourself in your work

increases the pride, meaning and enthusiasm that your job engenders.

2.3.11 Work absorption

The last of the hierarchical multiple regressions on work engagement

assessed the effect of the three blocks of variables on work absorption. The results of

202

Table 2.8

Results for the three steps for the hierarchical multiple regression for work dedication Variables added Step 1 Step 2 Step 3


2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .239, F(14,417) = 9.333*** .416, F(11,406) = 44.489* .007, F(7,399) = 1.138 ns

Block1

Dispositional optimism 0.180 0.055 .177*** .020 0.094 0.038 .092* .005 0.088 0.039 .087* .004

Coping self-efficacy 0.023 0.009 .147* .012 0.012 0.007 .078† .003 0.011 0.007 .066

Perceived control of time -0.073 0.195 -.018 -0.105 0.144 -.026 -0.193 0.167 -.048

Social skills -0.044 0.054 -.039 -0.044 0.038 -.039 -0.044 0.038 -.039

Humour 0.028 0.051 .027 -0.030 0.035 -.029 -0.037 0.035 -.036

Egalitarian gender roles 0.187 0.062 .139** .017 0.080 0.043 .060 0.077 0.044 .057† .003

Occupational role reward 0.099 0.070 .079 0.064 0.049 .051 0.064 0.049 .051

Occupational role commitment 0.215 0.060 .197*** .023 0.042 0.043 .038 0.040 0.043 .036



Marital role reward 0.049 0.041 .075 0.005 0.029 .008 0.000 0.029 .000

Marital role commitment 0.018 0.041 .029 0.022 0.029 .035 0.022 0.029 .035

Gender 0.854 0.533 .072 0.200 0.378 .017 0.183 0.384 .016

Age 0.083 0.020 .188*** .030 0.021 0.017 .047 0.019 0.018 .043

Block 2

Affective commitment 0.213 0.036 .211*** .029 0.201 0.037 .200*** .025

Managerial support 0.003 0.020 .005 -0.003 0.020 -.005

Job social support 0.070 0.048 .053 0.067 0.049 .050

Job autonomy 0.050 0.049 .037 0.042 0.050 .031

Skill discretion 0.581 0.036 .587*** .226 0.559 0.037 .565*** .194

Hours per week 0.013 0.013 .034 0.014 0.014 .035

Pref work hours 0.336 0.205 .056 0.274 0.215 .046

Family demands 0.049 0.149 .015 0.087 0.153 .027

Children 0.326 0.186 .090† .003 0.286 0.187 .079

Marital status -0.769 0.369 -.078* .004 -0.734 0.374 -.074† .003

Education -0.380 0.160 -.080* .005 -0.378 0.161 -.080* .005

Block 3


Work-life fit -0.359 0.278 -.055

Feeling busy 0.016 0.126 .005

Negative work-to-family spillover -0.046 0.060 -.032

Positive work-to-family spillover 0.110 0.067 .056

Negative family-to-work spillover -0.092 0.059 -.057



2 = .634, Final model, F(32,399) = 24.367***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

203

the three steps of the regression are shown in Table 2.9, with ΔR2 for each step, and

its F test for each step, across the top of the table. The R2 for the regression model

was large and significant, R2 = .376, F(32,399) = 7.508, p < .001. The adjusted R

2

was .326, which indicates that about one third of the variability of an individual‟s

absorption in their work is predicted by the variables. Specifically, the Individual

Difference variables explained 14.5% (medium effect), with Work and Family

variables adding 20.7% and the Work-Life Interface variables adding another 2.4%

to the variance, which were both significant increments (medium and small effects,

respectively).

At Step 1, only occupational role reward, occupational role commitment and

age were significant, positive predictors of absorption in work. Although age

becomes non- significant after the addition of the Work and Family variables at Step

2, there was no evidence of any mediation that may have occurred. Similarly,

occupational role reward and commitment became less significant after the addition

of the Work and Family and Work-Life Interface variables but there was no evidence

of mediation for either variable. However, these variables remained significant at

Step 3. Skill discretion, job autonomy and affective commitment were again

influential Work variables with skill discretion having a medium-small effect size

(Step 2, sr2 = .088; Step 3, sr

2 = .068). At Step 3, along with occupational role reward

and commitment, the significant predictors of work absorption were greater skill

discretion, greater affective commitment and more job autonomy. There was

evidence that the effects of education (at Step 3, r = .060, β = -.137, p = .003) and

social skills (at Step 3, r = .044, β = -.090, p = .052) have been enhanced by negative

suppression, as the correlations have the opposite sign to the beta weights. The effect

of negative work-to-family spillover was also positively enhanced by suppressor

204

Table 2.9

Results for the three steps for the hierarchical multiple regression for work absorption Variables added Step 1 Step 2 Step 3


2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .145, F(14.417) = 5.043*** .207, F(11,406) = 11.784** .024, F(7,399) = 2.200*

Block 1

Dispositional optimism 0.065 0.051 .073 0.037 0.046 .042 0.024 0.046 .027

Coping self-efficacy 0.009 0.009 .067 0.001 0.008 .009 0.002 0.008 .015

Perceived control of time -0.234 0.180 -.067 -0.262 0.172 -.075 -0.027 0.198 -.008

Social skills -0.060 0.050 -.062 -0.082 0.045 -.084† .005 -0.088 0.045 -.090† .006

Humour 0.010 0.047 .012 -0.015 0.042 -.017 -0.013 0.042 -.015

Egalitarian gender roles -0.004 0.057 -.003 -0.064 0.052 -.054 -0.026 0.052 -.022

Occupational role reward 0.142 0.064 .130* .010 0.140 0.058 .128* .009 0.130 0.058 .119* .008

Occupational role commitment 0.240 0.056 .253*** .038 0.135 0.052 .143** .011 0.117 0.051 .123* .008

Parental role reward -0.028 0.042 -.052 -0.049 0.040 -.090 -0.033 0.040 -.061




Gender 0.851 0.493 .082† .006 0.421 0.452 .041 0.255 0.455 .025

Age 0.066 0.019 .172** .025 0.014 0.021 .037 0.012 0.021 .032

Block 2

Affective commitment 0.129 0.043 .147** .014 0.125 0.044 .143** .013

Managerial support -0.006 0.024 -.012 0.006 0.024 .012


Job autonomy 0.177 0.059 .151** .014 0.160 0.059 .137** .012

Skill discretion 0.315 0.043 .365*** .088 0.288 0.044 .334*** .068

Hours per week -0.007 0.016 -.021 -0.017 0.016 -.051

Pref work hours 0.066 0.245 .013 0.221 0.255 .043

Family demands 0.122 0.178 .043 0.130 0.181 .046

Children 0.282 0.222 .089 0.256 0.221 .081

Marital status -0.439 0.441 -.051 -0.531 0.443 -.062

Education -0.519 0.191 -.126** .012 -0.566 0.191 -.137** .012

Block 3

Satisfaction with WLB -0.026 0.194 -.008

Work-life fit -0.593 0.329 -.103† .005

Feeling busy 0.058 0.149 .021

Negative work-to-family spillover 0.161 0.072 .127* .008


Negative family-to-work spillover -0.133 0.069 -.094† .006

Positive family-to-work spillover -0.034 0.068 -.024

Total R2 = .376, Total Adj R2 = .326, Final model F(32,399) = 7.508***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

205

variables (step 3, r = .080, β = .127, p = .025).

In summary, absorption in work rested with being attached to a job that

allowed the individual to use their skills and creativity, to make their own decisions

and was found in individuals who see their career as salient and important to their

life, although the effects of education, social skills and negative spillover were less

obvious. It could be speculated that individuals with poorer social skills are less

socially capable and spend more time at work to compensate and that education,

whilst opening opportunities, may make the individual more aware of non-work

interests. Negative work-to-family spillover may also lead the individual to spend

more time at work as work problems may be easier to solve than family problems or

work was more salient than the family.

2.3.12 Depression

The next three hierarchical multiple regressions will examine the mental

illnesses, depression, anxiety and stress. The first of these assessed the effect of the

three blocks of variables on depression. The results of the three steps are shown in

Table 2.10, with the ΔR2 for each step and the F test for each step across the top of

the table. The R2 for the model was large and significant, R

2 = .448, F(32,399) =

10.013, p < .001. The adjusted R2 was .403, which indicates that just under half of

the variability in the individual‟s depression was accounted for by the variables.

Specifically, the Individual Difference variables explained 37.3% of the variance

(large effect), Work and Family variables added 2.2%, which was a small, non-

significant increase and the Work-Life Interface variables added 5.3%, which was a

small and significant increase to the explained variance in depression.

At Step 1, about half of the Individual Difference variables were significant,

negative predictors of depression, with a lack of coping self-efficacy being the

206

Table 2.10

Results for the three steps for the hierarchical multiple regression for depression


At each step ΔR2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2

.373, F(11,417) = 17.733*** .022, F(11,406) = 1.320 ns .053, F(7,399) = 5.5445***

Block 1

Dispositional optimism -0.309 0.069 -.220*** .030 -0.292 0.070 -.208*** .026 -0.293 0.068 -.209*** .025

Coping self-efficacy -0.093 0.012 -.421*** .096 -0.092 0.012 -.415*** .085 -0.080 0.012 -.362*** .058

Perceived control of time -0.529 0.245 -.095* .007 -0.512 0.264 -.092† .006 0.139 0.296 .025

Social skills 0.118 0.068 .076† .005 0.127 0.069 .082† .005 0.093 0.068 .060

Humour 0.139 0.063 .097* .007 0.157 0.064 .110* .009 0.160 0.063 .112* .009

Egalitarian gender roles -0.192 0.077 -.103* .009 -0.191 0.079 -.103* .009 -0.134 0.078 -.072† .004

Occupational role reward 0.179 0.087 .103* .006 0.156 0.089 .090† .005 0.129 0.087 .075

Occupational role commitment -0.030 0.076 -.020 -0.004 0.079 -.003 -0.011 0.077 -.007


Parental role commitment -0.109 0.051 -.140* .007 -0.085 0.052 -.110 -0.088 0.050 -.114† .004

Marital role reward -0.019 0.051 -.021 -0.019 0.053 -.021 -0.004 0.052 -.004


Gender -0.372 0.670 -.023 -0.089 0.693 -.005 -0.437 0.679 -.027

Age 0.007 0.026 .011 0.044 0.032 .072 0.052 0.031 .085† .004

Block 2


Managerial support -0.061 0.036 -.081† .004 -0.027 0.036 -.036


Job autonomy 0.026 0.091 .014 0.015 0.088 .008

Skill discretion -0.081 0.065 -.059 -0.036 0.065 -.026

Hours per week 0.016 0.025 .030 0.002 0.024 .003

Pref work hours 0.384 0.375 .047 0.610 0.380 .074

Family demands -0.060 0.274 -.013 -0.304 0.271 -.068

Children -0.606 0.340 -.120† .005 -0.549 0.331 -.109† .004

Marital status 0.521 0.676 .038 0.341 0.661 .025

Education 0.365 0.294 .056 0.226 0.285 .034

Block 3


Work-life fit 0.190 0.492 .021

Feeling busy -0.018 0.223 -.004



Negative family-to-work spillover 0.432 0.103 .193*** .024



2 = .403, Final model F(32,399) = 10.103**

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

207

strongest predictor with a medium effect size (sr2 = .096). As the addition of the

Work and Family variables did not lead to a significant increment in variance, they

will not be considered further, whilst the Work-Life Interface variables significantly

increased the variance explained. At Step 3, the significant predictors were a lack of

coping self-efficacy and less dispositional optimism, higher levels of negative

family-to-work spillover and negative work-to-family spillover and more humour

used as a coping strategy. There were no variables that showed the influence of

suppressor variables in the analysis. In summary, depression in this current sample

was linked to a lack of confidence in managing difficult situations, lacking positive

expectations for the future and having problems and worries, mostly at home, that

spilt over into other areas of life, rather then the conditions at work or home per se.

Using humour to cope may indicate that this type of humour is used when life is

„blue‟, rather than in more cheerful situations.

2.3.13 Anxiety

The second hierarchical multiple regression on mental illness assessed the

effects of the three blocks of variables on the individual‟s level of anxiety. The

results of the three steps are shown in Table 2.11, with the ΔR2 for each step, and the

F test for each step across the top of the table. The R2 for the model was medium-

large and significant, R2 = .289, F(32,399) = 5.604 , p <.001. The adjusted R

2 was

.232 which indicates that just under one quarter of the variability in the individual‟s

anxiety was accounted for by the blocks of variables. This was the least variance

explained by any of the regression models, which is in turn reflected in the

significant predictors having only small (or slightly more) effect sizes. Specifically,

Individual Difference variable accounted for 19.1% of the variance, the Work and

Family variables accounted for 3.0% and the Work-Life Interface variables

208

Table 2.11

Results for the three steps for the hierarchical multiple regression for anxiety


At each step ΔR2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .191, F(14,417) = 7.011*** .030, F(11,406) = 1.417 ns .068, F(7,399) = 5.479***

Block 1

Dispositional optimism -0.179 0.070 -.142* .013 -0.134 0.072 -.107† .007 -0.153 0.070 -.121* .009

Coping self-efficacy -0.038 0.012 -.189** .019 -0.045 0.012 -.227*** .026 -0.037 0.013 -.189** .016

Perceived control of time -0.764 0.251 -.153** .018 -0.851 0.269 -.171** .019 -0.248 0.302 -.050

Social skills 0.177 0.069 .126* .013 0.192 0.071 .137** .014 0.144 0.069 .103* .008

Humour 0.009 0.065 .007 0.031 0.066 .025 0.010 0.064 .007

Egalitarian gender roles -0.149 0.079 -.089† .007 -0.136 0.081 -.081† .005 -0.076 0.080 -.045

Occupational role reward 0.185 0.089 .119* .008 0.187 0.091 .120* .008 0.146 0.089 .094

Occupational role commitment -0.051 0.078 -.037 -0.044 0.081 -.032 -0.052 0.078 -.038


Parental role commitment -0.080 0.052 -.114 -0.064 0.053 -.091 -0.072 0.051 -.103

Marital role reward -0.001 0.052 -.001 -0.007 0.054 -.008 -0.003 0.053 -.003

Marital role commitment 0.003 0.052 .004 -0.006 0.054 -.007 -0.001 0.053 -.001

Gender 0.111 0.685 .008 -0.156 0.708 -.011 -0.769 0.693 -.052

Age -0.041 0.026 -.074 -0.054 0.032 -.099† .005 -0.045 0.032 -.082

Block 2


Managerial support -0.075 0.037 -.111* .008 -0.045 0.037 -.066


Job autonomy 0.084 0.093 .050 0.039 0.090 .023

Skill discretion -0.037 0.067 -.030 -0.041 0.067 -.033

Hours per week 0.027 0.025 .055 0.008 0.025 .017

Pref work hours 0.467 0.383 .063 0.578 0.388 .078

Family demands -0.468 0.279 -.116† .005 -0.671 0.276 -.167* .011

Children 0.233 0.347 .051 0.202 0.338 .044

Marital status 0.268 0.690 .022 0.020 0.675 .002

Education -0.471 0.300 -.080 -0.638 0.291 -.108* .009

Block 3


Work-life fit -0.466 0.502 -.057

Feeling busy 0.235 0.228 .059


Positive work-to-family spillover 0.318 0.121 .131** .012

Negative family-to-work spillover 0.306 0.106 .152** .015



2 = .232, Final model F(32,399) = 5.064***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

209

accounted for 6.8% of the variance of anxiety. The Individual Difference and Work-

Life Interface variables represented significant increments in variance whilst the

change due to the Work and Family variables was not significant.

At Step 1, coping self-efficacy, dispositional optimism, perceived control of

time, social skills and occupational role reward were significant, negative predictors

of anxiety. Although coping self-efficacy was the strongest predictor, its effect size

was only a little above „small‟, sr2 = .019. As with depression, the addition of the

work and family variables did not significantly change the variance of the model and

were not considered further. However, in Step 3, two of the Work and Family

variables, education and family demands were significant predictors. Education was

again under the influence of suppressor variables and the effect of family demands

appeared to be strengthened by the Work-Life interface variables. Further evidence

of suppressor variables enhancing the effectiveness of variables was shown by social

skills and by positive work-to-family spillover. At Step 3, the significant predictors

of anxiety were a lack of coping self-efficacy, less dispositional optimism, higher

levels of both negative family-to-work spillover and negative work-to-family

spillover and as noted, social skills, positive work-to-family spillover, education and

family demands.

In summary, anxiety in this study appeared to be linked to the management of

problems at work and at home rather than work or family situations per se.

Individuals who were more anxious felt less capable of managing difficult situations,

had less optimistic expectations for the future and had more negative spillover in

both directions between work and family. They also had less family responsibilities

and more education and paradoxically, better social skills and more positive spillover

(i.e. there were benefits from work which may help at home).

210

2.3.14 Stress

The final hierarchical multiple regression for mental illness examined the

effects of the three blocks of variables on the individual‟s level of stress. The results

of the three steps of the analysis are shown in Table 2.12, with the ΔR2 for each step,

and the F test for each step across the top of the table. The R2 for the model was large

and significant R2 = .455, F(32,399) = 10.417, p < .001. The adjusted R

2 was .411

which indicated that just under half of the variability in the individual‟s depression

was accounted for by the blocks of variables. Specifically, the Individual Difference

variables accounted for 27.6% of the variance in stress, the Work and Family

variables accounted for a small and non-significant increment of 2.9%, whilst the

Work-Life Interface had a medium effect of 15.0% on the variance, a significant

increase. As was the case for depression and anxiety, the Work and Family variables

did not add to the prediction of anxiety and will not be further discussed. At Step 1,

coping self-efficacy and perceived control of time were the strongest negative

predictors, having medium-small effect sizes (sr2 = .051, sr

2 = .057, respectively),

with social skills and egalitarian gender roles having smaller contributions. The

addition of the Work-Life Interface variables substantially decreased the beta and

significance of perceived control of time, suggesting mediation. The bootstrap

method of multiple mediators (Preacher & Hayes, 2008) was used to assess if the

effect of perceived control of time on stress was mediated by negative work-to-

family spillover, negative family-to-work spillover and feeling busy, as a simplified

test of the possible mediation. The mediation pathway through „feeling busy‟ was not

significant (i.e. the estimates for the confidence interval included zero). However, the

indirect paths through negative work-to-family spillover (Z = -6.67, p < .001) and

through negative family-to-work spillover (Z = -3.87, p < .001) were significant,

211

Table 2.12

Results for the three steps for the hierarchical multiple regression for stress



2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .276, (F(14,417) = 11.369*** .029, F(11,406) = 1.530 ns .150, F(7,399) = 15.706***

Block 1

Dispositional optimism -0.053 0.088 -.032 -0.039 0.090 -.024 -0.073 0.081 -.044

Coping self-efficacy -0.080 0.015 -.306*** .051 -0.087 0.015 -.329*** .054 -0.077 0.015 -.291*** .038

Perceived control of time -1.792 0.314 -.272*** .057 -1.590 0.337 -.241*** .038 -0.055 0.350 -.008

Social skills 0.259 0.087 .140** .015 0.267 0.088 .144** .016 0.190 0.080 .103* .008

Humour 0.016 0.081 .009 0.014 0.082 .008 0.010 0.074 .006

Egalitarian gender roles -0.053 0.099 -.024 -0.076 0.101 -.034 0.065 0.093 .029

Occupational role reward 0.250 0.112 .121* .009 0.252 0.114 .122* .008 0.176 0.103 .085† .004

Occupational role commitment 0.003 0.097 .002 -0.022 0.101 -.013 -0.045 0.091 -.025



Marital role reward -0.098 0.066 -.091 -0.119 0.068 -.110† .005 -0.086 0.061 -.079

Marital role commitment 0.125 0.065 .119† .006 0.107 0.068 .102 0.093 0.062 .089

Gender 0.077 0.858 .004 0.363 0.885 .019 -0.742 0.803 -.038

Age -0.032 0.033 -.044 -0.041 0.041 -.056 -0.013 0.037 -.018

Block 2


Managerial support -0.135 0.046 -.150** .014 -0.055 0.042 -.061

Job social support 0.125 0.113 .057 0.232 0.103 .106* .007

Job autonomy 0.126 0.116 .057 0.066 0.104 .030

Skill discretion 0.068 0.083 .042 0.095 0.077 .058

Hours per week 0.059 0.031 .091† .006 0.019 0.029 .029

Pref work hours 0.158 0.479 .016 0.859 0.450 .087† .005

Family demands 0.057 0.349 .011 -0.312 0.320 -.058

Children -0.141 0.435 -.023 -0.117 0.391 -.019

Marital status 0.668 0.863 .041 -0.143 0.782 -.009

Education -0.020 0.375 -.002 -0.321 0.337 -.041

Block 3

Satisfaction with WLB -0.566 0.342 -.091† .004

Work-life fit -0.223 0.582 -.021

Feeling busy 0.688 0.264 .131** .009

Negative work-to-family spillover 0.701 0.126 .293*** .042


Negative family-to-work spillover 0.503 0.122 .188*** .023



2 = .411, Final model F(32,398) = 10.417***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

212

indicating mediation had occurred, and the confidence intervals around the estimates

of the indirect effects did not include zero.

At Step 3, the significant predictors of stress were more negative work-to-

family spillover, less coping self-efficacy, more negative family-to-work spillover,

feeling busy and having better social skills, which was enhanced by the presence of

suppressor variables. Managerial support belonged to the non-significant Work and

Family variables and will not be considered to avoid Type 1 errors. In summary,

stress is linked to feeling that life is hectic and the individual was not able to manage

difficult situations, particularly the problems that spill over in both directions

between work and family, which lessened their feelings of controlling their time

2.3.15 Emotional exhaustion

The last set of the hierarchical multiple regressions examined burnout, with

the first to examine the effect of the three blocks of variables on emotional

exhaustion. The results of the three steps of the analyses are shown in Table 2.13,

with the ΔR2 for each step, and the F test for each step across the top of the table.

The R2 for the model was very large and significant, R

2 = .601, F(32,399) = 12.815,

p < .001. The adjusted R2 was .569 which indicates that over half of the variability in

the individual‟s emotional exhaustion was accounted for by the blocks of variables.

Specifically, the Individual Difference variables accounted for 30.1% of the variance

in emotional exhaustion, the Work and Family variables accounted for 13.5%, whilst

the Work-Life Interface accounted for 16.5% of the variance, both of which were

medium effect sizes and significant increases in the explained variance of emotional

exhaustion.

At Step 1, control of time and egalitarian gender role attitudes, with a small

contribution from coping self-efficacy, were the significant negative predictors, with

213

control of time having a slightly greater than medium effect size (sr2 = .114). At step

2, control of time had a reduced input, with affective commitment, managerial

support, preferred working hours and hours per week being significant negative

predictors. However, it was the addition of the Work-Life Interface variables at Step

3 that altered the contributions of the predictor variables, suggesting mediation, in

particular by negative work-to-family spillover (sr2 = .091). Using the bootstrap

method of multiple mediator analyses (Preacher & Hayes, 2008), the possible

mediation of negative work-to-family spillover and negative family-to-work

spillover on the effects of the formerly significant predictors, coping self-efficacy,

perceived control of time, egalitarian gender role attitudes, managerial support, hours

per week and preferred working hours on emotional exhaustion were assessed.

Work-family fit was not included as it did not show evidence of being a mediator.

There was support for mediation occurring, although not for working hours

and preference for working hours (i.e. the confidence intervals for the indirect paths

included zero). The indirect paths through negative work-to-family spillover to

emotional exhaustion were significant for perceived control of time (Z = -9.157, p <

.001), coping self-efficacy (Z = -5.508, p < .001), egalitarian gender role attitudes (Z

= -4.000, p < .001) and managerial support (Z = -6.630, p < .001) with the

confidence intervals around the estimates of the indirect effects not including zero

for any of these paths. The indirect paths through negative family-to-work spillover

to emotional exhaustion were significant for perceived control of time (Z = -2.362, p

= .018), coping self-efficacy (Z = -2.440, p = .015), managerial support (Z = -2.511,

p = .012) and egalitarian gender role attitudes (Z = -1.901, p = .057). These paths

were not as strong as for negative work-to-family spillover and whilst the indirect

path from egalitarian gender role attitudes approached significance, the confidence

214

Table 2.13

Results for the three steps for the hierarchical multiple regression for emotional exhaustion



2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .301, F(14,417) = 12.815*** .135, F(11,406) = 8.852*** .165, F(7,399) = 23.494***

Block 1

Dispositional optimism -0.056 0.057 -.051 -0.035 0.054 -.032 -0.056 0.046 -.051

Coping self-efficacy -0.020 0.010 -.115* .007 -0.019 0.009 -.108* .006 -0.010 0.008 -.058

Perceived control of time -1.689 0.204 -.386*** .114 -1.120 0.201 -.256*** .043 -0.198 0.199 -.045

Social skills 0.067 0.057 .055 0.095 0.053 .077† .004 0.073 0.045 .060

Humour -0.072 0.053 -.064 -0.062 0.049 -.055 -0.040 0.042 -.036

Egalitarian gender roles -0.196 0.065 -.134** .016 -0.169 0.060 -.115** .011 -0.041 0.053 -.028

Occupational role reward 0.026 0.073 .019 0.039 0.068 .028 -0.002 0.058 -.002

Occupational role commitment -0.018 0.063 -.015 0.025 0.060 .021 -0.006 0.051 -.005

Parental role reward -0.073 0.048 -.107 -0.071 0.046 -.105 -0.032 0.040 -.047


Marital role reward -0.024 0.043 -.033 -0.022 0.040 -.031 0.001 0.035 .001


Gender 0.166 0.559 .013 0.910 0.528 .070† 0.317 0.456 .024

Age -0.005 0.021 -.011 0.004 0.024 .008 0.012 0.021 .026

Block 2

Affective commitment -0.196 0.051 -.179*** .021 -0.167 0.044 -.151*** .014

Managerial support -0.102 0.028 -.171*** .019 -0.052 0.024 -.087* .005

Job social support -0.040 0.067 -.027 0.074 0.058 .051

Job autonomy -0.072 0.069 -.049 -0.102 0.059 -.070† .003

Skill discretion 0.013 0.050 .012 0.026 0.044 .024

Hours per week 0.046 0.019 .106* .008 0.017 0.016 .040

Pref work hours -0.922 0.286 -.142** .014 -0.455 0.255 -.070† .003

Family demands 0.109 0.209 .031 -0.050 0.182 -.014

Children -0.157 0.259 -.039 -0.081 0.222 -.020

Marital status 0.656 0.515 .061 0.231 0.444 .021

Education 0.047 0.224 .009 -0.158 0.191 -.031

Block 3

Satisfaction with WLB -0.370 0.194 -.090† .004

Work-life fit -0.668 0.330 -.093* .004

Feeling busy -0.178 0.150 -.051



Negative family-to-work spillover 0.176 0.069 .099* .006



2 = .569, Final model F(32,399) = 18.755***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

215

intervals did not include zero.

At Step 3, higher levels of negative work-to-family spillover were the most

significant predictor of emotional exhaustion. The other significant predictors were a

lack of affective commitment, a lack of managerial support (over and above the

partial mediation by negative spillover in both directions), a lack of work-life and

negative family-to-work spillover.

In summary, emotional exhaustion among the participants was increased by

the negative spillover between work and family domains and a lack of attachment to

one‟s workplace, with negative spillover increased by the absence of a number of

personal and workplace supports. Perceived control of time, coping self-efficacy,

egalitarian gender role attitudes and managerial support were significant, negative

predictors of emotional exhaustion, but their effects are exerted through negative

spillover from both work to home and home to work. As such, the absence of a sense

of being able to control time commitments, a lack of confidence to deal with difficult

situations, a lack of support from ones‟ manager for work and family responsibilities

(i.e. a lack of managerial support) and feeling that gender roles are not based on

merit (i.e. egalitarian gender role were not supported) would increase the negative

spillover that was experienced between work and family domains which would in

turn increase emotional exhaustion.

2.3.16 Cynicism

The second hierarchical multiple regression on burnout will examine the

effect of the three blocks of variables on the individual‟s level of cynicism. The

results of the three steps of the regression are shown in Table 2.14, with the ΔR2, and

its F test, across the top of the table. The R2 for the overall regression model was

very large and significant, R2 = .548, F(32, 399) = 15.122, p < .001. The adjusted R

2

216

was .512, which indicates that just over half of the variability of an individual‟s level

of cynicism is explained by the variables. Specifically, the Individual Difference

variables (22.0%) and Work and Family variables (29.0%) had similar, medium-

large effects on the variance of cynicism, with the Work-Life Interface variables

adding a small (3.8%), significant increment to the explanation of cynicism.

At Step 1, dispositional optimism, perceived control of time, egalitarian

gender role attitudes and occupational role commitment were significant negative

predictors of cynicism, with small effects from each variable (e.g. sr2 = .022 for

egalitarian gender role attitudes). The importance of Step 2, the addition of the Work

and Family variables, was shown by the medium-large increase in variance explained

and the effect sizes of the individual predictors, for example affective commitment

(sr2 = .089) and skill discretion (sr

2 = .033), with the reduction in contributions from

age and occupational role commitment to the understanding of cynicism. The

decrease in the significance of perceived control of time and occupational role

commitment as the subsequent blocks of variables were added suggested mediation,

although there was no evidence of this for occupational role commitment. Following

the bootstrap method for multiple mediators (Preacher & Hayes, 2008), the effect of

perceived control of time on cynicism was mediated by both affective commitment

(Z = -1.996, p = .046) and negative work-to- family spillover (Z = -5.990, p <.001),

and the 95% confidence intervals around the estimates of the direct effects did not

include zero. The link between not feeling in control of your time and greater

cynicism was exerted through less attachment to the workplace and greater negative

spillover between work and home. At Step 3, the significant predictors of cynicism

were a lack of affective commitment and skill discretion, negative work-to-family

217

Table 2.14

Results for the three steps for the hierarchical multiple regression for cynicism



2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .220, F(14,417) = 8.400*** .290, F(11,406) = 21.852*** .038, F(7,399) = 4.792***

Block 1

Dispositional optimism -0.169 0.058 -.161** .016 -0.117 0.048 -.111* .007 -0.119 0.046 -.113* .007

Coping self-efficacy -0.017 0.010 -.101† .006 -0.011 0.008 -.066 -0.008 0.008 -.048

Perceived control of time -0.532 0.205 -.128* .013 -0.238 0.178 -.057 0.042 0.201 .010

Social skills 0.017 0.057 .015 0.054 0.047 .046 0.057 0.046 .049

Humour -0.063 0.053 -.059 -0.024 0.044 -.023 -0.005 0.043 -.004

Egalitarian gender roles -0.224 0.065 -.160** .022 -0.172 0.054 -.124** .013 -0.127 0.053 -.091* .006

Occupational role reward 0.051 0.073 .039 0.068 0.060 .053 0.056 0.059 .043

Occupational role commitment -0.165 0.064 -.146* .013 0.000 0.053 .000 -0.008 0.052 -.007

Parental role reward -0.004 0.048 -.006 -0.011 0.041 -.017 0.000 0.040 .001




Gender -0.276 0.561 -.022 0.472 0.468 .038 0.339 0.461 .028

Age -0.052 0.021 -.115* .011 0.022 0.021 .049 0.026 0.021 .057

Block 2

Affective commitment -0.385 0.045 -.368*** .089 -0.355 0.044 -.339*** .073

Managerial support -0.031 0.025 -.056 -0.015 0.024 -.027


Job autonomy -0.154 0.061 -.111* .008 -0.154 0.060 -.110* .008

Skill discretion -0.229 0.044 -.223*** .033 -0.207 0.044 -.201*** .025

Hours per week -0.003 0.017 -.007 -0.010 0.016 -.024

Pref work hours -0.455 0.253 -.073 -0.286 0.258 -.046

Family demands 0.090 0.185 .027 0.050 0.184 .015

Children -0.584 0.230 -.154* .008 -0.510 0.224 -.135* .006

Marital status 0.734 0.456 .072 0.578 0.449 .056

Education 0.310 0.198 .063 0.245 0.194 .050

Block 3


Work-life fit 0.037 0.334 .005

Feeling busy -0.278 0.151 -.084† .004


Positive work-to-family spillover -0.148 0.080 -.073† .004

Negative family-to-work spillover 0.107 0.070 .064



2 = .512, Final Model, F(32,399) = 15.122***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

218

spillover, lower levels of dispositional optimism and less egalitarian gender role

attitudes. In addition, having children was some protection against cynicism.

In summary, cynicism among the participants suggested a jaded view of work

and life. Lacking an attachment to their work, having fewer opportunities to use their

talents, feeling less optimistic about the future and with negative effects spilling from

work to home, cynicism appears to make the individual less hopeful and less

directed. Children may give protection from cynicism by virtue of providing a

different, longer term perspective to life.

2.3.17 Professional efficacy

The last of the hierarchical multiple regressions for burnout examined the

effects of the three blocks of variables on the individual‟s sense of professional

efficacy. The results of the three steps are shown in Table 2.15, with the ΔR2, and its

F test, across the top of the table. The R2 of the final regression model was medium-

large and significant, R2 = .308, F(32, 399) = 5.562, p < .001. The adjusted R

2 was

.253 and indicated that a quarter of the variability professional efficacy was

explained by the variables, which was the second lowest amount of variance

explained by the regression models. As with anxiety with the least explained

variance, the significant predictors had only small effect sizes. For the blocks of

variables, Individual Difference variables had a medium affect and accounted for

17.1 % of professional efficacy, the Work and Family added 10.9% (a medium

effect) and the Work-Life Interface variables added 2.8% (a small effect) to the

explanation of professional efficacy, both of which were significant increments to the

explanation of professional efficacy.

At Step 1, egalitarian gender role attitudes, coping self-efficacy, occupational

role reward and age were significant positive predictors of professional efficacy

219

Table 2.15

Results for the three steps for the hierarchical multiple regression for professional efficacy



2 ΔR

2 B (SE of B) β sr

2 ΔR

2 B (SE of B) β sr

2


2 .171, F(14,417) = 6.153*** .109, F(11,406) = 5.614*** .028, F(7,399) = 2.295*

Block 1

Dispositional optimism 0.075 0.039 .108† .007 0.059 0.038 .085 0.048 0.038 .069

Coping self-efficacy 0.018 0.007 .161** .014 0.013 0.007 .122* .007 0.010 0.007 .088

Perceived control of time 0.123 0.140 .045 0.047 0.143 .017 0.145 0.164 .053

Social skills 0.022 0.039 .029 -0.002 0.037 -.002 -0.013 0.038 -.017

Humour 0.034 0.036 .048 0.023 0.035 .033 0.014 0.035 .019

Egalitarian gender roles 0.133 0.044 .144** .018 0.095 0.043 .103* .009 0.103 0.043 .111* .010

Occupational role reward 0.139 0.050 .162** .015 0.133 0.048 .155** .013 0.119 0.048 .139* .011

Occupational role commitment 0.004 0.043 .006 -0.055 0.043 -.074 -0.056 0.043 -.075





Gender 0.119 0.382 .015 -0.008 0.375 -.001 -0.228 0.377 -.028

Age 0.033 0.015 .110* .010 0.021 0.017 .069 0.025 0.017 .081

Block 2

Affective commitment 0.099 0.036 .144** .014 0.089 0.036 .129* .011

Managerial support 0.003 0.020 .007 0.008 0.020 .020


Job autonomy 0.157 0.049 .170** .018 0.135 0.049 .147** .013

Skill discretion 0.102 0.035 .150** .015 0.075 0.036 .110* .007

Hours per week 0.004 0.013 .015 -0.001 0.013 -.004

Pref work hours 0.142 0.203 .035 0.202 0.211 .049

Family demands 0.260 0.148 .117† .005 0.293 0.150 .132† .007

Children -0.139 0.184 -.056 -0.172 0.184 -.069

Marital status -0.522 0.366 -.077 -0.700 0.367 -.103† .006

Education -0.117 0.159 -.036 -0.131 0.158 -.040

Block 3


Work-life fit -0.051 0.273 -.011

Feeling busy 0.312 0.124 .142* .011

Negative work-to-family spillover 0.074 0.059 .074

Positive work-to-family spillover 0.114 0.066 .085† .005

Negative family-to-work spillover -0.095 0.057 -.086† .005


Total R2 = .308, Total Adj R2 = .253, Final Model F(32,399) = 5.562***

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

220

with slightly above small effect sizes (e.g. sr2 = .018 for egalitarian gender role

attitudes). The addition of the Work and Family variables showed that the

availability of workplace resources were important to professional efficacy and these

remain significant when the Work-Life Interface variables were added. At Step 3, the

significant predictors of professional efficacy were higher levels of the workplace

resources, job autonomy, affective commitment and skill discretion, along with

feeling busy, finding the occupational role rewarding and supporting egalitarian

gender role attitudes.

The decrease in the significance of coping self-efficacy suggested that

mediation of its effect on professional efficacy had occurred. Using the bootstrapping

method of multiple mediators (Preacher & Hayes, 2008), the possible mediators of

job autonomy, affective commitment and feeling busy were tested. The results found

that the effect of coping self-efficacy was mediated by job autonomy, as the indirect

path through was significant (Z = 3.981, p < .001) and the 95% confidence intervals

around the estimate of the indirect effect did not include zero.

In summary, the participants‟ professional efficacy was linked to the

availability of workplace resources and their use. Participants with greater

professional efficacy can feel free to make their own decisions (i.e. job autonomy),

they feel busy and active, using their skills and talents in a workplace that they are

attached to, in occupations that they feel are interesting and rewarding and where

everyone is considered equal (i.e. egalitarian gender role attitudes are supported).

2.3.18 Summary of the significant predictors of the hierarchical multiple regressions

The significant predictors of the outcomes of the preceding hierarchical

multiple regressions were many and varied, although there were some patterns that

did emerge. Table 2.16 brought together the beta weights from each regression

221

model, with the significant predictors highlighted in bold to make them easier to see.

It was hypothesized that the individual with a more generative disposition and more

positive demand characteristics, with more workplace and family resources would be

predictive of better psychological functioning, which was generally supported,

although more specific hypotheses about individual predictors of the outcomes were

harder to qualify. However, Table 2.16 makes it clear that whilst the preceding

regressions tested a large number of variables, only a small number of predictors

were consistently important across all the outcomes. These were personal and

workplace resources (Hobfoll, 2002) that the individual could use to meet the

challenges of work and family issues and which underpinned their overall

functioning.

From P, the Person (i.e. the Individual Difference variables), only

dispositional optimism (8 of the 12 outcomes) and coping self-efficacy (6 of the 12

outcomes) were among the most frequent predictors, being particularly important for

the well-being and mental illness outcomes. Perceived control of time and egalitarian

gender role attitudes were initially important but were mediated by negative spillover

in both directions for a number of outcomes. Perhaps not surprisingly, neither marital

role reward nor commitment were important for these outcomes and may be

significant predictors for analyses that focus more on relationships. Humour was not

a significant predictor except for depression and this puzzling lack of predictiveness

was examined in the post hoc analyses in the following section. Gender also was

surprisingly not a significant predictor, apart from women reporting higher work

satisfaction than men. Whilst men and women may have different roles and

responsibilities, in this sample they did not differ on their well-being, their

engagement in work or their levels of mental illness and burnout.

222

From C, the context of the Work and Family variables, the workplace

resources of skill discretion (8 of the 12 outcomes), affective commitment (7 of 12

outcomes), job autonomy (4 of the 12 outcomes) and education (4 of the 12

outcomes) were among the important, frequent predictors. However, the effect of

education on the outcomes was often enhanced by suppressor variables, which may

not occur again in later analyses, limiting its explanatory power. Job autonomy,

although only predicting a small number of outcomes, was particularly important to

the understanding of burnout and work engagement. Of limited value to the

regression models were some surprising variables. Managerial support, job social

support, working hours, family demands and number of children are often considered

important to working adults but this was not the case in these analyses. Particularly

for managerial support and job social support, in part due to the mediation of their

effects by negative spillover.

From the Work-Life Interface variables, negative work-to-family spillover (9

of the 12 outcomes) and negative family-to-work spillover (5 out of the 12 outcomes)

were the most important predictors with limited input from positive spillover scales,

work family fit and balance and how busy a person felt. Whilst positive work-to-

family spillover did not contribute significantly to the outcomes, positive family-to-

work spillover, capturing a supportive home environment was important to the

overall assessment of well-being, contributing strongly to both life satisfaction and

psychological well-being. From the mediation analyses within the previous

regressions, negative work-to-family spillover mediated between a number of

predictors and the outcomes, for example, for both perceived control of time and

managerial support to emotional exhaustion. As both negative spillover scales were

influential in understanding the outcomes, a post hoc investigation of the predictors

223

Table 2.16

Summary of standardized regression weights (β) of the predictor variables for the hierarchical multiple regressions .

Well-being Work engagement Mental illness Burnout .

Variables entered in Life Psychological Work Professional

Each block Satisfaction WB Satisfaction Vigour Dedication Absorption Depression Anxiety Stress Exhaustion Cynicism Efficacy

Block 1

Dispositional optimism .180*** .166*** .103* .123** .087* .027 -.209*** -.121* -.044 -.051 -.113* .069

Coping self-efficacy .168** .302*** .008 .178** .066 .015 -.362*** -.189** -.291*** -.058 -.048 .088

Perceived control of time .123* .050 .082 -.030 -.048 -.008 .025 -.050 -.008 -.045 .010 .053

Social skills .075† .127** .004 -.011 -.039 -.090† .060 .103* .103* .060 .049 -.017

Humour .039 .040 -.039 -.009 -.036 -.015 .112* .007 .006 -.036 -.004 .019

Egalitarian gender roles .046 .106** .058 .023 .057† -.022 -.072† -.045 .029 -.028 -.091* .111*

Occupational role reward .031 .055 -.006 .078 .051 .119* .075 .094 .085† -.002 .043 .139*

Occupational role commitment -.056 -.082† -.077 .107* .036 .123* -.007 -.038 -.025 -.005 -.007 -.075

Parental role reward -.054 -.115† .154* .013 .027 -.061 .109 .107 .044 -.047 .001 .028

Parental role commitment .168** .148** -.121† .020 .007 .030 -.114† -.103 -.043 -.038 -.053 .047

Marital role reward .049 -.004 .053 -.027 .000 .040 -.004 -.003 -.079 .001 .073 .050

Marital role commitment .026 .092† -.064 -.015 .035 -.051 -.015 -.001 .089 -.056 -.051 -.075

Gender .032 .049 .100* .032 .016 .025 -.027 -.052 -.038 .024 .028 -.028

Age -.015 .014 -.102 .149** .043 .032 .085† -.082 -.018 .026 .057 .081

Block 2

Affective commitment -.013 -.012 .288*** .125** .200*** .143** -.029 -.041 -.024 -.151*** -.339*** .129*

Managerial support .095* .027 .006 .058 -.005 .012 -.036 -.066 -.061 -.087* -.027 .020

Job social support -.044 -.028 .070 -.037 .050 -.004 .024 .077 .106* .051 -.001 .009

Job autonomy .082 .052 .068 .103* .031 .137** .008 .023 .030 -.070† -.110* .147**

Skill discretion .086* .098* .181*** .139** .565*** .334*** -.026 -.033 .058 .024 -.201*** .110*

Hours per week .012 .051 -.007 .084* .035 -.051 .003 .017 .029 .040 -.024 -.004

Pref work hours .012 -.034 .001 .148** .046 .043 .074 .078 .087† -.070† -.046 .049

Family demands .048 -.026 .053 .031 .027 .046 -.068 -.167* -.058 -.014 .015 .132†

Children -.104† .015 .065 .096 .079 .081 -.109† .044 -.019 -.020 -.135* -.069

Marital status .047 -.026 -.052 -.025 -.074† -.062 .025 .002 -.009 .021 .056 -.103†

Education -.026 .115** .028 -.022 -.080* -.137** .034 -.108* -.041 -.031 .050 -.040

Block 3

Satisfaction with WLB .177*** .007 .216*** .009 .051 -.008 -.082 .025 -.091† -.090† -.064 .055

Work-life fit .004 .059 -.012 .012 -.055 -.103† .021 -.057 -.021 -.093* .005 -.011

Feeling busy .035 .043 .085† .081† .005 .021 -.004 .059 .131** -.051 -.084 .142*

Negative WF Spillover .037 -.117* -.125* -.120* -.032 .127* .133* .143* .293*** .430*** .197*** .074

Positive WF Spillover -.034 -.039 .076† -.002 .056 .053 .002 .131** .030 -.023 -.073† .085†

Negative FW Spillover -.052 -.002 -.003 -.132** -.057 -.094† .193*** .152** .188*** .099* .064 -.086†

Positive FW Spillover .205*** .199*** -.077† .062 .009 -.024 -.042 -.017 .016 -.019 .013 .067

Note. † p < .100, * p < .050, ** p < .010, ***p < .001

Note. WF = work-to-family; FW = family-to-work

224

of both negative work-to-family spillover and negative family-to-work spillover were

conducted, following the post hoc analyses of moderation, humour and gender.

Generally, the individual‟s confidence in the future and their capabilities,

where they are attached to a job that allows them to use their skills and talents and

make their own decisions, without too many problems or tiredness spilling over

between work and home domains can reasonably predict greater well-being, better

mental health, less burnout and more work engagement and work satisfaction.

Specific additional predictors add to specific outcomes. For example, a supportive

home environment and valuing the parenting role adds specifically to greater well-

being, whilst work-life fit do not contribute to mental health or well-being but did

add to feeling less emotional exhaustion. Variables that would be considered

important, such as perceived control of time and managerial support were found to

be mediated by negative spillover removing them from the final list of significant

predictors. The summary of the significant predictors highlighted that both the broad

outline of predictors as well as the fine-grained analyses for each outcome best

explained the individual‟s developmental outcomes.

2.3.19 Post-hoc analysis: Examining moderation between the most common

predictors for the outcomes

Following on from the mediation explored within the regression analyses, the

first step of the post hoc analyses was to consider if there was any moderation

between the variables, to add to the explanatory power of the regression models.

Rather than use all of the predictor variables, the process will be simplified by using

only the most frequent significant predictors identified in the previous section. This

kept the number of comparisons within bounds as well as using only those variables

that have already „proved‟ themselves. The Type I errors were controlled by dividing

225

by the number of actual comparisons (16 in total) made for each outcome to maintain

the „per hypothesis‟ alpha (α < .05/16 < .0031) (J. Cohen et al., 2003). Only an

interaction that was significant at α ≤ .003 was deemed to be significant and the

moderation examined further.

The comparisons for moderation that were considered were between each of

the two individual difference variables (dispositional optimism and coping self-

efficacy) and each of the three workplace resources (affective commitment, job

autonomy and skill discretion) and each of negative work-to-family spillover

(NWFS) and negative family-to-work spillover (NFaWS). These combinations gave

five comparisons for each individual difference variable, as the individual difference

variables were combined in turn with a workplace resource and then with each

spillover scale (10 in total). The three workplace resources were then combined in

turn with each of the negative spillover scales, giving two comparisons for each

resource (6 in total), adding up to the 16 planned comparisons.

The variables were centred and interaction terms calculated from the centred

variables. The hierarchical multiple regressions conducted for the possible

combinations, with the centred variables in Step 1 (for example, Opt-C, NWFS-C,

the centred variables for dispositional optimism and negative work-to-family

spillover) and the interaction term in Step 2, with the criterion variable remaining in

its original form (J. Cohen et al., 2003). Only nine significant interactions were

found. Five involved individual difference variables, with negative spillover in both

directions moderated the effect of optimism on depression, whilst negative family-to-

work spillover moderated the effect of optimism on anxiety. Negative work-to-

family spillover moderated the effect of coping self-efficacy on depression and

anxiety. For the four significant interactions involving the workplace variables,

226

negative work-to-family spillover moderated the effect of skill discretion on

absorption in work and affective commitment on professional efficacy. Negative

family-to-work spillover moderated the effect of job autonomy on both exhaustion

and cynicism.

The results of the moderated regression analyses at Step 2 are shown in Table

2.17, giving the B, SE of B, standardized beta weights (β) and unique variance of the

centred predictors and the interaction terms (sr2). Not only are most of the

interactions between the centred variables significant, but nearly all of the main

effects were significant as well, which would be expected from the regression

analyses. These results indicated that the presence or absence of personal and

workplace resources and negative spillover, as well as their combinations, were

important to the individual‟s experience of mental health, work engagement and

burnout. The simple slopes for high and low levels of the moderating variables,

negative work-to-family spillover (NWFS) and negative family-to-work spillover

(NFaWS) are listed in Table 2.18. All of the simple slopes are significant and are

mostly negative, except for the simple slopes where the outcome was a „positive‟, i.e.

between skill discretion and work absorption and between affective commitment and

professional efficacy. The significant negative slopes indicate that at low levels of

the first variable (i.e. low in a resource), participants would have significantly higher

levels of the criterion variable (i.e. a mental illness or burnout) regardless of the level

of negative spillover, than for an individual who is high in that variable. However,

examination of the simple slopes, shown in Appendix G, Figures G1 to G.6, indicate

that the moderating influence of negative spillover operated differently, whether it

was a personal resource (i.e. dispositional optimism or coping self-efficacy) or a

workplace resource (i.e. affective commitment, job autonomy or skill discretion).

227

Table 2.17

Results at Step 2, showing the significant interactions in the moderated regression

Outcomes Predictors B SE of B β sr2

Depression Opt-C -0.536 0.055 -.382*** .142

NWFS-C 0.582 0.080 .289*** .078

OptxNW-C -0.062 0.016 -.150*** .021

Depression Opt-C -0.546 0.056 -.390*** .144

NFaWS-C 0.604 0.091 .269*** .067

OptxNFa-C -0.060 0.019 -.126** .015

Depression CSE-C -0.104 0.009 -.470*** .204

NWFS-C 0.543 0.077 .269*** .068

CSExNW-C -0.011 0.002 -.161*** .025

Anxiety CSE-C -0.052 0.009 -.262*** .063

NWFS-C 0.505 0.078 .279*** .072

CSExNW-C -0.009 0.002 -.152*** .023

Anxiety Opt-C -0.325 0.054 -.258*** .063

NFaWS-C 0.510 0.087 .253*** .059

OptxNFa-C -0.071 0.018 -.167*** .027

Work Absorption SD-C 0.390 0.035 .452*** .202

NWFS-C 0.071 0.051 .056 .003

SDxNW-C 0.038 0.011 .145*** .021

Emotional exhaustion Aut-C -0.340 0.062 -.227*** .050

NFaWS-C 0.629 0.074 .354*** .122

AutxNFa-C 0.053 0.016 .141** .020

Cynicism Aut-C -0.543 0.058 -.385*** .145

NFaWS-C 0.359 0.068 .215*** .045

AutxNFa-C 0.053 0.014 .148*** .022

Professional AC-C 0.206 0.032 .291*** .082

Efficacy NWFS-C -0.047 0.045 -.047 .002

ACxNW-C -0.026 0.009 -.131** .017 **p < .01, *** p< .001

Note. Opt-C = Dispositional optimism centred; NWFS-C = Negative work-to-family spillover centred;

NFaWS-C = Negative family-to-work spillover centred; CSE-C= Coping self-efficacy centred; SD-C

= Skill discretion centred; Aut-C = Job autonomy centred; AC-C = Affective commitment centred.

Interaction terms (e.g. Opt-NW-C) are made from the multiplying the centred terms as listed, with

NWFS shortened to „NW‟ and NFaWS shortened to „NFa‟

228

Table 2.18

Simple slopes for the centred predictor variable (X1) and the criterion variable (Y) at

Low and High levels of the second, centred moderating variable (X2)

Predictor (X1) and Criterion (Y) X2 Slope t (66)

Dispositional optimism and Depression Low NWFS -.510 -8.932***

High NWFS -.562 -9.843***

Dispositional optimism and Depression Low NFaWS -.486 -8.460***

High NFaWS -.606 -9.857***

Coping self-efficacy and Depression Low NWFS -.093 -10.398***

High NWFS -.115 -12.857***

Coping self-efficacy and Anxiety Low NWFS -.254 -4.622***

High NWFS -.396 -6.732***

Dispositional optimism and Anxiety Low NFaWS -.254 -4.622***

High NFaWS -.396 -6.732***

Skill Discretion and Work absorption Low NWFS .352 9.408***

High NWFS .428 11.963***

Job autonomy and Emotional exhaustion Low NFaWS -.393 -6.198***

High NFaWS -.287 -4.460***

Job autonomy and Cynicism Low NFaWS -.596 -10.103***

High NFaWS -.490 -8.212***

Affective commitment and Professional efficacy Low NWFS .232 6.871***

High NWFS .180 5.636***

**p < .01, *** p< .001

Note. All predictor variables were centred for the moderated regression analyses. NWFS =

Negative work-to-family spillover centred; NFaWS = Negative family-to-work spillover

centred;

Note. For the simple slopes, „Low‟ is -1SD below the mean, and „High‟ is +1SD above the mean.

229

For dispositional optimism and coping self-efficacy, the presence of the

resource buffered the effect of negative spillover, such that levels of depression and

anxiety were similarly low when levels of dispositional optimism and coping self-

efficacy were high, regardless on the level of spillover. However, when examining

the simple slopes for the workplace resources, it was the absence of the resource that

increased the effect of the negative spillover. Emotional exhaustion and cynicism

remained high when job autonomy was low, regardless of the level of negative

spillover. When job autonomy was high, levels of emotional exhaustion and

cynicism were lower when the negative family-to-work spillover was low. Similarly,

the absence of affective commitment to work reduced professional efficacy,

regardless of negative spillover, whilst greater affective commitment lead to greater

levels of professional efficacy when less negative work-to-family spillover was

present. The absence of the protective, workplace resources means that the risks

associated with negative spillover are not buffered, further increasing the likelihood

of burnout.

The moderated relationship between skill discretion and work absorption was

more complex as this was a disordinal interaction (i.e. the line of the slopes cross).

When skill discretion was low, absorption in work was low, regardless of negative

spillover. However, the highest level of work absorption was associated with high

levels of both skill discretion and negative work-to-family spillover. It could be

speculated that when a job allows the individual to use their skills and talents in a

way that was highly absorbing, any problems that spill from work to family domains

can be tolerated or possibly ignored.

The moderated relationships add to results of the regression analyses by

showing how specific resources are important for specific outcomes, rather than to

230

the overall, general processes. These could be thought of as resource-driven

behaviours (such as coping better with difficult situations to lessen depression and

anxiety when negative spillover is greater) that allow the individual to manage the

problems and tiredness associated with negative spillover to bolster specific

psychological outcomes.

2.3.20 Post-hoc analysis: What happened to humour?

From the literature review in Chapter 1, humour, measured by the coping

humour scale was expected to be a strong predictor of many of the outcomes.

However, this hypothesis was not supported, although there was some input for

humour as a predictor of depression, albeit in a counter-intuitive way. As

dispositional optimism and coping self-efficacy were the most frequent predictors in

the block of Individual Difference variables, it was suspected that these variables

could be of interest as possible mediators. Therefore, mediation was explored to

establish whether the lack of the expected effects of humour on the outcomes would

be explained in this way.

Using the bootstrapping method of multiple mediators (Preacher & Hayes,

2008), humour was entered as the independent variable, dispositional optimism and

coping self-efficacy were entered as the mediators and each of the outcomes (e.g. life

satisfaction, psychological well-being and so on) was separately considered as the

dependent variable. Except for work satisfaction and work absorption, the effect of

humour on the outcomes was mediated by dispositional optimism and coping self-

efficacy, and only by coping self-efficacy for the path from humour to stress. The

direct paths between humour and each outcome changed from being significant (i.e.

at least p <.01) to being non-significant (ranging from p = .106 for psychological

well-being to p = .977 for work vigour). An example of the changes in the

231

Table 2.19

Z scores for the indirect effects between humour and the outcomes, through

dispositional optimism and coping self-efficacy as the mediators

Mediators

Outcome Dispositional optimism Coping self-efficacy

Life satisfaction 4.365*** 6.320***

Psychological well-being 4.829*** 7.109***

Work vigour 3.820*** 5.542***

Work dedication 3.618*** 3.482***

Depression -4.280*** -6.515***

Anxiety -3.196** -3.920***

Stress -1.299 -5.542***

Emotional exhaustion -2.676** -3.823***

Cynicism -3.741*** -3.013***

Professional efficacy 2.365* 3.501***

*p < .05, ** p < .01, *** p < .001

significance and values was shown for the direct path between humour and life

satisfaction. The total effect, c, changed from .234 (the unstandardized estimate of

the effect of humour on life satisfaction), t(469) = 5.613, p < .001, to the direct

effect after the calculation of the indirect effects, c`, of .011 (the unstandardized

estimate of the effect), t(469) = 0.260, p = .795. All the outcomes saw similar

decreases in the total to direct effect of humour on the outcomes. The Z scores and

significance test of the indirect effects (i.e. the mediation paths through dispositional

optimism and coping self-efficacy) are shown in Table 2.19. The indirect effects of

the paths through the multiple mediators were significant and their confidence

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intervals did not include zero (i.e. the paths were significantly different to zero). In

summary, the effect of coping humour was seen through the lens of the effective use

of dispositional optimism and coping self-efficacy. It could be concluded that the use

of humour to provide relief when situations are tense and to bring problems into

perspective is an extension of being able to manage challenging and difficult

situations in general (i.e. using coping self-efficacy) and having a positive and

optimistic expectation for the future (i.e. to be high in dispositional optimism). As

such, where dispositional optimism and coping self-efficacy were significant

predictors of the outcomes in the regressions, part of the explanation of their effect

rests with the individual having the ability to use humour to lighten and lift their

mood and bring perspective about their life, as part of a broader suite of actions and

abilities.

2.3.21 Post-hoc analysis: An examination of gender

From the multiple regressions, gender was only a significant predictor for one

of the outcomes, with women reporting greater work satisfaction than men. A

comparison of working hours found that men (M = 44.42 hours, SD = 12.90) worked

longer lours than women (M = 39.83 hours, SD = 11.52), F(1,465) = 11.580, p <.001,

although there was no significant difference between the hours either gender would

prefer to work, F(1,468) = 0.162, p = .661. The male participants (M = 2.52, SD =

1.59) had slightly, but not significantly greater family and parental demands than

women (M = 2.14, SD = 1.43) after accounting for the breach of the assumption of

homogeneity of variance, F(1,468) = 4.869, p = .028. In addition, when considering

satisfaction with non-work domains, women reported slightly higher levels for each

than men but there were no significant differences between their levels of satisfaction

with their family lives (F(1,465) = 3.196, p = .074) and recreational activities

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(F(1,461) = 2.591, p = .108).

Of interest to the debate on how gender is important to how household chores

were shared, men and women did not differ on their satisfaction of how the

household chores were shared, F(1,425) = 0.821, p = .365. However, as could be

expected with the presence of another person in a household, participants with

partners (M = 3.56, SD = 1.33) reported significantly greater satisfaction with how

chores were divided than those participants who were without a partner (i.e. single or

divorced, M = 3.19, SD = 0.85), F(1, 423) = 7.075, p = .008. The interaction between

gender and partner status was not significant, (F(1, 423) = 0.470, p = .493). Among

the participants with spouses and partners, women reported more satisfaction with

their partners (M = 4.32, SD = 1.04) than men did with their partners (M = 3.85, SD

= 1.36), F(1,287) = 8.811, p = .003, which remained significant after accounting for

the breach in the assumption of homogeneity of variance.

Apart from the small differences outlined above, the genders were in fact

rather alike in their assessment of their lives. There was no difference how busy they

felt (F(1,468) = 0.046, p = .830), how easily their lives fitted together (F(1,468) =

0.298, p = .585) or in their satisfaction with their work-life balance (F(1,468) =

0.082, p = .775). They did not differ on the rewards they gained (F(1,468) = 0.201, p

= .654) or in their commitment (F(1,468) = 0.160, p = .689) to their occupations or in

the reward they felt as parents (F(1,468) = 0.908, p = .341) or their commitment to

the parenting role (F(1,468) = 0.869, p = .352). However, among participants with

spouses of partners, men reported somewhat slightly higher rewards from their

marital role (F(1, 281) = 3.841, p = .052), but there were no differences between the

partnered men and women on their marital role commitment, (F(1,281) = 0.079, p =

.779). However, all of these results must be considered with the caveat that there

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were many more women than men in the sample of the current research.

2.3.22 Post-hoc analysis: What predicts negative spillover?

From Table 3.16, negative spillover from work to home and home to work

were significant predictors of many outcomes and mediated between many variables

and emotional exhaustion. What lies behind this? It was not sufficient to say

„negative spillover is a problem‟, rather it was sensible to explore what may be

predictors of both forms of negative spillover that employers and employees can take

steps to reduce or eliminate the sources of negative spillover.

Returning to the predictor variables used in the initial hierarchical multiple

regressions, a multiple regression was conducted to again assess the merits of all the

variables. After taking out the predictors previously identified as the more frequent

significant predictors of the main outcomes (i.e. dispositional optimism, coping self-

efficacy, affective commitment, job autonomy and skill discretion) and the two

negative spillover scales, 25 of the original 32 predictors remained. The regression

model for negative work-to-family spillover had a large effect size and was

significant R2 = .414, F(25, 444) = 12.538, p < .001. The adjusted R

2 was .381,

which indicates that over a third of the variability in negative work-to-family

spillover was explained by the variables, although only six of the 25 possible

predictors were significant. The predictors came from the Individual Difference

variables (perceived control of time and egalitarian gender role attitudes), from the

Work and Family variables (managerial support, job social support and education)

and from the Work-Life Interface variables (feeling busy) and were most of the

variables for which negative work-to-family spillover was a strong mediator for their

effects on emotional exhaustion. Negative work-to-family spillover was predicted by

a lack of perceived control of time (β = -.267, p < .001, sr2 = .049), less support for

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egalitarian gender role attitudes (β = -.109, p < .006, sr2 = .010), less managerial

support (β = -.115, p = .008, sr2 = .009) and less job social support (β = -.188, p

<.001, sr2 = .024), for higher levels of education (β = .094, p = .016, sr

2 = .008) and

when individuals felt busier (β = .275, p <.001, sr2 = .053).

In summary, negative work-to-family spillover is increased where the

individual felt that they did not have control of their time, where gender equality was

not supported and where the individual had less workplace supports, generally from

supervisors and co-workers, and specifically from their managers for any work-

family matters and they feel their life is more hectic that they would like.

A separate consideration was given to the predictors of negative family-to-

work spillover, using the same set of predictors. The regression model was

significant, R2

= .243, F(25,444) = 5.707, p < .001, although this was a only a

medium-large effect size. The adjusted R2 was .201 which indicates that 20% of the

variability of negative family-to-work spillover is explained by these variables,

although only four of the 25 possible predictors were significant. Negative family-to-

work spillover was predicted by a lack of perceived control of time (β = -.261, p <

.001, sr2 = .047), increased family demands from younger children (β = .283, p <

.001, sr2 = .037) and feeling busier (.134, p = .007, sr

2 = .013). The effect of children

was enhanced by negative suppression, such that fewer children increased negative

spillover (r = .083, β = -.150, p = .012, sr2 = .011). In summary, negative family-to-

work spillover was greatest when the individual feels that time was not under control

and that life was hectic and busy. Taking the effect of the number of children

together with family demands, it could be speculated that fewer, younger children

were more likely to increase the negative spillover from the family to the work

domain. This may perhaps occur as one child may take more „work‟ than two or

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more children because the children could entertain each other, rather than one child

relying only on their parents.

It is interesting to note that for both types of negative spillover, neither the

hours and individual works or their preferences for working hours influenced the

level of negative spillover that that they experience. Working hours (β = -.015, p =

.683) and a preference for less working hours (β = -.025, p = .521) were weak,

negative non-significant predictors for negative work-to family spillover. The results

were still non-significant but were slightly different for spillover from family to

work, as working hours (β = -.043, p = .321) and a preference for more working

hours (β = .062, p = .153) were weak, negative, non-significant predictors for

negative family-to-work spillover. However, as all these results are non-significant,

any differences were not meaningful in terms of understanding negative spillover

either from work to home or home to work.

2.3.23 Post-hoc analysis: Understanding positive spillover

To round out the understanding of spillover, a consideration of positive

spillover will be undertaken. From the literature review in Chapter 1, positive

spillover was expected to be an important predictor of the outcomes that were

considered. However, this did not occur, with only positive family-to-work spillover

a significant predictor of life satisfaction and psychological well-being. This final

post hoc analysis explored the relationships between the work-life interface and

positive spillover and then examined the predictors of positive spillover. The

correlations table, Table 2.3 shows the relationships between the work-life balance

and fit, feeling busy and the direction and quality of spillover. Interestingly, positive

work-to-family spillover is not correlated to the other components of the interface,

being only correlated with feeling busy (r = .098, p = .033). Further, positive family-

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to-work is only correlated to satisfaction with work-life balance (r = .267, p < .001)

and work-life fit (r =.190, p < .001) and less so to feeling busy (r = -.116, p = .012)

and negative family-to-work spillover (r = -.110, p = .017). Interestingly, positive

spillover, i.e. the benefits gained from a role appeared to be unrelated to negative

spillover, i.e. the problems associated with a role.

In an exploratory analysis of the predictors of both positive spillover scales,

positive work-to-family spillover was first to be considered as the dependent

variable, with all of the other predictors entered in the multiple regressions. The

regression model was significant and had a medium-large effect, R2 = .277, F(31,

402) = 4.967, p < .001. The adjusted R2

was .221, with over 20% of the variance of

positive work-to-family spillover explained by the variables, although only 5 of the

possible 31 predictors were significant. Positive work-to-family spillover, where the

activities and skills learnt at work help the individual in their home life, was

predicted by having greater social skills (β = .099, p = .046), more affective

commitment to one‟s job (β = .148, p = .005), greater job autonomy (β = .108, p =

.046) and skill discretion (β = .219, p< .001). Greater negative family-to-work

spillover was also significant, positive predictor (r = .050, β = .164, p = .002)

although this effect was enhanced by the presence of suppressor variables. In

summary, a socially skilled individual, when combined with the benefits of a

resourceful workplace, where the individual likes their work, can make their own

decisions and use their talents, provide the foundation for positive spillover from

work to family domains. These resources help the individual perform better in their

home and family lives, with speculation that the negative spillover between family

and home could trigger some sort of spiral; more problems or stress at home leading

to greater use of workplace resources to find solutions to those problems.

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When positive family-to-work spillover was entered as the dependent

variable, the regression model was again significant and had a large effect, R2 = .366,

F(31,402) = 7.472, p < .001. The adjusted R2 was .317, indicating that just over 30%

of the variance was explained by the variables, although only 5 of the 31 variables

were significant. Positive family-to-work spillover, where the support and affection

at home allows the individual to perform better at work was significantly predicted

by greater coping self-efficacy (β = .341, p < .001), placing more value on the

rewards of the marital role (β = .140, p = .022), having a partner (β = .118, p = .022)

with the effects of being younger (r = -.106, β = -.144, p = .008) and negative work-

to-family spillover (r = -.059, β = .144, p =.011) being enhanced by suppressor

variables. In summary, feeling capable of managing challenging situations, along

with having and valuing a spouse or partner led to increases in positive spillover, the

home-based support given to the individual. Younger people possibly received

support from other family members, not just a marital partner. The effect of negative

work-to-family spillover was made stronger, although in the opposite direction by

suppressor variables, possibly indicating that the individual can gain more support

from home as problems or troubles at work increase.

2.4 Discussion

The results of these analyses showed that Bronfenbrenner‟s developmental

equation provided a useful framework to better understand the developmental

outcomes of the working adult. By specifying the components of a generative

disposition, positive demand characteristics and the work-life interface, and applying

these to a broad view of psychological functioning, both the general trends and

specific predictors can be seen. The general trend was that as was hypothesized,

more generative dispositions and positive demand characteristics, along with more

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resources at work and within families and less demands from the work-family

interface were predictive of higher levels of well-being (as life satisfaction and

psychological well-being), work satisfaction, and work engagement (as work vigour,

dedication and absorption), better mental health (as the absence of depression,

anxiety and stress) and less burn out (as less emotional exhaustion and cynicism and

greater professional efficacy). However, more specifically, the most important

predictors of these outcomes were personal and workplace resources along with the

demands from negative spillover to and from work and home. There were seven

main predictor variables; dispositional optimism and coping self-efficacy (resources

of P, the generative disposition, from the Individual Difference variables), affective

commitment, job autonomy, and skill discretion (resources of C, the Work and

Family context), negative work-to-family spillover and negative family-to-work

spillover (demands of C, the Work-Life Interface). However whilst these were most

important across the board, being more specific still, a mosaic of variables was

necessary to fully explain the breadth of a person‟s functioning. The fine-grained

analysis from the multiple regressions shows the particular components about the

individual and their surrounding context that are important for each particular

outcome. The seven most common predictors may provide a broad overview of

general importance, but it was the pieces of the mosaic that round out the overall

understanding of the individual‟s psychological functioning. Given the breadth of the

outcomes that were examined, it was perhaps not surprising that there was this

mosaic of significant predictors. The diversity of psychological functioning meant

that it was likely that many would be important (in varying combinations) and that

no one variable would be the important predictor. Bronfenbrenner‟s developmental

equation allowed for the analyses to illustrate levels of understanding of what would

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influence competent development, from the overview that the individual‟s generative

disposition, workplace resources, and the demands of work-life spillover influence

psychological functioning down to the specific predictors of each outcome.

The limited number of mediated and moderated relationships indicates that

the principle mode of action between the individual difference, work and family and

work-family interface variables was likely to be straight forward and additive, rather

than multiplicative. A general consensus would be that an individual with good

levels of personal resources, sufficient workplace and family resources and with less

negative and more positive spillover between their life domains will have fare better

across the measured outcomes. Dispositional optimism, skill discretion and negative

work-to-family spillover were strong contenders to be central to these processes,

with negative work-to-family spillover an important mediator, particularly for

emotional exhaustion, and moderator as well. The specific examples of where

moderation occurs shows that the general additive nature of development has parts

that are not so simple, where the presence or absence of a resource can buffer the risk

that negative spillover presents. Only for some resources and only in some situations

does this occur, again adding to the fine-grained analysis of competent development,

which supplements a broader, more general understanding of the individual.

Some of the intriguing findings from the multiple regressions are what was

not important, for example, gender. The findings that women were more satisfied

with their work than men is similar to previous findings (A. E. Clark, 1997),

although the level of education did not reduce the gender difference, as was the case

in Clark‟s study of British employees. Further, men and women did not differ on

their commitment to work and family and did not experience differences in how they

were able to use their skills at work. Among the participants of the current research,

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men and women may experience their lives differently, but they did not differ how

on satisfied they were with life in general, whether they have a mental illness or felt

burnt out. It could be speculated that amongst this sample of employed individuals,

gender was less important to the outcomes as employment provided sufficient

resources for both genders to buffer against poorer outcomes, as among

underemployed, partnerless men in the Australian Quality of Life surveys (Cummins

et al., 2003; Cummins, Woerner et al., 2007) or for women and the unemployed on

other Australian research (Andrews et al., 2001; Hawthorne et al., 2003). In this

sample, gender was not important to the end result of competent development, as

measured by this broad range of outcomes. As such, a happy and fulfilling life does

not depend on being one gender or the other in the present sample.

An important consideration then to come from this thesis is that rather than

focus on the individual‟s gender, it is the way that individuals think that is much

more important. Any individual who has positive, optimistic views of the future, and

who views themselves as being capable to manage difficult situations will have

better psychological functioning. It is not the preserve of one gender or the other to

„think better‟. I believe that gender, as will be shown with working hours, is too blunt

a measure of what is important in individual differences. An individual‟s behaviour,

based on their levels of optimism and self-efficacy will be a better indicator of how

they will respond to the challenges of managing their work and family lives, rather

than merely accounting for their gender.

The prevalence of dispositional optimism as a significant predictor of the

diverse outcomes indicates that it is a fundamental personal resource that gives the

individual many benefits for positive psychological functioning. This finding

supports the central position of this thesis, that dispositional optimism, which is the

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expression of self-regulation (Carver & Scheier, 1998; Scheier et al., 1994), captures

Bronfenbrenner‟s generative disposition (Bronfenbrenner & Morris, 1998, 2006) and

is essential for competent development. Dispositional optimism directly predicted

life satisfaction, psychological well-being and reduced depression and anxiety.

Further, it buffered the individual from the effects of negative spillover from

depression and anxiety, such that individuals who were high in dispositional

optimism were less influenced by high levels of negative spillover in both directions.

The results support the effect of dispositional optimism to moderate the effects of

health problems to improve well-being and reduce depression and distress (Elavsky

& McAuley, 2009; Hart et al., 2008; Major et al., 1998; McGregor et al., 2004), as

well as reducing stress in work situations (Atienza et al., 2004; Chang, 1998; Sumi,

1997; Taubman-Ben-Ari & Weintroub, 2008). The results extend the importance of

the individual‟s optimism to the research on work engagement and burnout.

Dispositional optimism was associated with greater satisfaction with work and more

engagement with work, as more vigour and dedication and the lessening of cynicism,

supporting the limited findings for dispositional optimism in the occupational health

psychology research (Xanthopoulou et al., 2007).

The importance of coping self-efficacy, as a measure of the individual‟s

confidence in their ability to handle difficult situations as a predictor of the

outcomes, adds to dispositional optimism as a marker of the generative disposition.

Coping self-efficacy is particularly important to the mental health outcomes as well

as well-being and work vigour, which supports previous research among working

individuals (Jex & Bliese, 1999; Judge et al., 1998; Marshall & Lang, 1990) and

amongst carers of AIDS patients (Chesney et al., 2003). Feeling capable and

competent and persisting toward goals (DiBartolo, 2002; Ryff & Singer, 1998;

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Semmer, 2003) may also explain the buffering of the effect of high negative work-to-

family spillover depression and anxiety, as the individual is able to formulate plans

to offset the problems associated with the work-family interface, rather than be

downcast by those problems.

Control of time was considered to be important to the understanding of the

developmental outcomes, but this was not the case as the effect of feeling in control

of time was mediated by negative spillover. Rather than a fundamental characteristic

of the individual, as locus of control, the belief that one is control of their own time

occurs in response to the current situation or external cues, which supports the view

that control should be taken in context (Fournier & Jeanrie, 2003). As such, an

individual will feel that they have control of time where negative spillover (taken as

problems and tiredness between roles) is limited and feel „out of control‟ when

negative spillover rises. The subsequent analyses on the predictors of negative

spillover showed that feeling a lack of control of time predicted both directions of

negative spillover, which highlights the results of the mediation analyses. This may

be a circular argument about whether there is mediation or prediction but in the end,

greater levels of either type of negative spillover lessen the individual‟s sense that

they can control their time.

Other variables that were considered likely to be important predictors were

humour and social skills, which were proposed as the demand characteristics with

which the individual interacts with their environment. Humour was important to the

outcomes until the addition of dispositional optimism and self-efficacy, where after it

was completely absorbed by these personal resources. This was an unexpected result,

given the breadth of previous research on the benefits of humour to cope with

stressful situations (Abel & Maxwell, 2002; Kuiper & Nicholl, 2004; Nezlek &

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Derks, 2001; Thorson et al., 1997). The addition of dispositional optimism and

coping self-efficacy mean that the cognitive –affective reappraisals that are involved

in the use of humour are actually part of the suite of behaviours that come with the

use of these personal resources. Humour would therefore be a part or outcome of

optimism and self-efficacy, rather than a separate construct, with self-regulation

involving the use of mature defense mechanisms (Vaillant, 2000). However, the

thesis did not measure humour as an interpersonal style (R. A. Martin et al., 2003),

so these comments can only apply to humour as a coping mechanism. The other

measure of the demand characteristics was social skills but this was also not a major

contributor. Interestingly, better social skills was associated with higher levels of

anxiety and stress, perhaps indicating that social skills are more needed in distressing

or stressful situations.

The limited input from role salience scales toward the outcomes may reflect

the breadth of the analyses, whereby the importance attached to a role is, like

humour, part of the self-regulation of a goal-directed life. However, parental role

commitment was specifically important to well-being. I believe that parental role

commitment, measured as being prepared to be involved with the activities of child

rearing, reflects a generative view of life (McAdam, de St Aubin, & Logan, 1993),

seeing the care of children is an important activity in the life span. This would

indicate that greater well-being may be attached to fulfilling (or having the

expectation to fulfil) the role of parent, as part of the many social roles in life (Elder

& Shanahan, 2006). Many participants, including those without children, strongly

endorsed the items on this scale and future research should explore how participants

regard the parental role, whether or not they have children. Marital role salience did

not provide predictors for any of the outcomes, whilst occupational role reward and

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commitment was only important for work absorption, role commitment to work

vigour, and role reward to higher levels of professional efficacy. As such, valuing the

work identity did not lead to greater stress or work satisfaction, only increased the

specific facets of absorption, vigour and competence at work.

Although the influence of egalitarian gender role attitudes was mediated by

negative work-to-family spillover, this mediation may prove useful in understanding

the process that leads to increased emotional exhaustion. Among health care workers,

a lack of fairness is considered one of the pathways that lead to the development of

burnout (Maslach, 2006; Maslach & Leiter, 1997). As the positive endorsement of

egalitarian gender roles brings with it an implicit belief that everyone can have a „fair

go‟, it is possible that the problems implicit in negative spillover could make the

individual feel that their work or family situation was unfair and that they were not

being supported as they felt they should be, either at home or at work. Further, the

perception that everyone is equal also implies that everyone should be helping

equally, so it may capture assessments of inequality as well as unfairness.

Perceptions of inequality may lead to or exacerbate the problems and tiredness

associated with negative spillover, which would increase emotional exhaustion.

Therefore, rather than just a statement about gender roles, these attitudes appear to

capture the fairness of gender relationships and indicate how fairness in workplace

relationships could reduce the experience of negative work-to-family spillover.

Turning to the Context of the Work, Family and Work-Family Interface

variables, another interesting and surprising „non predictor‟ was working hours as

hours are often taken as prime importance to the work-life interface (Pocock, 2003,

2005). However, in these results, the length of the working week was not an

important predictor of any of the outcomes, nor was it associated with levels of

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negative spillover. The recent literature on the detrimental effects of working hours

(Pocock, 2003; Relationships Forum Australia, 2007) is not supported in the present

research rather the more important characteristic about working is the nature of the

work itself. Just using hours per week is a rather crude metric for the sort of work an

individual does, as hours do not capture anything of the type of work or the way the

individual views the work that they do. A simple thought experiment can illustrate

this: an hour in a boring or disliked job is too long, whilst 60 hours in an engrossing,

interesting, challenging job may not seem enough. Working hours may be more

easily gleaned from government statistics (for example, from the Australian Bureau

of Statistics, www.abs.gov.au), but the harder effort to find out the employees‟

affective commitment or their autonomy or use of their skills which would be better

measures of how work affects the individual.

The results show that the two most important workplace resource are that the

individual is able to use their skills, talents and creativity, i.e. to have high levels of

skill discretion and that they are attached to their work. Skill discretion and affective

commitment to work are closely linked in the current sample and could be expected

to enable the individual to better meet the demands of their job, which has support

from previous research (Bakker et al., 2003; Demerouti et al., 2000; Hakanen et al.,

2006; Meyer et al., 2002; Schaufeli & Bakker, 2004).

The workplace resources, along with job autonomy, the ability to direct one‟s

work, are closely linked with higher levels of work engagement and less burnout

among the participants, which is similar to findings based on the Job Demand-

Resources model (Bakker et al., 2003; Demerouti et al., 2001). The importance of

skill discretion in explaining the outcomes also lends support to the Demand-Control

Support model (Karasek & Theorell, 1990), where high levels of control, a

http://www.abs.gov.au/

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combination of skill discretion and decision latitude (i.e. job autonomy), reduced the

impact of greater psychological demands and limited social support. In the current

study, working hours can be taken as a measure of work demands, whilst support

was measured by managerial support for work-life matters and general social support

from supervisors and co-workers. It has already been noted that working hours was

not a useful predictor and the effects of support, managerial or in general, were

mediated by negative spillover. As such, the current research does not add to either

the Demand-Resources or the Demand-Control-Support models. It would be fairer to

say that the experience of the workplace is more complicated than either model, as it

is necessary to acknowledge that workers have family lives and personal resources,

which are not included in either model. Only recently has personal resources been

considered as a possible addition to the Job Demand-Resources model (Bakker,

2005), which is in line with the current results. Again, Bronfenbrenner‟s equation

allows the more complex consideration of all the influences on the individual, with

the context of the individual‟s life expanded from their workplace to their family and

the interface between those roles.

The Family variables were not as potent as perhaps was expected, unlike

previous research which showed young children to increase negative spillover

(Grzywacz et al., 2002). In the current research, the effects of the number of children

and family demands were mediated by negative family-to-work spillover whilst

marital status did not predict any of the outcomes. It was interesting that fewer,

younger children were associated with more problems at home interfering with work.

More children, rather than just one child, may make child care easier. For example,

two children could play together with limited parental input, whilst one child needs

their mother or father to be their playmate, increasing the demands on the parent.

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Further, the presence of younger children, which leads to more family demands, was

associated with lower levels of anxiety and having children, per se, was associated

with lower levels of cynicism. Taken together, the results show that the care and

involvement with children can provide the individual with a sense of perspective that

protects against distress and cynicism. Children have their own interests and

activities and could give adults another view on the world that makes the adults less

jaded and their own concerns less worrying. By including both parent and non-

parents in the sample it was possible to show that children are not detrimental to

mental health and well-being. As noted previously, valuing child care activities was

associated with higher levels of well-being, even among non-parents. Taken together,

children may change the way and adult lives their life but they are not a „burden‟ that

reduces well-being or increases mental illness, as there were few difference between

parents and non-parents in the developmental outcomes.

Managerial support for work-life issues and social support on the job

generally were not substantial contributors to the outcomes, despite the strong

correlations between these two variables and all of the outcomes that were shown in

Table 2.3. Job social support was only a predictor of stress, such that more support

was given when the individual reported more stress. Managerial support did predict

greater life satisfaction and predicted less emotional exhaustion, over and above the

partial mediation of negative spillover in both directions. This result is in line with

previous research among university staff (Grawitch et al., 2007). The reason for the

lack of predictive power most probably lies with negative spillover, as job social

support and managerial support were significant predictors of negative work-to-

family spillover, which in turn was a pervasive, negative contributor to the

developmental outcomes.

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Negative work-to-family spillover impacts all aspects of psychological

functioning, reducing well-being and work satisfaction and vigour, increasing mental

illnesses and particularly impacting on emotional exhaustion and stress. Previous

research has shown that managerial support is beneficial for employees to use

available workplace flexibility programs (Behson, 2005; Dikkers et al., 2004; C. A.

Thompson et al., 1999) and that the general support from supervisors and co-workers

increased work engagement and reduced burnout (Bakker et al., 2007; Klusmann et

al., 2008a; Schaufeli & Bakker, 2004). However, when combined with the research

showing that increased managerial support reduced work-family conflict (Behson,

2002), it is possible to see the path whereby the lack of workplace social support,

either in general or specifically for work-family issues, would increase the

experience of problems in the workplace spilling over and impacting on the home

domain.

Another implication from the mediation of managerial support, general job

social support and egalitarian gender role attitudes by negative spillover comes from

the research on burnout. Burnout is most likely to occur when there was a high

workload, where there was a lack of control over situations and work tasks, such as

handling similar problems for many different people, insufficient rewards for the

work that is done, by a breakdown of community within the workplace and a loss of

support, an absence of fairness and conflicting values between the individual and

their employment (Maslach, 1993, 1998, 2006; Maslach & Leiter, 2008; Maslach et

al., 2001). There is s similarity here to the results of the multiple regressions, where

negative work-to-family spillover was the strongest predictor of emotional

exhaustion, a major component of burnout. The predictors of negative work-to-

family spillover can be seen in the predictors of burnout. The loss of support and

250

workplace community would be equivalent to the lack of managerial support and

lack of general job social support, the lack of control is similar to a lack of perceived

control of time, whilst the absence of fairness can be seen in the lack of support for

egalitarian gender role attitudes. Negative spillover becomes a potent predictor of

emotional exhaustion because it is bringing together the direct contributors to

burnout. The combination of lost workplace supports would be another reason why

negative work-to-family spillover has such a widespread effect on all of the

outcomes.

Whilst negative spillover in both directions were important to understanding

the outcomes, there was limited input from the other Work-Family Interface

variables with satisfaction with work-life balance, work-life fit, feeling busy and

positive spillover only predicting a few outcomes. Satisfaction with work-life

balance added to the individual‟s life satisfaction, which would reflect how

satisfaction with the domains of life (i.e. a bottom-up approach) adds to overall

assessment of life satisfaction (Easterlin, 2006). Work-life fit, the ease with which

the demands of work and family could be managed, was only a predictor of reduced

emotional exhaustion. Further exploration of work-life fit is necessary to better

understand how this assessment of fit is made. It could be speculated that work-life

fit may be similar to humour, that it is a consequence of successful self-regulation

with better fit arising from the behaviours and choices to that allow work and family

roles to be managed more easily. The new item about how busy the participant felt

was an interesting addition to the likely predictors of the outcomes. Whilst feeling

busy added to the stress an individual felt, it also added to their sense of professional

efficacy. Perhaps this indicates that while an individual can feel busy and stressed,

being busy gives them a sense of importance about their work. There is a further

251

consideration, as feeling busy also contributed to both work-to-family and family-to-

work negative spillover. It will be interesting to further investigate the point at which

„busy‟ becomes „crazy busy‟, when the assessment of too much to do increases stress

and negative spillover beyond the benefits to feeling competent at work.

The last two components of the Work-Family Interface are the positive

spillover scales. The lack of correlations between positive and negative spillover

indicate that the benefits and problems of roles are not necessarily tied to each other.

As with the experience of positive and negative affect when coping with difficult

situations (Folkman & Moskowitz, 2000; Folkman & Moskowitz, 2004), it is

possible that the positive and negative experiences from the interactions between

roles can co-exist somewhat independently of each other. The lack of correlations

between the various measures of the work-life interface also limits further discussion

of what is the best definition of „work-life balance‟. Given the pervasiveness of

negative spillover in combination with personal and workplace resources to explain

the outcomes, how the individual views the sum or balance of their life may be a

verb (Greenhaus et al., 2003), dependent on the shifting, dynamic interplay of the

many predictors identified in this analyses.

Positive family-to-work spillover was one of the significant predictors of life

satisfaction and psychological well-being and captures how a supportive home

environment bolsters the individual in their assessment of their overall well-being.

The exploration of positive family-to-work spillover found that the predictors were

coping self-efficacy, marital status and marital role reward and younger age, with the

surprising inclusion of negative work-to-family spillover. For the individual who is

able to manage challenging situation, having the support from a spouse or partner

(and for younger people, perhaps support from parents) would see the home and

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family domain as a source of strength to manage work situations, particularly when

there are problems at work that are likely to spill over to home. It may be almost a

circular argument that valuing a partnership will engender more support from that

partner, and the analyses cannot imply causality, only association.

Turning to the predictors of positive work-to-family spillover, the same

workplace resources that were associated with more positive psychological

functioning were also associated with a workplace that enabled the individual to

perform better in their personal lives. It is possible that these positive associations

between workplace and family in both directions are similar to the affective

pathways of work-family enhancement proposed recently by Greenhaus and Powell

(2006). However, the results of the current research are not conclusive to confirm

these pathways and further research can explore these pathways further.

The important practical outcome was that the links between the measures of

work-based support and negative spillover provide leverage points at which

employers may make a substantial contribution to their employees‟ psychological

functioning. In addition to the previous comments about egalitarian gender role

attitudes, employers can take steps to ensure that equality is practiced, cooperation

between their employees is positively encouraged and that their managers actively

supported their employees‟ work-family responsibilities. There are many workplace

programs currently available to achieve these ends (for example, the Family Friendly

Index outlined in Clifton & Shepard, 2004), which will benefit the employer by

increasing productivity and retaining staff and benefit the employee by decreasing

negative work-to-family spillover. By providing more resourceful work

environments, employers can support their employees to improve work engagement

and limit burnout, which would be a win-win situation for both parties.

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The importance of containing negative spillover is also illustrated by the

moderation analyses. The importance of personal and workplace resources to the

developmental outcomes supports Hobfoll‟s (1989, 2001, 2002) Conservation of

Resources theory, with the resources (dispositional optimism, coping self-efficacy,

skill discretion, affective commitment and job autonomy) broadly associated with

achieving greater well-being, better mental health, less burnout and more work

engagement. However, the moderating effect of negative spillover (in both

directions) brought out an interesting difference between the personal resources and

the workplace resources. In the absence of workplace resources (i.e. at low levels of

job autonomy, affective commitment and skill discretion), there was no protection

from the effect of negative spillover, either low or high. In contrast, the presence of

personal resources (i.e. high levels of dispositional optimism and coping self-

efficacy) insulated the individual from depression and anxiety, regardless of the

levels of negative spillover. The presence of high levels of dispositional optimism

buffered the moderating effect of high negative spillover (both work-to-family and

family-to-work) on depression and anxiety (family-to-work only). Individual who

were more optimistic were better able to withstand the depressive and distressing

effect of negative spillover, again highlighting the importance of dispositional

optimism as a central personal resource that allows the individual to manage their

lives successfully which is in support of previous research (Armor & Taylor, 1998;

Aspinwall et al., 2002; Aspinwall & Taylor, 1997; Culver et al., 2003; Scheier et al.,

2002).

The presence of coping self-efficacy similarly buffered individuals from the

effects of negative work-to-family spillover on depression and anxiety, as at high

levels of coping self-efficacy, high negative work-to-family spillover was limited in

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its effect. More confidence in managing challenges (Chesney et al., 2003; Scholz et

al., 2002) would allow the individual to find solutions to the problems at work that

threaten to spill into their family lives, protecting from any distresses.

When considering the workplace resources, the simple slopes give a different

explanation to the way the workplace resources operate. Rather than there being little

difference in the outcomes at high levels of the personal resources, it is at low levels

of the resources that there is little difference in outcome due to the level of negative

spillover. When job autonomy was low, emotional exhaustion and cynicism were

increased to similar levels, regardless of negative family-to-work spillover. When

affective commitment was low, professional efficacy was similarly reduced and

when skill discretion was low, work absorption was limited, both occurring

regardless of the levels of negative work-to-family spillover. At high levels of the

resources, the differing effects of low versus high negative spillover could be seen,

where the effect is as could be expected; low negative spillover, less exhaustion or

cynicism where there was greater job autonomy and greater professional efficacy

where there was higher affective commitment. However, the results for work

absorption were perhaps counter-intuitive, as the individual who had a job with

greater use of their skills and talents became more engrossed in their work, enabling

them to overcome or ignore problems at work that impacted their families. Whilst

this may be sustainable in the short term, not disengaging from work can lead to

tiredness and reductions in positive mood at home (Sonnentag & Bayer, 2006) and

increase the likelihood of burnout developing (Vinje & Mittelmark, 2007). As with

the moderations involving dispositional optimism and coping self-efficacy, these are

specific effects of the workplace resources on specific outcomes.

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In summary, the general processes between the individual and their context,

outlined by Bronfenbrenner‟s developmental equation were largely additive but the

moderations provide specific instances where the resources act on the effects of

negative spillover. Both individual and workplace resource were important to this

process, indicating that programs that bolster the individual as well as supportive

workplace and employers could be targeted to improve functioning. Across all the

analyses, competent development, however defined requires the input of personal

and contextual resources (mostly from the workplace) to deal with the consequences

of negative spillover that occurs in both directions. There were a core of important

variables, but it was the fine grained analyses of the regressions that showed how

there was a mosaic of many variables that predict the many different outcomes that

represent competent and positive psychological functioning.

2.4.1 Limitations and strengths of Study 1

The sample for Study 1 was largely university educated, although the sample

from the public hospital did include a numbers of individuals with less than a

university education. These constraints do limit the generalisability to blue-collar

employees or manual labourers, although with the rise of the knowledge economy,

this component of the labour market is diminishing. Another limitation is that the

sample is largely female and the conclusions about the factors that lead to competent

development may not apply to men equally as to women. Further research among

predominately male samples and in other cultural settings should test the conclusion

of the current research to confirm that the findings apply across genders and in other

less individualistic cultures. In addition, other sources of information should be

included in future research to avoid any possible bias from common method

variance.

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The strengths of the sample for Study 1 were first that it included a very

diverse range of occupations, particularly among the university alumni sample and

even within the hospital sample. The administration of the hospital involved a range

of occupations, including nurses, managers and clerical workers. The breadth of

occupations would indicate that the findings have greater external validity and are

applicable to many separate industries rather than being limited to narrow groups of

employees, such as healthcare workers or police officers (de Jonge et al., 2001;

Demerouti, Bakker et al., 2004; Dikkers et al., 2004). A second strength of Study 1

was the size of the sample, which allowed sufficient numbers to investigate the large

number of predictor variables in the multiple regressions and provided enough power

for the longitudinal modelling. The sample size gives robustness to the findings,

although replication in other populations of employees is necessary to confirm the

findings.

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Chapter 3, Study 2: Longitudinal modelling

Following on from the hierarchical multiple regressions of the previous

chapter, seven variables were identified as the most common predictors of the

developmental outcomes. Specifically, these were the personal resources of

dispositional optimism (Scheier et al., 1994) and coping self-efficacy (Chesney et al.,

2003) and the workplace resources of skill discretion (Schwartz et al., 1988), job

autonomy (Voydanoff, 2004c), and affective commitment (N. J. Allen & Meyer,

1990), with the negative spillover from work to the home arena and from the home

arena to work (Grzywacz & Marks, 2000b).

Study 2 will take these variables of the personal and workplace resources and

the negative spillover between domains and model their influences on the

developmental outcomes longitudinally. This modelling will bring the Time

component into Bronfenbrenner‟s equation, specifically testing for the gain and loss

in resources over time, proposed by Hobfoll‟s (1989, 2001, 2002) Conservation of

Resources Theory.

One of the advantages of longitudinal analyses can be to tease out cause and

effect where there is no clear beginning to the effect of one variable on another

variable or where reciprocal relationships may be operating (Menard, 1991). The

framework of Bronfenbrenner‟s developmental equation, D f PPCT (Bronfenbrenner

& Morris, 1998, 2006) acknowledges the dynamic and reciprocal relationships

between the individual, their environment and the developmental outcomes over

time, such that the person is influential on both sides of the equation. First as the

active participant in dynamic interactions with their environment and second through

the developmental outcomes that support and reciprocally influence later behaviours

of the person, who then interacts with their environment. This process implies

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ongoing, dynamic relationships of which the current research can capture only a

sliver of the passing time.

The longitudinal models will be tested in the format described by De Jonge,

Demerouti and colleagues (for example, de Jonge et al., 2001; Demerouti, Bakker et

al., 2004; Llorens et al., 2007) which allow for the testing of concurrent functioning

and both the stability of variables and the strength of reciprocal relationships over

time. However, this thesis will take the process of testing the competing longitudinal

models one step further than described by De Jonge and colleagues. By removing

trivial pathways from the models, I believe that it will be possible to more clearly

identify the influential pathways that will show Bronfenbrenner‟s developmental

equation in action. Further, it is possible that the consequences of prior functioning at

the individual level, of their work-life interface and of their well-being and mental

health can be seen as loss and gain spirals of resources (Hobfoll, 1989, 2002). The

gain and loss spirals are proposed by the Conservation of Resources theory (Hobfoll,

1989, 2002), which states that individuals will feel stressed if personal or workplace

resources are lost, if their resources are threatened or they do not gain the resources

that could be expected from their efforts. The loss of resources set up a loss spiral,

with initial losses increasing over time. However, in times that were not stressful,

individuals were likely to take steps to increase their resource base, to provide for

future needs, which would result in a gain spiral of resources. In addition, stability

and continuity of resources are shown where similar resources were linked together

and maintaining the stability of each resource over time, in „resource caravans‟

(Hobfoll, 1989, 2001, 2002).

The longitudinal modelling of De Jonge and colleagues found the presence of

both loss spirals (de Jonge et al., 2001; Demerouti, Bakker et al., 2004) and gain

259

spirals (Llorens et al., 2007) with stability evident between the same variables over

time. However, the research often focused separately on the negative or positive

outcomes, not both outcomes together. The current research will extend previous

research on longitudinal modelling by including both positive and negative outcomes

together in the models and by trimming the models to better understand the most

influential pathways in the models.

3.1.1 Hypothesis for Study 2

It is hypothesized that the longitudinal modelling will show evidence that

there is stability in the variables over time and that there are changes in variables

over time which will be the result of gain and loss spirals of resources. Gain and loss

spirals are evident in the significant reciprocal relationships between variables over

the measurement times. Specifically, it is expected that the greatest influence on a

variable at a later time will be from the same variable at the previous measurement

times (i.e. the auto-lagged paths), which will be taken as the stability of a variable

over time. In addition to the stability of variables, it is expected that there will be

smaller but important contribution from cross-lagged paths, such that personal and

workplace resources will increase positive functioning over time and that the

demands of negative spillover will increase burnout and mental illnesses over time.

These cross-lagged paths will represent the gain and loss spirals that lead to the

accumulation or loss of resources over time.

3.2 Methods

3.2.1 Participants

The sample consisted of participants from the alumni of a university and the

administrative staff from a large public hospital, who took part in the prospective

panel study. Of the individuals who completed all three time periods (N = 198,

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78.8% female), the participants ranged in age from 19 to 62 years (M = 38.18 years,

SD = 11.14 years), and worked on average about 41 hours per week across the three

time periods (Time 1, M = 40.96 hours, SD =11.91 hours; Time 2, M = 41.18 hours,

SD = 11.39 hours; Time 3, M = 41.58 hours, SD = 12.58 hours). Attrition analysis

found that there was no difference between participants who completed all three

measures and those that dropped out after Time 1, based on age (F(1, 462) = 0.024, p

= .877), gender composition (F(1, 462) = 0.174, p = .677), or the hours worked per

week at Time 1 (F(1, 462) = 0.121, p = .729). The full demographic details of the

participants will be given in the results section.

3.2.2 Recruitment of participants, survey methods and materials

The recruitment process and online survey methods are outlined in the

previous chapter on the cross-sectional analyses of the data. This chapter describes

the processes involved in the longitudinal modeling, building on the previous chapter

and using the same measures and data collection methods to construct a prospective

panel study used in Study 2. Briefly to recap, participants were recruited from a

university alumni e-magazine or by managers within the administrative section of the

public hospital to take part in an on-line survey. Of the original participants (n =

470), just under half (n = 198, 42.1%) competed identical surveys online at all three

time points. The details of the measures used, along with the scales‟ reliabilities were

given in Chapter 3, with Table 3.3 (pages 250-256) showing the correlations between

all the variables at Time 1.

3.2.3 General process for longitudinal modelling

Whilst the SEM analysis for this thesis will be conducted using the AMOS

program (Arbuckle, 2006), the initial multiple regressions was conducted by SPSS.

Whilst structural equation modelling using can investigate the relationships between

261

many variables, these relationships can be difficult to interpret when the variables are

closely related or correlated to each other (Zapf et al., 1999). Given the number,

diversity and breadth of variables that were measured in the current study, it was

considered very likely that there would be considerable overlap in the constructs and

an associated difficulty of obtained clear and interpretable results from the structural

equation modelling. The results of the cross-sectional analyses (i.e. the hierarchical

multiple regressions of Study 1) are detailed in Chapter 2 and were the first step to

simplifying the analytic process.

The second step of the longitudinal modelling involved pooling the results of

the hierarchical multiple regressions to find which variables were the most frequent

significant predictors of the developmental outcomes. The seven predictors were

dispositional optimism (8 of 12 outcomes) and coping self-efficacy (6 of 12),

affective commitment (7 of 12), skill discretion (8 of 12) and job autonomy (4 of 12),

and negative work-to-family spillover (9 of 12) and negative family-to-work

spillover (5 of 12). Although job autonomy predicted the least of these variables, it

was important to burnout and work engagement and therefore included in the

modelling. The seven predictors form distinct groupings of the individual and their

context. The groupings have been labelled Individual Factors (dispositional optimism

and coping self-efficacy), Positive Workplace Factors (affective commitment, skill

discretion and job autonomy), and Negative Spillover (negative work-to-family

spillover and negative family-to-work spillover), which provide a clear basis for the

subsequent analyses.

3.2.4 Introduction to SEM and associated terminology

To assist with the terminology and the many models involved in the analyses,

names of variables and models and a glossary of terms and fit indices are available in

262

Individual Factors

Dispositional

optimism

e1

1

1

Coping

self-efficacy

e21

Positive Workplace

Factors

Affective

commitment

e31

Job

autonomy

e41

Overall Well-Being

Psychological

well-being

e7

1

1

Life

satisfaction

e6

1

e8

1

Skill

discretion

e51

1

Appendix K. These are the last 2 pages of the appendices and can be easily accessed,

as a ready reminder for the reader throughout this chapter.

The AMOS program (Arbuckle, 2006) will be used for all the SEM analyses

conducted in this thesis. The process of structural equation modelling has two

separate phases. First, the structural component hypothesizes how latent variables

(constructs that are not easily measured, such as well-being or mental health) are

related to each other and second, the measurement component details the observed

indicator variables that best reflect or are „caused‟ by these latent variables (Holmes-

Smith, Cunningham, & Coote, 2006). Using Figure 3.1 as a simplified example of

the models that will follow, the researcher would theorise that in the structural part of

Figure 3.1. Simplified representation of the components used in SEM

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the model that the latent factors, „Individual Factors‟ and „Positive Workplace

Factors‟ are correlated and jointly influence the outcome, „Overall Well-Being‟. The

latent variables Individual Factors and Positive Workplace Factors‟ are independent,

or exogenous, variables, whilst the latent variable, Overall Well-Being, is a

dependent, or endogenous, variable. It is also necessary to specify a residual error,

„e8‟, as explanation of all other possible influences on „Overall Well-Being‟

Using Figure 3.1 again as an example of the measurement part of the model,

the researcher designates that the latent factor „Individual Factors‟ is best represented

by the measured variables, „Dispositional optimism‟ and „Coping self-efficacy‟, the

latent factor „Positive Workplace Factors‟ is best represented by the measured

variables, „Affective commitment‟, „Job autonomy‟ and „Skill discretion‟, whilst the

latent factor „Overall Well-being‟ is best represented by the measured variables, „life

satisfaction‟ and „psychological well-being‟. Previous research would be used to

guide the selection of suitable instruments and scales for the observed variables. As

the observed variables are not perfect estimates of the latent factors, it is necessary to

include a measurement error term to each observed variable, which are shown as „e1‟

through to „e7‟ in Figure 3.1 (Byrne, 2001; Holmes-Smith et al., 2006).

3.2.5 Assessing model fit

Once the model has been developed and the parameters estimated, the fit of

the model can be assessed. Traditional and SEM analyses differ in their tests of

significance, as in traditional analyses, the null hypothesis, Ho is tested and either

retained (if p > .05) or rejected (if p < .05). The desired outcome is that the research

will find a significant difference between the variables or conditions and the null

hypothesis, Ho can be rejected. In the modeling context however, the situation is

different. The assessment of the model‟s fit is based on testing the research

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hypothesis that there is a minimal discrepancy (i.e. no difference) between the model

calculated from the sample and an implied matrix drawn from the population. That

is, the model has good fit when the result of the significance test in SEM is p > .05,

indicating that the discrepancy is not significant. Larger p-values would indicate a

better fit of the model to the data. A Chi-squared (Χ2) test is used to assess the

difference between the implied variance - covariance matrix (Σ) and the empirical

variance – covariance matrix derived from the sample (S) (Byrne, 2001; Holmes-

Smith et al., 2006). In simple terms, SEM assesses how well the hypothesized model,

as drawn, matches or „fits‟ the sample data.

A number of measures assess the fit and parsimony of the model, based on

Χ2, the discrepancy function. The current thesis will use the Normed Chi-squared (Χ

2

/df), the Comparative Fit Index (CFI), the Root Mean Square of Approximation

(RMSEA) and its 90% confidence interval, Akaike‟s Information Criteria (AIC) and

the Expected Cross-Validation Index (ECVI), as measures of each model‟s

adequacy of fit and parsimony and to compare the models. Of these, the Normed

Chi-squared, the RMSEA (and the 90% confidence interval) and the AIC will be

used as the principle measures of fit and parsimony to assess which model has the

best fit, whilst CFI and ECVI will add to the understanding of the fitness of the

competing sets of models.

3.2.5.1 Normed Chi-Squared statistic. The Normed Chi-squared is derived

from the Chi-squared statistic and the model degrees of freedom. The Chi-squared

(Χ2) statistic tests the exact fit of the specified model but can be inflated by

increasingly large sample sizes with reasonable models rejected in large samples

(Holmes-Smith et al., 2006). In addition, the impractical expectation in large samples

that an implied model will exactly fit real world data leads to a significant Chi-

265

squared test with p < .05 (Byrne, 2001). The Chi-squared statistic ranges from 0

(zero) in the saturated model where all possible paths or relationships are included in

the model to the maximum value in the independence (or null) model, where there

are no paths or relationships and therefore no covariances in the model (Schmacker

& Lomax, 2004). The Chi-squared statistic for the specified model falls between

these limits, depending on the particular model being tested. Model degrees of

freedom, relevant for the test of significance of the Χ2 statistic, are calculated as the

number of observations (i.e. the variances and covariance among the observed

variables) less the number of free parameters (sample statistics to be calculated)

(Kline, 2006). Degrees of freedom increase as paths are added or decrease as paths

are removed from the model.

Given the uncertainty associated with using Χ2

alone, it is considered

preferable to use the Normed Chi-squared (Χ2/df), which divides the Chi-squared by

the degree of freedom for the model and accounts for the complexity of the model.

An acceptable range for Normed Chi-squared (Χ2/df) is considered to be between 1

and 2 (Holmes-Smith et al., 2006), although Schumacker & Lomax (2004) note that

models up to a Normed Chi-squared < 5 have acceptable fit but do require

improvement. From these ranges, an acceptable level for the Normed Chi-Squared

(Χ2/df) is considered to be between 1 and 3, with Normed Chi-squared of less than 1

indicate possible overfitting of the model to the data (Holmes-Smith et al., 2004).

3.2.5.2 Root Mean Square Error of Approximation (RMSEA). The Root Mean

Square Error of Approximation (RMSEA) is one of the more important indices, as it

allows for error in estimating the model in the population, includes sample size in its

calculation and is less rigid than the expectation of perfect fit inherent in the Chi-

squared statistic. In this way, the RMSEA assesses the closeness of fit and by

266

accounting for the parsimony of the model and gives better fit estimates for more

parsimonious models. RMSEA can be given as a point estimate, as a 90% confidence

interval and with a significance test of close fit (i.e. that the RMSEA < .05). As with

the significance test for X2, larger p-values indicate better fit, but interpretation of the

p-values for the test of close fit can be confused with the point estimates of the

RMSEA. As the confidence interval and the test of close fit give similar information

about the estimation of the RMSEA (Browne & Cudeck, 1993) and to avoid any

confusion, only the confidence interval will be reported in this thesis.

For the point estimates, an RMSEA equal to 0 indicates perfect fit, ≤ .05

indicate close or good fit, between .05 and .08 indicates reasonable fit, between .08

and .10 indicates mediocre fit and estimates over .10 indicate poor or unsatisfactory

fit (Browne & Cudeck, 1993; Byrne, 2001: Holmes-Smith et al., 2006; Kline, 2005).

Hu and Bentler (1998) have also noted that RMSEA ≤ .06 indicate good fit of a

model. A 90% confidence interval (C.I.) is calculated by AMOS, where a lower

bound estimate of .00 indicated that exact fit of the model can be supported (Holmes-

Smith et al., 2006). Whilst it may be difficult to achieve exact fit of a model, close fit

of the model can be supported when the lower bound estimate of the RMSEA in the

confidence interval is less than .05 (Browne & Cudeck, 1993) and reasonable fit is

supported with an upper bound estimate of .08 (Garson, 2007). However, a CI that

had an upper bound estimate > .10 would indicate poor fit of the model. As with all

confidence intervals, a narrow range indicates that there is less error the calculation

of the RMSEA (Byrne, 2001). The 90% confidence interval can also be used as a

basis to calculate the power of an analysis using the test of close fit (which indicates

that the CI includes .05), the degrees of freedom of the analyses and the sample size

(MacCallum, Browne, & Sugawara, 1996). Having sufficient power was important to

267

ensure that Type I errors are avoided and that the „true‟ state of reality can by

reflected in the modeling. As was demonstrated in the results of the longitudinal

modelling, the sample size in this thesis, combined with the number of parameters

involved in the models, provided sufficient power for the modelling (power ≥ .60) in

4 of the 5 models that have been tested.

3.2.5.3 Akaike Information Criteria (AIC). The Akaike Information Criteria

(AIC) is a predictive fit index of model adequacy, based on information theory of

data analysis, which is useful for determining the most parsimonious model in a set

of competing models. The calculation of the AIC is based on X2 and the number of

parameters estimated in the model, therefore taking into account the complexity of

the model. The acceptable level for the most parsimonious model is the lowest AIC

when comparing two or more models (Byrne, 2001; Kline, 2006). The trend for AIC

estimates is to fall as the fit of the model improves (reflected by the decreasing Chi-

squared statistic) but to increase again past the point of the most parsimonious

model, as an inverted U shape. The AIC allows for the selection of the most

parsimonious model among a set of competing models that are not hierarchical and

use the same data set, as is the case in the current thesis. The AIC therefore indicates

which model combines the best fit with the fewest parameters (Byrne, 2001; Holmes-

Smith et al., 2006; Kline, 2006).

3.2.5.4 Comparative Fit Index (CFI). In the past, the Goodness-of-Fit Index

(GFI) and the Normed Fit Index (NFI) have been used to assess model fit, but these

will not be used in this thesis as these fit indices do not have any penalty for adding

parameters and therefore complexity to the model (Byrne, 2001; Kenny, 2008).

Sample size can affect the assessment of fit using these indices, as the GFI can

overestimate fit in poorly specified models whilst the NFI can underestimate fit in

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small samples (Byrne, 2001). Based the model‟s Χ2

and including allowance for the

model‟s degree of freedom, the Comparative Fit Index (CFI) is an incremental index

that compares the specified model to the independence or null model. In this way, the

CFI is less sensitive to sample size and more responsive to model complexity (Byrne,

2001). The CFI is scaled from 0 to 1, where CFI ≥ .95 indicate good fit and estimates

of 1 indicate prefect fit (Holmes-Smith et al., 2006).

3.2.5.5 Expected Cross-Validation Index (ECVI). The Expected Cross-

Validation Index (ECVI) addresses the issue of cross-validation of a single-sample

model, when the sample size, as in for the current research, does not allow for

dividing the sample into a calibration and validation sub-samples. The ECVI tests

that the model would be valid in similar sized samples from the same population

(Byrne, 2001; Schmacker & Lomax, 2004). In smaller samples, dividing the data in a

calibration and validation samples for cross-validation can be problematic, as this

can increase the errors of approximation overall and lessen the reliability of the

outcome (Browne & Cudeck, 1993). The ECVI overcomes this by calculating the

fitted covariance matrix using the available data against the expected covariance in a

sample of similar size from the same population, checking the expected overall

discrepancy against all possible calibrations samples. It is then possible using a

single sample to determine if the model would cross-validate to other, similar

populations (Browne & Cudeck, 1993; Byrne, 2001). By comparing the ECVI

estimates of all the competing models, including that of the saturated model of all

possible paths, the smallest ECVI estimate is found and this model will be the most

likely to be replicated (Byrne, 2001) and to be the most stable (Schmacker & Lomax,

2000). This model will also guard against accepting a model that is based on chance

associations within a particular sample that do no apply across a population (Holmes-

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Smith et al., 2006). A 90% confidence interval (CI) can also be calculated for the

ECVI, which allows for an estimation of the precision of the calculated ECVI and is

a further useful method to compare alternative models (Schmacker & Lomax, 2004).

3.2.6 Early SEM models

Following the hierarchical multiple regressions described in the last chapter,

the significant predictors of the well-being and mental health outcomes were entered

into preliminary SEM analyses to gain an early understanding of the relationships

among the variables to be studied. As with the hierarchical multiple regressions, the

preliminary modeling were conducted using the Time 1 data set (N = 470). The early

models aimed to establish whether causal relationships between the variables,

representing individual differences, workplace factors and the work-life interface and

the well-being and mental health outcomes could be demonstrated. In all cases,

satisfactory, well fitted models could be achieved and provided sound bases for the

subsequent Confirmatory Factor Analyses.

For all of the models under consideration, the initial models were

hypothesized with three exogenous variables and the applicable endogenous

variables. From the results of the HMR, and drawn in a similar way to the simplifies

representation of SEM shown in Figure 3.1, the models were initially hypothesized

with three exogenous factors that were correlated to each other and with each

exogenous variable having a causal influence on each of the endogenous factors. The

endogenous variables were also considered to be correlated to each other in the initial

model.

The exogenous, or independent, latent variables were designated as

Individual Factors, as the observed indicators of dispositional optimism and coping

self-efficacy; Positive Workplace Factors, as the observed indicators, job autonomy,

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skill discretion and affective commitment; and Negative Spillover, as the observed

indicators of negative work-to-family spillover and negative family-to-work

spillover. Whilst the measurement errors for the observed variables for the

independent variables were also exogenous variables (Holmes-Smith et al. 2006), for

the purposes of the current discussion, only the latent variables were considered.

The endogenous, or dependent, variables change with the model under

consideration. For the first early SEM of positive outcomes, the two endogenous

variables were Work Well-being/Engagement, as the observed indicators of work

satisfaction, work vigour, work dedication and work absorption and Overall Well-

Being, as the observed indicators of life satisfaction and psychological well-being.

For the second early SEM of negative outcomes, the endogenous latent variables

were Mental Illness, as the observed indicators of depression, anxiety and stress and

Burnout, as the observed indicators of exhaustion, cynicism and professional

efficacy. The third model combines all the positive and negative outcomes. These

early SEMs provided the basis for the next step of the confirmatory factor analyses.

3.2.7 Confirmatory Factor Analysis (CFA)

Confirmatory factor analyses (CFAs) were measurement models, where the

relationships between the latent and indicator variables were examined. Figure 3.2

shows a simplified representation of the CFAs used in this thesis, with the latent

factors which reflect or are said to cause the observed, or indicator, variables.

Indicator variables can be either the measured predictor or outcome variables. In the

simplified example of the proposed models shown in Figure 4.2, the latent factor,

„Individual Factors‟ underlies or „causes‟ the indicator variables, „Dispositional

optimism‟ and „Coping self-efficacy‟ and the latent factor, „Overall Well-Being‟

causes the indicator variables, „Life satisfaction‟ and „Psychological well-being‟.

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Individual

Factors

Overall Well-Being

Dispositional

optimism e111

Coping

Self-efficacy e21

Life

satisfaction e31

1

Psychological

well-being e41

Figure 3.2. Simplified representation of the confirmatory factor analyses

As with Figure 3.1, measurement error terms, „e1‟ to „e4‟ are included as the

observed variables are not perfect estimates of the latent factors.

3.2.8 Models to be considered in the CFAs and for longitudinal modeling

In each of the CFAs that have been conducted, appropriate latent variables

accounted for the multiple predictors and multiple outcomes required for each

individual analysis, as detailed in Table 3.1. Using the Time 1 data set (N = 470),

separate confirmatory factor analyses (CFAs) were conducted in AMOS (Arbuckle,

2006) to develop models of the relationships between the latent outcome and

predictor variables. Five models were considered in the thesis to explore and

understand the longitudinal relationships between the individual, their environment

and their well-being and mental health. As noted by Seligman and Csikszentmihalyi

(2000), the focus of much psychological research in past decades has been on

understanding mental illness and finding ways to alleviate the symptoms of such.

Positive psychology, as an antidote, is orientated toward understanding and exploring

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Table 3.1

Latent and observed variables used in confirmatory factor analyses

Latent variable SEM Label Observed indicator variables used in CFAs

Overall Well-Being OWB Life satisfaction, psychological well-being

Work Well-Being WWB Work satisfaction, work dedication,

work absorption

Mental Illness MI Depression, anxiety, stress

Burnout Burnout Exhaustion, cynicism, professional efficacy

Work Engagement WE Work dedication, work absorption,

professional efficacy

Individual Factors IF Dispositional optimism, coping self-efficacy

Positive Workplace Factors

PWF Affective commitment, job autonomy, skill

discretion

Negative Spillover NSP Negative work-to-family spillover, negative

family-to-work spillover, exhaustion (only

included in final Integrated model)

the strengths and positives of human functioning. The first and second longitudinal

models considered this divide between positive and negative outcomes. The first

longitudinal model explored the positive outcomes of overall well-being and work

well-being as an analysis of Well-Being and the second longitudinal model explored

the negative outcomes of mental illness and burnout as an analysis of Mental

Distress.

However, positive and negative outcomes do not occur in isolation from each

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other (Bohart, 2002) and it was important to consider and explore together the

relative influences of the positive and negative over time, which could then account

for that the multiple influences on an individual. As such, the third and fourth

longitudinal models looked at the combination of positive and negative outcomes.

The third longitudinal model was an analysis of Well-Being - Mental Health,

combining overall well-being and mental illness (in a similar manner to

Keyes‟(2002, 2005) research). The fourth model was an analysis of Work

Engagement, which explored the affective states within burnout (as exhaustion,

cynicism and professional efficacy) and work engagement (as work vigour, work

dedication and work absorption) and extended recent research on the formulation of

these constructs (for example, Schaufeli et al., 2008). The fifth longitudinal model

included all the outcomes to explore a more complete, Integrated model which had

not been published elsewhere and brought together all influences on the individual

for an overall understanding their lives.

3.2.9 Constructing composite variables for the longitudinal models

When an acceptable CFA model has been shown to have good fit, a

composite variable can be calculated. Factor score weights were derived from the

CFA and converted the latent unmeasured variable to an observed measured

variable. A limitation of using many observed and latent variables in modelling of

longitudinal data was that the models can be unstable and confounded by the

difficulties of estimating numerous parameters unless the sample size is very large.

The composite variables overcome this serious limitation (Holmes-Smith et al.,

2006). The reliability of the composite variables was also assessed by examination of

the squared multiple correlation of each indicator variable that contribute to the

composite variable. Squared multiple correlations (SMC) of greater than .30 were

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acceptable, with SMC greater than .50 indicating a good observed variable (Holmes-

Smith et al., 2006).

The calculation of the composite variables for each participant was stated

formally as ξ = ωX, and calculated for the ith

participant with n indicator variables, as

ξi = ω1X1i + ω2X2i + … + ωnXn (2)

(Holmes-Smith et al., 2006). That was to say that the estimated composite score for

the new variable was the sum of the factor score weight (ω) for the particular

observed indicator variable multiplied by the participant‟s scores (X) for that

observed indicator variable. Factor score weights therefore represent the proportion

that each of the measured variables contributes to the latent variables, based on the

relationships found in the confirmatory factor analyses (Holmes-Smith et al., 2006).

For example, in the Well-Being - Mental Health model, the mental illness latent

variable at Time 1 became the measured Mental Illness composite variable,

MIwbmh1, through the following equation (3):

MIwbmh1 = [Depression tm1*.519] + [Anxiety tm1*.083] + [Stress

tm1*.156] + [life satisfaction tm1*-.019] + [psychological well-being tm1*-

.020] + [negative family-work spillover tm1*.146] + [negative work-family

spillover tm1*.032] + [affective commitment tm1*.008] + [job autonomy

tm1*.023] + [skill discretion tm1*-.034] + [coping self-efficacy tm1*-.011] +

[dispositional optimism tm1*-.081]. (3)

Note that „tm1‟ at the end of each variable indicates that this was the

participant‟s score at Time 1 for each of the following measured variables. From this

example, it can be seen that whilst depression, anxiety and stress contribute greatly to

the composite variable MI (mental illness), every other variable also contributed

something to its final calculation. These contributions can be both intuitive and

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counterintuitive. In the example above, life satisfaction (SWLStm1) had the

expected, negative contribution to the mental illness composite variable, whilst job

autonomy (JobAUTtm1) has an unexpected, positive contribution. This could be

explained as life satisfaction acting as a buffer for the individual against mental

illnesses (i.e. less life satisfaction increased mental illness) but that greater autonomy

or decision making in one‟s job could add to the stress that the individual

experiences, leading to greater mental illnesses.

3.2.10 Naming the composite variables

Naming the new variables took into account the different models for which

they were constructed, such that the labels were as clear as possible in indicating to

which model the variables belong. For example, as a general term, IF referred to

individual factors, whilst IFwb referred to the individual factors variable in the Well-

Being model, IFmi referred to the individual factors variable in the Mental Distress

model, IFwbmh referred to the individual factors variable in the Well-Being - Mental

Health model, IFwa referred to the individual factors variable in the Work

Engagement model, and IFcm referred to the individual factors variable in the

complete, Integrated model. The labels were therefore derived from a specific CFA

and relate to a particular model. As such, „wb‟ was the label for the Well-Being, „mi‟

for the Mental Distress model, „wbmh‟ for the Well-Being - Mental Health model

that combined well-being and mental health, „wa‟ for the Work Engagement model,

and „cm‟ for the complete Integrated model. The label „im‟ was considered for the

Integrated model, but discarded because of the likely confusion between the „mi‟ of

the Mental Distress model and the „im‟ of the Integrated model.

It should be noted that it was not appropriate to use the composite variables in

any other analysis than for the one for which it is calculated. By labelling the

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composite variables in this way, it remained clear which variables were to be

included in which analyses. When a general analytic strategy is described however,

the general label of IF can be used rather than the particular labels, IFwb, IFmi,

IFwbmh, IFwa and IFcm, as the general process rather than specific models are being

described. It should also be noted that „1‟, „2‟, and „3‟ at the end of a variable name

indicated that the variable was relevant to Time 1, Time 2, and Time 3 respectively.

3.2.11 Calculations of the composite variables

The composite variables were calculated in SPSS using the prospective panel

data set of participants (n=198) that provided data at all three time periods. As noted

in Chapter 2, attrition analysis showed that the participants who were „lost‟ from the

earlier rounds of data collection were not significantly different to those people who

completed measures at all three time periods. Cross-validation of the SPSS

calculations of the composite variables was conducted using hand calculations with

these composite scores matching the scores calculated by SPSS. Each new variable

was calculated separately for Times 1, 2 and 3, using the observed indicator variables

of that particular time period. As shown in the example above on calculating the

composite variable of mental illness at time 1, MIwbmh1, only Time 1 observed

variables were used. To calculate the new Time 2 and Time 3 composite variables for

mental illness, the Time 1 variables (e.g. depression tm1) were replaced by the

observed Time 2 (depression tm2) and Time 3 (depression tm3) variables,

respectively. In this way, the panel data set now contained composite variables

suitable to use in the longitudinal analyses.

3.2.12 Analytical strategy for longitudinal modelling

3.2.12.1 Set of models to be compared. The panel data was analysed

in AMOS (Arbuckle, 2006), using the Maximum Likelihood (ML) method.

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Structural equation modelling is useful in longitudinal analyses as it accounts for any

errors in the measurement of the variables, it allows for all causal relationships to be

simultaneously estimated and for reciprocal relationships to be introduced and for the

methodological limitations of multiple regression to be overcome (Zapf, Dormann, &

Frese, 1996). Whilst there is limited research that investigates longitudinal

relationships around the work-life interface, the available research is consistent in its

approach. Following the procedures outlined de Jonge, Demerouti, Bakker and

colleagues in their investigations of burnout and work engagement (de Jonge et al.,

2001; Demerouti, Bakker et al., 2004; Llorens et al., 2007), the set of the models

were compared in the following sequence of Stability, Causality, Reverse Causality,

and Reciprocal Models. This sequence represented a progression of model building,

starting on the basis of the Stability model and culminating in the most complex, the

Reciprocal model. These models were not nested models however as the Causality

and Reverse Causality cannot be derived from each other (Kline, 2006). This thesis

Figure 3.3. Representation of the basic relationships to be tested in the longitudinal

analyses

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took the longitudinal model approach a novel step further by investigating the effects

of model trimming, in that the pathways that have trivial contributions to the model

were removed to better understand the influential pathways of the better fitting

models.

The explanations of the set of competing models were based on the simplified

relationships as shown in Figure 3.3. The composite predictor variables of individual

differences, positive workplace factors, and negative spillover were represented as

predictor1, predictor2, and predictor3, at Time 1, Time 2 and Time 3 respectively.

Similarly, the composite outcome variables of overall well-being, work well-being,

work affect, mental illness and burnout are represented as outcome1, outcome2, and

outcome3 at Time 1, Time 2 and Time 3, respectively. By convention, correlations

were drawn as double-headed arrows, whilst causal relationships were drawn as

single-headed arrow, pointing from the causal variable to the variable being

influenced (Holmes-Smith et al., 2006). As noted previously, there was not perfect

measurement of the indicator variables and measurement errors are added to the

model, shown as „e1‟ to „e6‟.

The initial models did not include the longer term stability elements (i.e.

causal arrows) from Time 1 to Time 3, but the fit of all models within the set of

competing models were substantially improved by the addition of these paths. For

example, the AIC of the Well-Being Stability model was reduced from 285.58 to

178.40 and the RMSEA was reduced from an ill-fitting and unacceptable figure of

.147 to a better, although still mediocre fit of .091 by the inclusion of Time 1 to Time

3 auto-lagged paths. Similar dramatic improvements were found for the fit indices

for the Causality, Reverse Causality and Reciprocal models and the auto-lagged

paths from Time 1 to Time 3 were therefore included as standard in each of the

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longitudinal models.

Model A is the Stability model represented the synchronous correlations

between the variables (i.e. the cross-sectional relationships), along with the auto-

lagged relationships (i.e. the relationships between the same variable across time),

which represent the temporal stability of each variable from Time 1 to Time 2, Time

2 to Time 3 and Time 1 to Time 3. The Stability model therefore accounted for

concurrent functioning and the short-term and longer term stability of each variable.

These results show that the individual‟s current functioning had significant

contributions from functioning in the recent and the more distant past.

Model B was the Causality model, which incorporated the stability model and

added the additional cross-lagged paths of Time 1 to Time 2 and from Time 2 to time

3. However, the Time 1 to Time 3 paths, i.e. „predictor1‟ to „outcome3‟, were not

included in the final models as these paths reduced the goodness of the model fit and

each path was highly non-significant. For example in the Well-Being model, adding

the cross-lagged paths from Time 1 to Time 3 paths in the Causality model increased

the AIC from 135.39 to 141.27 and increased the RMSEA from .037 to .044,

indicating a reduction in model fit. In summary, the causality model, Model B,

represents how predictor variables lead to changes in the outcome variables at a later

time, in addition to concurrent functioning and short and long term stability.

Model C was the Reverse Causality model which incorporated the stability

model and added the reverse causal relationships between the outcome variables and

the predictor variables, positive workplace factors and negative spillover, which were

cross-lagged from Time 1 to Time 2 and Time 2 to Time 3. For example, the

pathways would include each reverse causal pathway from „outcome1‟ to

„predictor2‟ and „outcome2‟ to „predictor3‟. Similarly to Model B, cross-lagged

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pathways from Time 1 to Time 3 were not included, as these paths reduced the

model fit. For example, in the Well-Being Reverse Causality model, the addition of

the Time 1 to Time 3 paths, that is „outcome1‟ to „predictor3‟, increased the AIC

from 142.18 to 147.23, and increased the RMSEA from .051 to .057. These changes

again indicated that these paths did not add to the fit of the model. In summary, the

reverse causality model, Model C, tested the causal influence of variables, which are

usually only considered as outcomes of developmental processes, in addition to

concurrent functioning and short and long term variable stability.

Model D was the Reciprocal model, which incorporated the Stability,

Causality and Reverse Causality models, such that all the relationships of the

previous models can be considered together. For the same reasons noted for Models

B and C, Model D did not include the Time 1 to Time 3 cross-lagged pathways.

Model D allowed for the inclusion of the causality and reversed causality pathways

that are likely to be influential to longitudinal functioning.

3.2.12.2 Model trimming. The process of building up the models, from the

basic Stability model to the more complex Reciprocal model, allowed for the

consideration of how each of these added pathways increased or decreased model fit.

The next phase was to remove superficial pathways to better understand which of the

pathways could be considered responsible for influencing functioning at a later time,

i.e. having a causal effect over time. In the five longitudinal models, either the

Reciprocal model alone or the Reciprocal model tied with the Causality model to be

the best fitting model before model trimming was considered. The final step in the

comparison of the competing models, the trimmed model is designated as Model E

and explored the effect of removing the trivial pathways on the fit and explanatory

power of the model (Garson, 2007; Kline, 2006). Whilst a number of the cross-

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lagged paths were statistically not significant, the purpose was to remove those paths

with minimal variance (≤ 1%), measured as standardized regression weights (β

weights) of β ≤ .10, that were non-significant (p > .20), as opposed to removing all

non-significant pathway. In this way, it was expected that only trivial paths would be

removed. Kline (2006) noted that it is not necessary to remove all the non-significant

paths, where either sample size or power are small. Further, he stated that true

nonzero causal paths may be non-significant in a particular sample and their removal

would lead to Type II errors and loss of explanatory power. Removing the non-

significant paths also guards against Type I errors such that chance relationships

would not be considered important.

In the current thesis, the sample size (N = 198) was considered adequate,

although the power of the analyses, which was lower for the less complex models

than for the Integrated model, may raise issues of replication (MacCallum et al.,

1996). Kline (2006) also suggested retaining the paths until replication of the model

in another sample could show if the paths were meaningful or had a negligible effect.

The Expected Cross-Validation Index (ECVI) has been given for each of the models

in an effort to address the issue of reproducing the results of the modeling in the

current thesis in other samples, given the consideration of sample size and power,

with the lowest ECVI indicating the model most likely to be replicated in a similar

sample drawn from the same population. It was important to show that the saturated

model, where all possible pathways are estimated did not have the lowest ECVI,

which would indicate that despite a model having acceptable fit indices, the proposed

model would not be the most likely to be replicated (Browne & Cudeck, 1993).

3.2.13 Summary of methods used for the longitudinal modeling

This chapter has outlined the process involved in longitudinal modelling.

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Beginning with hierarchical multiple regressions which clarified the most common

significant predictors of the well-being and mental health outcomes (and detailed in

Chapter 2), the second step was the development of the initial structural equation

models to explore the causal relationships between predictors and outcomes. After

satisfactory models were achieved, the third step was the confirmatory factor

analyses (CFAs) which were conducted to show the dynamic nature of the

relationships that occur synchronously. From the CFAs, composite variables were

then constructed to represent these dynamic relationships and to test the stability and

reciprocity of relationships within and between the composite variables across the

three time periods. The final step was to compare the sets of longitudinal models and

the effect of removing the trivial pathways, such that the best fit of the longitudinal

models was established.

By finding the model that was the best fit of the data, Study 2 will allow the

influential developmental pathways to be established. In this way, the intricate web

of cause and effect can be revealed to extend the understanding of how the

competent adult develops, how well-being and mental health are maintained over

time, and where interventions to improve mental health may be most effective.

3.3 Results of the Longitudinal Modeling

3.3.1 Sample size and characteristics

The sample for the longitudinal analysis of well-being and mental health was

drawn from the participants of the on-line surveys, described in Chapter 3 on

Hierarchical Multiple Regression. Only those participants that completed the on-line

surveys at all three time periods (N = 203) were included in the data set with 5

participants removed as being multivariate outliers, leaving a sample of N = 198.

Unlike multiple regression, where N = 50 + 8K (Tabachnick & Fidell, 2006) is

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accepted as the guide for the minimum sample size, there appears to be no definitive

minimum sample size for SEM, with the definition of a small sample ranging from N

≤ 50 to N ≤ 250 (Hu & Bentler, 1998; Kline, 2006). It was necessary to consider

whether the sample for the proposed analyses was sufficient to ensure adequate

power. A recent proposal that sample sizes of less than 200 be rejected for

publication (Barrett, 2007) has been rebutted vigorously by a number of authors (for

example, Bentler, 2007; Goffin, 2007; Hayduck, Cummings, Boadu, Pazderka-

Robinson, & Boulianne, 2007). Barrett‟s cut-off does not take into account the

number of parameters involved (and hence, model degrees of freedom) or the power

of the analyses (Bentler, 2007; Goffin, 2007), nor previously published articles (for

example, Llorens et al., 2007) that have found well-fitting models in small samples.

To address the issue of sample size for the proposed analyses, two methods

were used to calculate the adequacy of the sample size. First, a ratio of sample size to

observed variables (n:v) can be used as a guideline for a minimum sample size, using

the ratio of 10:1 or 15: 1 as a minimum (B. Thompson, 2000). For the most complex

longitudinal model analysed in the current thesis (the Integrated model), there were

198 participants and 18 composite variables used as observed variables, giving a

ratio of 11:1, which was above the minimum desired ratio. In the least complex

model, the Well-Being model, there were 198 participants and 12 composite

variables, for a ratio of 16.5:1. A second method for determining the sufficiency of

the sample size used the distributions of the Root Mean Square of Approximation

(RMSEA) to calculate the power of the test of closeness of fit, based on the model

degrees of freedom and the sample size (MacCallum et al., 1996). Given these inputs

of N= 198 and model degrees of freedom ranging from 22 (for the Well-Being

model) to 64 (for the Integrated model), the best fit in the sets of models proposed

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and tested in the current thesis had an estimated power to find a close fitting model

of 48% (Well-Being model) to 85%, respectively (Integrated model). From both

methods of assessing sample size, it was judged that for the current thesis, the sample

was sufficient to provide adequate power to test that the models can provide evidence

of close fitting models. Replication of the models in another sample is of course

desirable to confirm the generalisability of the models.

The sample consisted of participants from the alumni of a university and the

administrative staff from a large public hospital who took part in the prospective

panel study outlined in Chapter 2. The individuals who completed all three time

periods were very similar to the participants at Time 1. There were 156 women

(78.8%) and 42 (21.2%) men, ranging in age from 19 to 62 years (M = 38.18 years,

SD = 11.14 years), and working around 41 hours per week across the three time

periods (Time 1, M = 40.96 hours, SD =11.91 hours; Time 2, M = 41.18 hours, SD =

11.39 hours; Time 3, M = 41.58 hours, SD = 12.58 hours).

As with the Time 1 data, the participants were mostly married or living with

their partner (63.6%) or single, having never been married (25.3%). Over half of the

sample did not have children, whilst most who were parents had two or three

children (75.5% of parents). Education attainment was again similar between the

Time 1 data and the panel data, those who completed only high school (17.3%),

those with trade or technical qualifications (9.1%), and those with undergraduate

degrees (49.7%) to those with postgraduate qualifications (23.9%).

Around half of the sample would like to work a few less hours per week at

each time period (Time 1, 48.5%; Time 2, 51.1%; Time 3, 55.1%), with around 30%

preferring to work about the same hours as they currently work (Time 1, 34.7%;

Time 2, 30.8%; Time 3, 28.8%). In this sample, only 4 (at Time 1) and 3 (at Times 2

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& 3) participants (1.5%) preferred a few more hours per week but no one wanted to

work many more hours than they were currently working. It was interesting to note

that whilst there were significant, negative relationships between the hours

individuals worked and their preferences for more or less work hours (i.e. work

longer hours, prefer to work less hours), neither the actual nor the preferred length of

the working week were among the significant predictors of the well-being and

mental health outcomes. As such, the relationships between actual and preferred

work hours was not explored further.

3.3.2 Assessing the fit and parsimony of the models

To reiterate from the previous chapter on the methods involved in the

longitudinal modelling, a poorly fitting model was considered to be incorrectly

specified, as it did not reproduce the sample covariance matrix adequately (Byrne,

2001; Kline, 2006). From the initial hypothesized models to the final, fitted models,

the model can be respecified or revised to improve the fit between the model and the

sample covariance matrix. Paths can be removed or added based on the Modification

Indices (an indication of the change in X2 when a particular parameter was estimated

in the revised model) and a consideration of the statistical significance of the paths.

However, it was important to consider that any changes were theoretically

meaningful and not driven solely by statistical concerns (Holmes-Smith et al., 2006).

As noted earlier in the chapter, acceptable levels for the fit indices for SEM was

taken as follows: Normed Chi-Squared (X2/df) between 1 and 3; Comparative Fit

Index (CFI) ≥ .95; the point estimate of RMSEA, equal to .00 (perfect fit), ≤ .05

(close or good fit), .05 to .08 (reasonable fit), .08 to .10 (mediocre fit) and > .10

(unsatisfactory fit); and for the RMSEA 90% confidence interval (CI), a lower bound

estimate of .00 indicated perfect fit and a CI that had an upper bound estimate > .10

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would indicate poor fit of the model. For the longitudinal models, Akaike‟s

Information Criteria (AIC) and the Expected Cross-Validation Index (ECVI) are

given, with the lowest value within a set of models being compared the most

parsimonious and the most likely to be replicated, respectively (Browne & Cudeck,

1993; Byrne, 2001; Kline, 2006; Schmacker & Lomax, 2004).

3.4 Time 1 SEMs as a basis for longitudinal models

The modelling was intended, first, to establish that models could be

satisfactorily fitted to the data and, second, to understand the nature and direction of

the relationships in the models. Three SEMs were conducted, using the Time 1 data

(N = 470) for the positive outcomes, the negative outcomes and for a combination of

all the outcomes. These three models formed the basis for the subsequent

longitudinal analyses. The full explanations of the Time 1 models are shown in

Appendix H, with figures of the models and model summaries. There is limited detail

here as the main purpose of the initial models was to establish whether the proposed

relationships would be supported by the data.

For all of the models under consideration, the initial models were

hypothesized with three exogenous variables and the applicable endogenous

variables. Based on previous research and the correlations in Table 2.3, the models

were initially hypothesized as first, that the three exogenous factors were correlated

to each other and second, that each exogenous variable had a causal influence on

each of the endogenous factors. The endogenous variables were also considered to be

correlated to each other in the initial Time 1 model.

The exogenous latent variables for the three models were designated as

Individual Factors (as the observed indicators of dispositional optimism and coping

self-efficacy), Positive Workplace Factors (as the observed indicators, job autonomy,

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skill discretion and affective commitment) and Negative Spillover (as the observed

indicators of negative work-to-family spillover and negative family-to-work

spillover). Whilst the measurement errors for the observed variables for the

independent variables are also exogenous variables (Holmes-Smith et al. 2006), for

the purposes of the current discussion, only the latent variables were considered.

The endogenous variables changed with the Time 1 model under

consideration. For the first Time 1 model of the positive outcomes, the two

endogenous variables were Work Well-Being (as the observed indicators of work

satisfaction, work vigour, work dedication and work absorption) and Overall Well-

Being (as the observed indicators of life satisfaction and psychological well-being).

Modification indices were used to consider how best to improve the fit of the models,

removing work satisfaction and work vigour from the Work Well-Being and

removing Negative Spillover as an exogenous latent variable. The final model for the

positive outcomes was well fitting, X2/df = 1.758, CFI = .992, RMSEA = .041 (90%

CI = .011-.066). For the second Time 1 model of negative outcomes, the endogenous

latent variables were Mental Illness (as the observed indicators of depression, anxiety

and stress) and Burnout (as the observe indicators of exhaustion, cynicism, and

professional efficacy). Modification indices indicated that fit would be improved by

removing stress and exhaustion and the result of the first model of negative outcomes

had acceptable fit, X2/df = 2.309, CFI = .970, RMSEA = .053 (90% CI = .038 -

.069). Another model, with the same exogenous variables, was tested with stress and

exhaustion as the outcomes. This second model of negative outcomes also had

acceptable fit, X2/df = 1.616, CFI = .997, RMSEA = .037 (90% CI = .000 - .089).

With these Time 1 structural models establishing that the well-being and

mental health outcomes could be modelled separately in this data, the next step was

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to combine the initial three models to find if these would form a tenable overall

model that included all outcomes. The Time 1 SEM that combined all the outcomes

had the three exogenous latent variables of Individual Factors (as dispositional

optimism and coping self-efficacy), Positive Workplace Factors (as skill discretion,

job autonomy, and affective commitment) and Negative Spillover (as negative work-

to-family spillover and negative family-to-work spillover) which were correlated

with other and separately having a causal influence on the three endogenous latent

variables of Mental Illness (as depression, anxiety and stress), Work Engagement (as

work dedication, work absorption, professional efficacy and cynicism) and Overall

Well-Being (as life satisfaction and psychological well-being). Cynicism was

removed from the model to improve fit with some additional paths between indicator

variables. The final model in shown in Appendix H, Figure H.4, and had acceptable

fit, X2/df = 2.473, CFI = .965, RMSEA = .057 (90% CI = .046 -.068).

The relationships between the individual, their workplace and problems

within the work-life interface have distinct effects of the well-being, mental health

and work engagement of the participants involved in the current thesis. Using the

Time 1 SEMs as a basis, the next step was the confirmatory factor analyses for each

of the five designated models, with various combinations of the positive and negative

outcomes, and then developing and testing the longitudinal models.

3.5 Confirmatory factor analyses (CFAs)

From the early SEMs using the Time 1 data, there were five models to be

examined through the Confirmatory Factor Analyses (CFAs) and then as longitudinal

models. The first model of Well-Being combined the positive outcomes of Overall

Well-Being (as life satisfaction and psychological well-being) and Work Well-Being

(as work dedication and work absorption), the second model of Mental Distress

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combined the negative outcomes of Burnout (as exhaustion, cynicism, and

professional efficacy) and Mental Illness (as depression, anxiety and stress). The

third model of Well-Being – Mental Health combined Overall Well-Being and

Mental Illness, the fourth model of Work Engagement explored and refined Burnout

and Work Engagement in a study of Work engagement and the fifth Integrated model

brought together all the outcomes to consider all possible relationships. The CFA and

subsequent factor score weights of each model are considered in turn, before

examining the results of the longitudinal models themselves.

Whilst this may seem to be a long process to arrive at the final longitudinal

models, I believe that the exploration of the combinations of the positive and

negative outcomes illustrated the holistic view of psychological functioning where

„everything‟ can be included. For example, the combination of burnout and work

engagement was particularly interesting and gave unexpected results which were

quite different to much of the prevailing research. This step by step approach,

building up to the final Integrated model allowed for all of the relationships in this

sample to be fully explored.

3.5.1 Confirmatory factor analysis of Well-Being model

Building on the basis of the model of positive outcomes at Time 1, a

confirmatory factor analysis (CFA) was conducted for the Well-Being model. It

should be noted that unlike the graphical arrangement of the Time 1 SEMs with the

causal relationships of the structural model, the CFA shows a measurement model,

where the latent factors are considered to be correlated. In this way, factor score

weights were generated to allow the transformation of latent, unobserved variables to

observed variables for the longitudinal models (de Jonge et al., 2001; Holmes-Smith

et al. 2006). The fit and parsimony indices for the Well-Being CFA, shown in

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Appendix I, Figure I.1 are as follows and indicated a reasonable fit of the CFA; X2/df

= 1.987, CFI = .949, RMSEA = .046 (90% CI = .019-.072). The standardized

regression weights (β) and squared multiple correlations between the latent factors

and the observed indicators and the correlations between the latent factors and

correlations between the observed variables are shown in full in the Appendix

(Tables I.1 and I.2). The results of the CFA show that each indicator variable loads

well onto the latent variables, with the loadings ranging from β = .557 (p <.001) for

job autonomy to β =.994 (p < .001) for work dedication. The correlations between

the latent factors reflect the relationships evident in the early SEM, in that Individual

Factors and Overall Well-Being were closely related (r = .924, p < .001) and Positive

Workplace Factors and Work Well-Being were also closely related (r = .882, p <

.001). There were also positive correlations between Individual Factors and Positive

Workplace Factors (r = .490, p < .001) and Work Well-Being (r = .463, p < .001),

Positive Workplace Factors and Overall Well-Being (r =.546, p < .001) and between

Overall Well-Being and Work Well-Being (r = .460, p < .001). The correlations

between the indicator variables indicated that job autonomy had a relationship with

life satisfaction (r = .177, p = .039), over and above the specified relationships

through the latent variables of Positive Workplace Factors, Work Well-Being and

Overall Well-Being respectively.

3.5.2 Factor Score Weights for Well-Being model

Based on the CFA, factor score weights were calculated by AMOS to reflect

the relationships within the CFA and are shown in Table 3.2. The composite

variables were then calculated in SPSS as the weighted sum of each indicator

variable, based on the individual‟s score for an indicator variable multiplied by the

factor score weight for that indicator variable. The factor score weights can therefore

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be viewed as a raw weighting for each scale, without any implication of significance

of the predictors. Most scales have similar numbers of items (between 4 and 7) and

were based on a similar rating scale (from 1, strongly disagree to 5, strongly agree).

Psychological well-being had more items (18) but on the same rating scale (1 to 5)

whilst coping self-efficacy was the exception to both, with the scale having 26 items

and being rated on 1 (not at all certain) to 7 (completely certain). These differences

must be kept in mind when interpreting the factor score weights. For example, from

Tables 3.2, dispositional optimism with 6 items, had a maximum score of 30 that

could be multiplied by the factor score weight for IFwb .162 and would be weighted

as 4.860, whereas coping self-efficacy, with 26 items had a maximum score of 182

that could be multiplied by the factor score weight for IFwb of .032 and would be

weighted as 5.824 in the composite variable, IFwb.

The two highest contributors to each composite variable are highlighted in

bold in Table 3.2. For Overall Well-Being (OWB) the two highest contributors were

the two indicator variables of the corresponding latent variable in the CFA, whilst for

(WWB), the contributions were more varied. For Individual Factors, the highest raw

contribution come from dispositional optimism and psychological well-being, with

less weight given to the larger scale for coping self-efficacy. For both Positive

Workplace Factors and Work Well- Being, the highest raw contributors were work

dedication and skill discretion, although at different levels as shown in Table 3.2.

Work Well-Being was overwhelmingly composed of the indicator variable, work

dedication rather than skill discretion, whereas the contribution for Positive

Workplace Factors was work dedication and skill discretion was more evenly

distributed. Despite these obvious linkages, it was important to keep the composite

variables separate, to allow the conditions of a positive workplace and work

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Table 3.2

Factor Score weights for composite variables for the Well-Being model

Weighted contribution of observed variables to each

composite variable

Observed variable IFwba PWFwb

a WWBwb

a OWBwb

a

Dispositional optimism .162 -.006 .008 .221

Coping self-efficacy .032 -.001 .002 .044

Skill discretion -.009 .263 .079 .091

Job autonomy -.018 .114 .035 -.006

Work dedication .067 .451 .830 .050

Work absorption .005 .033 .060 .004

Psychological well-being .133 .037 .004 .435

Life satisfaction .116 .020 -.001 .374

Note. a IFwb, PWFwb, WWBwb, and OWBwb are the Individual Factors, Positive Workplace

Factors, Work Well-Being, and Overall Well-Being variables, respectively, for the Well-Being

longitudinal model

Note. Two highest factor score weights for each composite variable highlighted in bold

well-being to be studied separately to understand the processes involved, rather than

the processes be hidden within a single composite variable.

3.5.3 Confirmatory factor analysis of the Mental Distress model

From the early SEMs, a CFA was conducted to consider the negative

outcomes of Mental Illness (as depression, anxiety and stress) and Burnout (as

exhaustion, cynicism, and professional efficacy). The negative outcomes were

designated as two latent factors, Mental Illness and Burnout and these outcomes were

considered along with Individual Factors (as dispositional optimism and coping self-

efficacy), Positive Workplace Factors (as skill discretion, job autonomy and affective

commitment) and Negative Spillover (as negative work-to-family spillover and

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negative family-to-work spillover). Using the Modification Indices, the final CFA is

shown in Appendix I, Figure I.2 with the fit indices for the CFA being X2/df = 2.837,

CFI = .888, RMSEA = .063 (90% CI = .050-.076). Given that this CFA combined

the two SEMs of the negative outcomes, it was reasonable to expect and accept some

„untidiness‟, in that there would be a number of correlations between the

measurement errors, but fit of the CFA was acceptable although near the upper limits

of acceptability (e.g. X2/df ≤ 3). Whilst stress and exhaustion could be removed to

minimize the cross-loadings indicated by the correlations, the improvement in fit was

not sufficient to justify leaving out such important constructs.

The correlations between the latent factors show expected positive

correlations between Individual Factors and Positive Workplace Factors (r = .424, p

< .001), Negative Spillover and Burnout (r = .816, p <.001), Negative Spillover and

Mental Illness (r = .688, p <.001) and between Burnout and Mental Illness (r = .631,

p < .001). The correlations also indicate that negative correlations between the latent

factors, Individual Factors and Negative Spillover (r = -.490, p < .001), Burnout (r =

-.617, p < .001) and Mental Illness (r = -.712, p < .001) and Positive Workplace

Factors and Negative Spillover (r = -.603, p < .001), Burnout (r = -.916, p < .001)

and Mental Illness (r = -.369, p < .001). These relationships were supplemented by

the direct relationships between the observed variables as shown by the correlations

between the measurement errors. Of note, negative work-to-family spillover was

positively correlated to exhaustion (r = .468, p < .001) and to stress (r = .302, p

<.001), as were anxiety and stress (r = .348, p < .001). Interestingly, exhaustion was

positively correlated with skill discretion (r = .234, p < .001) and professional

efficacy was positively correlated with depression (r = .197, p = .007). The

remaining correlations were positive, which were both intuitive (exhaustion and

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stress, r = .145, p = .017) and counterintuitive (dispositional optimism and stress, r =

.113, p = .031; coping self-efficacy and cynicism, r = .150, p = .026; and professional

efficacy and exhaustion, r = .100, p = .022).

The squared multiple correlations give the reliability of the indicator

variables for the latent factors. All indicator variables were within acceptable ranges

for the squared multiple correlations (> .30, Holmes-Smith et al., 2006), with the

lowest being professional efficacy (.340) to the highest, depression (.810). The full

list is given in the Appendix in Tables I.3 and I.4.

3.5.4 Factor score weights for the Mental Distress model

The composite variables to be used in the longitudinal model were derived

from the factor score weights generated from the CFA for Mental Distress and are

shown in Table 3.3. The weighted balance of the contribution of each observed

variable to the new, composite variables can be seen, with greater factor score

weights indicating that the indicator variable would contribute more to the composite

variable than lesser factor score weights. The two highest raw contributors to each

composite variable are shown in bold in Table 3.3. For the Negative Spillover (NSP),

Burnout and Mental Illness (MI) composite variables, the two highest contributors

were indicator variables of the corresponding latent variables in the CFA. For

example negative work-to-family spillover (.190) and negative family-to-work

spillover (.212) contributing to Negative Spillover. However, for Individual Factors

(IF) and Positive Workplace Factors (PWF), the contributors were more varied.

Coping self- efficacy had less weight for Individual Factors after dispositional

optimism, a lack of cynicism and less depression. For Positive Workplace Factors,

contributions from a lack of cynicism and less exhaustion were followed by the

contribution from affective commitment.

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Table 3.3

Factor Score weights for composite variables for the Mental Distress model


Composite variable

Observed variable IFmia PWFmi

a NSPmi

a Burnout

a MIllness

a

Dispositional optimism .245 -.010 .012 -.020 -.080

CSE .048 .003 .000 -.010 -.012

Skill discretion .003 .127 -.007 -.110 .018

Autonomy -.008 .122 -.009 -.095 .027

Affective commitment -.007 .132 -.010 -.104 .028

Neg work-family spillover .072 .077 .190 .004 -.009

Neg family-work spillover .021 -.021 .212 .129 .105

Exhaustion -.034 -.155 .000 .163 .003

Cynicism -.091 -.187 .104 .267 .066

Professional efficacy .039 .097 -.061 -.148 -.141

Depression -.081 .019 .074 .066 .533

Anxiety .000 .003 .021 .012 .072

Stress -.051 .017 -.015 -.007 .155 Note. IFmi, PWFmi, NSPmi, Burnout and MIllness are the Individual Factors, Positive Workplace

Factors, Negative Spillover, Burnout and Mental Illness, respectively, for the Mental Distress

longitudinal model

Note. Two highest factor score weights for each composite variable are shown in bold

3.5.5 Confirmatory factor analysis for the Well-Being-Mental Health model

The third CFA combined the positive and negative outcomes of Overall Well-

Being and Mental Illness Fit and had good fit indices for the model, X2/df = 1.592,

CFI = .988, RMSEA = .036 (90% CI .000 - .052). The CFA is shown in Appendix I,

Figure I.3. The standardized regression weights (β) and squared multiple correlations

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for the indicator variables were well above acceptable levels (Holmes-Smith et al.,

2006) and are shown in the Appendix I (Tables I.5 and I.6). With the exception of

affective commitment (β = .477, p < .001), all indicators had β ≥ .625 (for negative

work-to- family spillover), with the highest for depression (β = .889, p < .001). As

such, the squared multiple correlations, as measures of the reliabilities of the

indicator variables, were also above acceptable levels (> .30). Although the squared

multiple correlation for affective commitment was below this cut-off level, it had

been retained as it was a highly significant indicator variable for Positive Workplace

Factors. The correlations between the latent factors gave the expected relationships.

Individual Factors was positively correlated with Positive Workplace Factors (r =

.463, p < .001) and with Overall Well-Being (r = .922, p < .001), Positive Workplace

Factors with Overall Well-Being (r = .538, p < .001) and Negative Spillover and

Mental Illness (r =.737, p < .001). Individual Factors were negatively correlated with

Negative Spillover (r = -.506, p < .001) and Mental Illness (r = -.694, p < .001),

Negative Spillover with Positive Workplace Factors (r = -.463) and Overall Well-

Being (r = -.529, p < .001), and Mental Illness with Positive Workplace Factors (r = -

.306, p < .001) and Overall. Well-Being (r = -.602, p < .001).

In addition, there are the correlations between the measurement errors that

indicate the relationships over and above those evident through the latent factors.

Like the Well-Being model, but unlike the Mental Distress model, there were few of

these relationships which would indicate that in the Well-Being –Mental Health

model, the latent factors capture the majority of the underlying relationships. In the

Well-Being - Mental Health model, there were again positive correlations between

stress and anxiety (r = .328, p < .001) and between stress and negative work-to-

family spillover (r =.357, p < .001), similar to the Mental Distress model. The link

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between dispositional optimism and stress (r = .119, p = .015) also appeared again in

the present model. There was a positive correlation between skill discretion and

negative work-to family spillover (r = .244, p < .001) and stress (r = .190, p < .001).

3.5.6 Factor score weights for the Well-Being – Mental Health model

From the CFA, the main relationships through the latent factors can be seen,

as well as the addition linkages between the indicator variables. The two highest

factor score weights for each new, observed variable are highlighted in bold in Table

3.4. For Positive Workplace Factors (PWF), Negative Spillover (NSP), Overall Well-

Being (OWB) and Mental Illness (MI), the two highest contributors for each

composite variable were from observed variables for the corresponding latent

variable in the CFA, for example, skill discretion and job autonomy for the Positive

Workplace Factors composite variable. For the Individual Factors (IF) composite

variable, the contributions were more varied. The highest contributors were

dispositional optimism and psychological well-being with less weight given again to

coping self-efficacy. This was a similar to the Well-Being and the Mental Distress

models, which may indicate that whether person views themselves optimistically or

competently is linked to their level of well-being or mental illness.

3.5.7 Confirmatory factor analysis for the Work Engagement model, based on the

scales of burnout and work engagement

Following the success of the early SEMs and the CFAs for the Well-Being,

Mental Distress, and Well-Being-Mental Health models, it was expected that a CFA

with Burnout and Work Engagement as the outcomes would have similar acceptable

fit as the other models with minimal additional pathways. However, to achieve a

reasonable fit of the CFA (i.e. X2/df <3 and RMSEA ≤ .08), based on the five latent

factors of Individual Factors, Positive Workplace Factors, Negative Spillover,

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Table 3.4

Factor Score weights for composite variables for the Well-Being – Mental Health

model


Composite variable

Observed variable IFwbmha PWFwbmh

a NSPwbmh

a OWBwbmh

a MIwbmh

a

Dispositional optimism .148 .013 .006 .200 -.081

Coping self-efficacy .031 .003 .001 .043 -.011

Skill discretion .015 .225 -.053 .071 -.034

Autonomy .016 .323 -.028 .087 .023

Affective Commitment .006 .118 -.010 .032 .008

Neg WF spillover .031 -.107 .215 -.043 .032

Neg FW spillover .009 -.038 .209 -.045 .146

Psychological well-being .110 .037 -.014 .401 -.020

Life satisfaction .106 .035 -.013 .385 -.019

Depression -.063 .020 .095 -.042 .519

Anxiety -.003 .008 .028 .000 .083

Stress -.039 -.007 -.007 -.031 .156 Note.

a IFwbmh, PWFwbmh, NSPwbmh, OWBwbmh, MIwbmh are the Individual Factors, Positive

Workplace Factors, Negative Spillover, Overall Well-Being and Mental Illness, respectively, for the

Well-Being-Mental Health longitudinal model

Note. Two highest factor score weights for each composite variable are shown in bold.

Burnout and Work Engagement, it was necessary to add correlations between nearly

all of the errors of the observed variables (shown in Appendix I, Figure I.4). This

result indicated that the latent factors, as drawn, could not provide a satisfactory

explanation of the data and that the CFA represented a serious misspecification of the

data. As Individual Factors, Positive Workplace Factors and Negative Spillover were

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not problematic in the previous CFAs, it was necessary to begin with a CFA that

examined the relationships between Burnout and Work Engagement, without the

other variables, to clarify the underlying relationships. There has been discussion

among researchers as to whether Burnout and Work Engagement are best represented

as one factor, with burnout as the loss of work engagement (for example, Maslach,

1993; Pines, 1993) or as two factors, with Burnout and Work Engagement as related

but distinct factors, (for example, Schaufeli et al., 2002; Schaufeli et al., 2008). Both

scenarios were considered in turn, first the one-factor, then second, the two factor

CFA to understand the constructs.

3.5.8 CFA for Burnout and Engagement alone

3.5.8.1 One-factor CFA. First, Burnout and Work Engagement were

considered as a single latent factor, to be called „Work Engagement‟. This

represented burnout and work engagement as a continuum of motivational-affective

responses to the workplace and is shown in Appendix I, Figure I.5. Modest fit was

achieved for this CFA with all six components (exhaustion, cynicism and

professional efficacy from Burnout and work vigour, work dedication and work

absorption from Work Engagement), X2/df = 3.430, CFI = .990, RMSEA = .073,

(90% CI = .037-.112). However, examination of the model found that the squared

multiple correlation for exhaustion was only .13, indicating that the Work

Engagement latent variable accounted for only 13% of the variance of exhaustion.

Such a low figure was much less that the level (.30) of an acceptable indicator

variable (Holmes-Smith et al., 2006), and would suggest that exhaustion was a poor

representative of Work Engagement, when seen as the combination of the scales of

both Burnout and Work Engagement.

Therefore, a single-factor CFA for the latent variable Work Engagement,

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with the five indicator variables (without exhaustion) was conducted and is shown in

Appendix I, Figure I.6. This was a well-fitting model, X2/df = .933, CFI = 1.000,

RMSEA = .000 (90% CI = .000- .077). In addition to the relationships through the

latent factors, there were the correlations between the measurement errors for the

indicator variables. There was a positive correlation between work absorption and

cynicism (r = .52, p < .001) and a negative correlation between work absorption and

professional efficacy (r = -.19, p < .001).

3.5.8.2 Two factor CFA. When the CFA for the two factors, Burnout and

Work Engagement was considered, the fit was unacceptable, X2/df = 17.656, CFI =

.933, RMSEA = .190, 90% CI = .157 - .226. This CFA is shown in Appendix I,

Figure I.7. Examination of the model however showed that fit could not be improved

by using the Modification Indices and the statistical significance of paths. Any

attempt to respecify the model to improve fit lead to an inadmissible result, as the

correlation between burnout and work engagement was greater than 1 (r = -1.003).

Further, the covariance matrix was non-positive definite, i.e. that some of the

covariances in the matrix were negative, rather than all covariances being positive.

Non-positive definite covariance matrices can be the result of linear dependence

between variables, such that two variables are perfectly correlated. A solution would

be to remove one of the variables, as the inadmissible result implies that there is

redundant information in the calculations of the CFA (Holmes-Smith et al., 2006;

Schmacker & Lomax, 2004).

To further try to understand the factorial structure of burnout and work

engagement, a second, two factor CFA was then conducted. Following the

suggestions of Schaufeli et al. (2002), the factors were rearranged. The first factor

had the „core‟ features of burnout, exhaustion and cynicism, and the second factor

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had the other four components of work vigour, work dedication, work vigour and

professional efficacy, as shown in Appendix I, Figure I.8. However, in this form, the

result was an inadmissible solution, as the covariance matrix was again non-positive

definite. These results indicated that in data for the current thesis, burnout and work

engagement can only be represented as one factor, rather than as two separate and

related factors. This single factor was called Work Engagement and was used in the

following analyses of the larger Work Engagement model.

3.5.9 Confirmatory Factor Analysis for the Work Engagement model

The CFA of the larger Work Engagement model with the five latent

variables, Individual Factors, Positive Workplace Factors, Negative Spillover and

Work Engagement, was again conducted. However, the process of fitting the model

to the data found that early solutions had inadequate fit (X2/df = 3.733, CFI = .948,

RMSEA = .077 (90% CI = .064 – .090)). Using the Modification Indices, the fit was

considerably improved by removing one of the proposed components of Work

Engagement, work vigour. It is likely that this could indicate that an individual‟s

energetic attitudes toward work, captured by work vigour, are explained elsewhere in

the CFA. The fit indices of the CFA for the Work Engagement model were

acceptable, X2/df = 2.608, CFI = .971, RMSEA = .059 (90% CI = .044 - .075), with

the CFA shown in Appendix I, Figure I.9.

The indicator variables load satisfactorily on each of the latent variables, as

shown in the Appendix (Table I.7), with the least being negative work-to-family

spillover (β = .534, p < .001) and the highest loadings for work dedication (β = .889,

p < .001), cynicism (β = -.788) and coping self-efficacy (β = .752, p <.001). The

squared multiple correlations show that the indicator variables are acceptable

representatives for the latent variables, although a significant pathway, negative

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work-to-family spillover was slightly below the acceptable cut-off (squared multiple

correlation = .285). The full list of beta weights and correlations were given in the

Appendix I, Tables I.7 and I.8. The latent factors were correlated with each other in

the expected directions. Individual Factors was positively correlated to Positive

Workplace Factors (r = .458, p < .001) and Work Engagement (r = .480, p < .001),

Positive Workplace Factors to Work Engagement (r = .912, p < .001) and Negative

Spillover was negatively correlated to Individual Factors (r = -.443, p < .001),

Positive Workplace Factors (r = -.400) and Work Engagement (r = -.421, p < .001).

In addition to the relationships through the latent factors, additional paths

were present in the CFA. Skill discretion and work dedication were positively

correlated (r = .519, p < .001), as are negative work-to-family spillover and cynicism

(r = .327, p < .001). Affective commitment and cynicism were negatively correlated

(r = -.242, p <.001), work absorption is positively correlated with cynicism (r = .378,

p < .001) and with negative work-to- family spillover (r = .257, p < .001). Skill

discretion is also positively correlated to negative work-to-family spillover (r = .150,

p = .001).

3.5.10 Factor score weights for the Work Engagement model

The factor score weights are shown in Table 3.5 and illustrate the balance of

the relationships explored in the Work Engagement CFA, with the two highest factor

score weights highlighted in bold in Table 3.5. For Negative Spillover (NSP) and

Work Engagement (WE), the two highest contributors for each composite variable

are from the indicator variables for the corresponding latent variable, for example,

for Negative Spillover, negative work-family spillover and negative family- work

spillover and for Work Engagement, work dedication and cynicism. For Individual

Factors (IF) and Positive Workplace Factors (PWF), the contributions were more

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Table 3.5

Factor Score weights for composite variables for the Work Engagement model


Composite variable

Observed variable IFwaa PWFwa

a NSPwa

a WEwa

a

Dispositional optimism .279 .019 -.022 .021

Coping self-efficacy .053 .003 -.004 .004

Skill discretion .006 .126 -.021 -.038

Autonomy .015 .165 -.007 .096

Affective Commitment .006 .083 -.010 .014

Neg Work-Family spillover -.034 -.023 .162 .022

Neg Family-Work spillover -.072 -.025 .316 -.047

Work dedication .043 .151 -.016 .419

Work absorption .030 .132 -.039 .207

Professional efficacy .015 .081 -.010 .122

Cynicism -.023 -.140 -.020 -.266

Note. a IFwa, PWFwa, NSPwa, WEwa are the composite variables, Individual Factors, Positive

Workplace Factors, Negative Spillover and work engagement, respectively used in the longitudinal

models.

Note. The highest factor score weights for each composite variable are shown in bold.

varied. For Individual Factors, the highest raw contribution came from dispositional

optimism, negative family-work spillover and coping self-efficacy. For Positive

Workplace Factors, the highest contributors were job autonomy and work dedication,

followed by work absorption and skill discretion.

3.5.11 Confirmatory factor analysis of the Integrated model

With the success of the CFAs for Well-Being, Mental Distress, Well-Being-

Mental Health and Work Engagement, the last step was to combine all the outcomes

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into a complete, Integrated model. It is unusual to test a large number of positive and

negative outcomes together and this process provided a unique opportunity to

understand how the balance of positive and negative were experienced by the

individual. It was important to further explore whether there were redundancies or

overlaps among the indicator variables for the latent factors. As seen when Burnout

and Work Engagement, two measures can be used in separate situations but when

combined, there was considerable convergence toward an underlying construct.

Taking a broad approach to well-being and mental health outcomes brought a more

holistic understanding of working adults.

In the same manner as the previous CFAs, the CFA was drawn as six latent

factors: Individual Factors (as dispositional optimism and coping self-efficacy),

Positive Workplace Factors (as skill discretion, job autonomy, and affective

commitment), Negative Spillover (as negative work-to-family spillover and negative

family-to-work spillover), Overall Well-Being (as life satisfaction and psychological

well-being), Mental Illness (as depression, anxiety, and stress) and Work

Engagement (as work dedication, work absorption, professional efficacy, and

cynicism). Unfortunately, the first CFA with all the listed variables was not an

admissible solution. The covariance matrix was non-positive definite (i.e. there were

negative covariances in the matrix) which indicates that there are linear dependencies

among some or more of the variables (Holmes-Smith et al., 2006) and the CFA as

drawn was not usable.

It was necessary to rethink how the Integrated CFA would proceed. The

process of understanding Work Engagement, as outlined in the previous section, had

also given non-positive definite covariance matrices. Therefore, changes to Work

Engagement were chosen as the first and most likely solution to the problem. The

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effect of removing the indicators of Work Engagement (work dedication, work

absorption, professional efficacy, and cynicism) were considered one at a time and it

was found that removing cynicism from the Work Engagement latent factor allowed

a solution to be calculated successfully. However, taking cynicism out of the

Integrated CFA provided another dilemma. If cynicism, which was a satisfactory

inclusion of the Work Engagement CFA was now redundant in the Integrated CFA,

should work vigour and exhaustion, which were excluded from the Work

Engagement CFA be reconsidered for this new Integrated CFA? As the purpose of

this Integrated model was to explore and understand how the many and diverse

constructs come together in previously untested combinations, it was decided to

revisit how work vigour and exhaustion related to the other variables.

First, work vigour was reconsidered. Work vigour could not be successfully

added to the Integrated CFA as part of the Work Engagement factor as the solution

was inadmissible (as a non-positive definite covariance matrix). As was the case in

the Work Engagement CFA, it is likely that the individual‟s vigour for their work is

captured elsewhere in the model. The hierarchical multiple regression for work

vigour, as shown in Chapter 2 found that all of the indicators of Individual Factors

and Positive Workplace Factors were significant predictors of work vigour, which

could explain how vigour was already being measured within the CFA. Second,

exhaustion was reconsidered. Emotional exhaustion proved to be an interesting

addition to the Integrated CFA. From the Modification Indices and standardized

regression weights, rather than loading onto the Work Engagement latent factor as

expected (and as found in the previous CFAs), in this Integrated CFA, exhaustion

loaded strongly on the Negative Spillover factor (β = .834, p < .001). This result

followed from the hierarchical multiple regressions in Chapter 2, where negative

306

work-to-family spillover was the strongest predictor of emotional exhaustion and

could indicate that emotional exhaustion is tied to a wider set of problems across the

work and family domains.

The final CFA for the Integrated model is shown in Appendix I, Figure I.11

and had the following fit indices, X2/df = 2.784, CFI = .954, RMSEA = .062 (90% CI

= .053 - .072). Even with the large number of latent and observed variables involved,

the final model was not overly complicated by correlations between the indicator

variables, which would show that the latent variables are reasonably explaining the

relationships between the variables.

In accordance with the previous models, the relationships between the latent

variables in the Integrated CFA were similar in their strength and direction.

Individual Factors were positively correlated with Positive Workplace Factors (r =

.425, p <.001), Overall Well-Being (r = .922, p < .001) and Work Engagement (r =

.429, p < .001). Positive Workplace Factors were positively related to Overall Well-

Being (r = .495, p < .001) and Work Engagement (r = .938, p < .001) and Overall

Well-Being and Work Engagement were positively correlated (r = .421, p < .001).

Negative Spillover was positively correlated with Mental Illness (r = .663, p < .001)

and negatively correlated with Individual Factors (r = -.471, p < .001), Positive

Workplace Factors (r = -.300, p < .001), Overall Well-Being (r = -. 506, p < .001)

and Work Engagement (r = -.366, p < .001). Lastly, Mental Illness was negatively

correlated with Individual Factors (r = -.685, p < .001), Positive Workplace Factors

(r = -.262, p < .001), Overall Well-Being (r = -.608, p < .001) and Work Engagement

(r = -.307, p <.001). The standardized regressions weights and the squared multiple

correlations and the correlations between the indicator variables are shown in the

Appendix I, Tables I.9 and I.10.

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The indicator variables loaded satisfactorily on the latent variables, with the

highest being work dedication (β=.982, p < .001), depression (β = 880, p < .001), and

psychological well-being (β = .858, p < .001). Whilst some of indicator variables had

beta weights below the cut-off for acceptable indicators (β < .55, Holmes-Smith et

al., 2006), the paths were significant and were retained. These indicator variables

were job autonomy (β = .513 p < .001), negative family-to-work spillover (β = .525 p

< .001), and professional efficacy (β = .532, p < .001). Adding to the relationships

between the latent variables were the direct correlations between the indicator

variables, which were the relationships in excess those given by the latent factors. As

in the Work Engagement and the Well Being-Mental Health CFAs, stress and

anxiety were positively correlated (r = .325, p < .001) as were work absorption and

work-to-family spillover (r = .154, p = .002). Affective commitment and exhaustion

were negative correlated (r = -.321, p < .001). The negative work-to-family spillover

and stress were positively correlated (r = .338, p < .001), as previously found in the

Mental Distress and Well-Being-Mental Health models. Job autonomy and

professional efficacy were positive correlated (r = .193, p < .001), as were

professional efficacy and psychological well-being (r = .227, p < .001).

In summary, all of the CFAs showed that individuals with more positive

views of the future and greater personal effectiveness were likely to feel they had

greater control and creativity in their work, felt more attached to their work, had

fewer problems that spread across their lives, felt greater satisfaction and purpose in

their lives, with fewer mental health concerns, and felt zest, focus and competence

about their work, which was in line with the findings of the multiple regressions

outlined in Chapter 2. The additional pathways added greater understanding of the

nuances of the individual‟s experiences.

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3.5.12 Factor score weights for the Integrated model

The factor score weights for the Integrated model are shown in Table 3.6 and

indicate the balance of the relationships found in the CFA and the weighted

contribution of each indicator variable to the composite variables to be used in the

longitudinal model. There were many similarities to the previous CFAs, which was

to be expected as the Integrated CFA was the combination of those CFAs. From the

CFA, the main relationships through the latent factors can be seen, as well as the

addition linkages between the indicator variables. The two highest factor score

weights were highlighted in bold in Table 3.6. For Negative Spillover (NSP), Overall

Well-Being (OWB) and Mental Illness (MI), the two highest contributors for each

composite variable were from observed variables for the corresponding latent

variable in the CFA. For example, negative work-to-family spillover and exhaustion

for Negative Spillover and psychological well-being and life satisfaction for Overall

Well-Being.

For the Individual Factors (IF), Positive Workplace Factors (PWF) and Work

Engagement (WE) composite variables, the contributions were more varied. For

Individual Factors, the highest raw contributors were dispositional optimism,

psychological well-being and life satisfaction, with lesser weighting from coping

self-efficacy. As in the Work Engagement model, both Positive Workplace Factors

and Work Well-Being had the highest raw contributors of work dedication and skill

discretion, although at different levels as shown in Table 3.6. Work Engagement was

again overwhelmingly composed of the indicator variable, work dedication rather

than skill discretion. For Positive Workplace Factors, the contributions were from

work dedication and skill discretion, although this was not as disparate. As in the

previous models, keeping the composite variables separate allowed the conditions of

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a positive workplace and work well-being to be studied separately to understand the

processes involved.

Table 3.6

Factor score weights for the composite variables for the Integrated model


Composite variable

Observed variable IFcma

PWFcma NSPcm

a OWBcm

a MIcm

a WEcm

a

Dispositional optimism .146 -.015 .003 .196 -.055 .006

Coping self-efficacy .031 -.003 .001 .042 -.012 .001

Skill discretion -.019 .166 .005 .104 .016 .052

Job autonomy -.002 .076 .002 .075 .006 .017

Affective commitment -.005 .059 .054 .022 .032 .016

Negative WF spillover .027 .001 .239 -.051 -.026 -.018

Negative FW spillover .003 .004 .097 -.025 .050 -.003

Exhaustion .004 .036 .237 -.045 .124 .000

Psychological well-being .116 .046 -.013 .405 -.026 -.004

Life satisfaction .108 .043 -.012 .372 -.024 .001

Depression -.062 .013 .050 -.049 .488 -.003

Anxiety -.010 .002 .022 -.012 .086 -.001

Stress -.025 .004 -.021 -.008 .166 .002

Work dedication .078 .533 -.041 .023 -.032 .875

Work absorption .001 .022 -.026 .006 .001 .038

Professional efficacy -.040 -.012 .003 -.167 .007 .033 Note.

a IFcm, PWFcm, NSPcm, OWBcm, MIcm, and WEcm are the composite variables, Individual

Factors, Positive Workplace Factors, Negative Spillover, Overall Well-Being, Mental Illness and

Work Engagement, respectively, used in the longitudinal Integrated model

Note. Two highest factor score weights for each composite variable are shown in bold.

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3.6 Comparing the longitudinal models

3.6.1 Competing sets of longitudinal models

The final part of the analyses was the longitudinal modelling, which will

examine the relationships between the variables in several ways: first, by testing the

strength of the synchronous correlations, as the relationships between variables at

each measurement time; second, as the auto-lagged relationships within each variable

across time; and third, as the cross-lagged relationships between variables across

time. The effect of concurrent functioning was captured by the synchronous

correlations and was seen in the previous section on the CFAs for each model,

representing the individual‟s here-and-now. The long term stability and persistence

of each part of the model was seen in the auto-lagged paths, which showed how

previous levels of each variable influence the current level of the same variables. As

such, this stability can show if, for example, well-being in the near and/or distant past

can influence current levels of well-being. Model A gave the fit of these first two

pathways. The last component to be tested was the influence of the cross-lagged

paths. These were the relationships between the variables over time, for example,

indicating the influence of well-being at one time on the level of spillover at the next

time. Models B and C tested the changes to model fit due to the addition of these

paths, whilst Model D combined all the hypothesized relationships.

Briefly to recap on how the relationships are drawn in the models, as was

shown in Figure 3.3 earlier in the chapter and shown at the start of Appendix K.

Correlations between the measurement errors at each time period (synchronous

correlations), single headed (i.e. causal) arrows between the same variable from

Time 1 to Time 2, Time 2 to Time 3, and Time 1 to Time 3 (auto-lagged paths) and

single headed (i.e. causal) arrows between variables from Time 1 to Time 2 and

311

Time 2 to Time 3 (cross-lagged paths). The cross-lagged paths in the Causality

models were from the „predictors‟, Individual Factors, Positive Workplace Factors

and Negative Spillover to the „outcomes‟, Overall Well-Being, Work Well-Being,

Mental Illness, Burnout, and Work engagement, as appropriate for the particular

model being considered. In the Reverse Causality models, the cross-lagged paths

were from the „outcomes‟, Overall Well-Being, Work Well-Being, Mental Illness,

Burnout and Work Engagement, as appropriate, to the „predictors‟, Individual

Factors, Positive Workplace Factors and Negative Spillover.

The initial longitudinal modelling for the Well-Being model considered only

the Time 1 to Time 2 and Time 2 to Time 3 auto-lagged relationships (for example,

IFwb1 to IFwb2 and IFwb2 to IFwb3). The fit indices for all the competing models

(i.e. Models A to D) for the Well-Being model were not acceptable and none of the

models could be supported, as shown in Table 3.7 in the „Without‟ column. The

inclusion of the Time 1 to Time 3 auto-lagged relationships improved the fit of all of

the models (models A to D) and the models now had acceptable fit, shown in the

„With‟ column of Table 3.7 Therefore, all Time 1 to Time 3 auto-lagged paths

Table 3.7

Improvement in the fit of non-nested models in the Well-Being model by including the

auto-lagged pathways from Time 1 to Time 3

Stability (A) Causality (B) Rev Causality (C) Reciprocal (D)

Model Fit Without With Without With Without With Without With

X2/df 4.754 2.648 4.199 1.265 4.390 1.457 4.848 1.202

RMSEA .142 .091 .132 .037 .135 .048 .144 .032 Note. „Without‟ models do not include Time 1 to Time 3 auto-lagged paths; „With‟ models include

Time 1 to Time 3 auto-lagged paths.

312

were included in all subsequent models. However, when the cross-lagged paths were

added, only the Time 1 to Time 2 and Time 2 to Time 3 paths proved to be important

to the fit of the models. Across all the competing models (Models A to D), the Time

1 to Time 3 paths were non-significant and reduced the fit of the models. Therefore

the Time 1 to Time 3 cross-lagged paths were not included in any of the subsequent

models.

After the set of models (Models A to D) were compared, the next step was to

consider whether any of the auto-lagged or cross-lagged paths were trivial and did

not contribute to the fit of the models (designated as Model E). Rather than a

speculative exploration of the data, the rationale for the model trimming followed

from process of fitting the early SEMs. For example, Negative Spillover was not

included in the final model of positive outcomes in the early SEM, nor did Negative

Spillover have an influence on Overall Well-Being in the model with both positive

and negative outcomes of the Time 1 SEM. The central question for model trimming

is whether paths were redundant (i.e. not influential) and should not be included in

the longitudinal models. The decision was based on the standardized regression

weights (beta, β), the non-significance of the paths and improvements to the AIC, as

the AIC measures the best fit in a model using the least number of parameters. The

standardized regression weights can be taken as the effect size for a path, with β in

the range of .50 - .80 indicating a strong effect, whilst β < .20 indicating weak effect

sizes (Holmes-Smith et al., 2006). For model trimming in the following models, the

criteria for removing paths were set at β ≤ .10 where the paths were also highly non-

significant (p > .20). Consideration of fit and parsimony to ascertain if the deletion

has lowered values of the AIC was also important to the final inclusion or deletion of

a pathway. The combination of minimal variance and non-significance of a path is

313

necessary to prevent to removal of paths with true non-zero causal effects that were

non-significant in this model, but may be significant in other, similar samples (Kline,

2006). Therefore, Type II errors can be avoided, as can overfitting of the model to

this particular data set, which would avoid a Type I error. Replication of the models

in other samples should be undertaken to confirm the results found in the current

thesis.

The results for the longitudinal models are reported in two steps. First, the fit

indices and measures of parsimony are given for each of the outcomes models, in the

following order: Well-Being, Mental Distress, Well-Being-Mental Health, Work

Engagement, and the Integrated model, showing the best fitting of the set of models

(A to E) for each of these longitudinal models (Tables 3.8 to 3.12). The betas weights

of the best fitting models is shown in Table 3.13. Rather than place all of the results

here in the chapter, only the important results will be shown in the chapter with the

supplementary results given in Appendix J. Tables J.1 to J. 5 list the means, standard

deviations and ranges, the correlations between the composite variables for each

longitudinal models are shown in Tables J.6 to J.10. Table J.11 shows the X2 and df

for each competing set of models (A to E) for each of the longitudinal models.

Figures J.1 to J.5 graphically show the sets of models (A to E) that were tested for

each longitudinal model. Tables J.12 to J.16 show the synchronous correlations of

the best fitting longitudinal models and Tables J.17 to J.21 give the beta weights of

the paths in the best fitting longitudinal models.

3.6.2 The longitudinal Well-Being Model

The competing set of models that have been compared for the Well-Being

model are shown in Appendix J, Figure J.1, with the best fitting model shown here in

Figure 3.4. The results of the competing set of models in the Well-Being model are

314

shown in Table 3.8, with the Chi-Squared statistics (and significance levels) and

degrees of freedom for the models shown in Appendix J, Table J.11. It should be

noted that the Comparative Fit Index (CFI) is satisfactory (CFI > .95) for each model

considered within the Well-Being model and did not provide a distinct difference

between models. There were differences between the Normed Chi-Squared, RMSEA

and AIC which were used to determine the best fitting model. The least well fitted

model was the Stability model (X2/df = 2.648), with the Reverse Causality improving

the fit (X2/df = 1.457). The Causality and Reciprocal models were about equal,

balancing the lower AIC of the Causality (AIC = 135.43) with the better Normed

Chi-squared (X2/df = 1.202), whilst RMSEA was equivalent for both models. For

Model E, all the non-significant pathways in the Reciprocal model were considered

for trimming, using the previously noted criteria (minimal β, p > .20). Interestingly,

removing all non-significant pathways decreased the fit of the model dramatically

(Normed Chi-Squared rose to 4.164, indicating very poor fit). This change indicated

that there were true non-zero causal pathways in this model that, whilst not

significant in this particular sample, should be included to avoid Type II errors

(Kline, 2006).After removing the trivial pathways, Model E, the Trimmed Reciprocal

was the best fitting model with the lowest Normed Chi-Squared and RMSEA and

with an AIC lower than the Causality model and therefore represents an

improvement on both the Causality and the Reciprocal models. The least value of the

Expected Cross-Validation Index (ECVI) (0.66 for E), in comparison to the other

models (A to D) further indicated that the Trimmed Reciprocal model, E was also the

most likely of the set of models to be validated in another sample of similar

participants. Figure 3.4 shows the Trimmed Reciprocal model. The correlations, the

auto-lagged and cross-lagged paths (shown in Appendix J, Tables J.12 and J.17) are

315

IFwb2 PWFwb2 WWBwb2 OWBwb2


e71

e8

e9 e10 e11 e12

1 1 1 1

e131

e141


e151

e161

e171

e18

1

1

Table 3.8

Results of longitudinal model testing for Well-Being Model

Model X2/df CFI RMSEA (90%CI) AIC ECVI (90%CI)

A Stability 2.648 .990 .091 (.069-.114) 179.34 0.91 (0.78-1.08)

B Causality 1.265 .999 .037 (.000-.070) 135.43 0.69 (0.65-0.79)

C Reverse Causality 1.457 .998 .048 (.000-.078) 140.79 0.72 (0.65-0.82)

D Reciprocal 1.202 .999 .032 (.000-.072) 140.03 0.71 (0.69-0.80)

E Trimmed 1.127 .999 .025 (.000-.062) 130.68 0.66 (0.65-0.76) Note. X

2/df – good fit in the range 1 < X

2/df < 2, close to 1 indicates good fit, <1 overfit; CFI

(Comparative Fit Index), good fit >.950; RMSEA (Root Mean Square Error of Approximation), good

fit if RMSEA < .050; 90%CI for RMSEA, range with lower end including .000 (indicates exact fit)

and upper range of < .08; AIC (Akaike‟s Information Criteria), lowest AIC is most parsimonious, AIC

of Saturated model = 156.00; ECVI (Expected Cross-Validation Index), lowest is most likely to be

replicated, ECVI of Saturated model = 0.79.

Figure 3.4 The best fitting model for the Well-Being model, E the Trimmed

Reciprocal Note. IF: Individual Factors; PWF Positive Workplace Factors; WWB: Work well-being, OWB:

Overall well-being; „wb‟ indicates composite variables of the Well-Being model; 1, 2, 3 indicate

Times 1, 2 and 3 respectively

316

discussed the following section with the standardized regression weights and

significance of the paths at the end of the fit of the longitudinal models.

3.6.3 The longitudinal Mental Distress model

The competing set of models that are compared in the Mental Distress model

are shown in the Appendix (Figure J.2), with the best fitting model shown in Figure

3.5 in this chapter. The results of the comparisons are shown in Table 3.9, with the

Chi-squared statistic (and significance levels) and the degrees of freedom for the

models in Appendix J, Table J.11. It should be noted that the Comparative Fit Index

(CFI) is satisfactory (CFI > .95) for each model considered within the Mental

Distress model and does not provide a distinct difference between models. There are

differences between the Normed Chi-Squared, RMSEA and AIC which will be used

to determine the best fitting model. As with the Well-Being model, the Stability

model is the least well fitting of the set (X2/df = 1.615), although the Stability model

would have acceptable fit if it were to be considered by itself. Whilst the Reverse

Causality model is an improvement on the Stability model (X2/df = 1.548), the AIC

is greater than the Stability model, indicating that the Stability model is more

parsimonious than the Reverse Causality. The Causality and Reciprocal models are

again ranked similarly, to balance the Causality model being more parsimonious

(AIC = 204.91) whilst the Reciprocal model has a slightly better Normed Chi-

squared (X2/df = 1.205) and RMSEA (RMSEA = .032, 90%CI = .000-.062). For

model E, the non-significant paths of the Reciprocal model that were considered for

trimming and using the same criteria (minimal β, p>.20) as the Well-Being model.

However, changes to the fit indices indicated that two paths (Mental Illness Time 2

MI2 Positive Workplace Factors Time 3, PWF3; and Mental Illness Time 2

MI2to Negative Spillover Time 3, NSP3) should be reinstated, despite small beta

317

IFmi1 PWFmi1 NegSp1 burnout1 MIllness1



e11

e21

e31

e41

e51

e61

e71

e81

e91

e101

e11 e12 e13 e14 e15

11 1 1 1

Table 3.9

Results of longitudinal model testing for Mental Distress Model


A. Stability 1.615 .995 .055 (.034-.075) 216.91 1.24 (1.10-1.42)

B. Causality 1.263 .998 .036 (.000-.061) 204.61 1.21 (1.02-1.26)

C. Reverse Causality 1.548 .996 .052 (.027-.074) 218.32 1.71 (1.06-1.32)

D. Reciprocal 1.205 .999 .032 (.000-.062) 211.37 1.07 (1.04-1.18)

E. Trimmed 1.079 1.000 .020 (.000-.053) 199.46 1.01 (1.00-1.12) Note. X


2/df > 1, close to 1 indicates good fit, <1 overfit; CFI

(comparative Fit Index), good fit >.950; RMSEA (Root Mean Square Error of Approximation), good





Figure 3.5. The best fitting of Mental Distress model, E, the Trimmed Reciprocal Note. IF: Individual Factors; PWF: Positive Workplace Factors; NegSp: Negative Spillover; MIllness:

Mental illness; 1, 2, 3: Times 1, 2 and 3 respectively

318

weights and are included in the final results for the model. Inclusion of the paths

guards against a Type II error of ignoring meaningful, yet non-significant (in this

sample) paths (Kline, 2006). The Trimmed Reciprocal model, E, was an

improvement on both the Causality and the Reciprocal models, as the most

parsimonious (the lowest AIC (199.46)), perfect comparative fit (CFI = 1.00), with

the lowest RMSEA (RMSEA = .020, 90%CI = .000-.053) and with the lowest values

of the Expected Cross-Validation Index (ECVI) (1.01), the Trimmed model was the

most likely to be validated in another similar sample. Figure 3.5 shows the Trimmed

Reciprocal model, E. The correlations, the auto-lagged and cross-lagged paths

(shown in Appendix J, Tables J.13 and J.18), are discussed with the standardized

regression weights and significance of the paths, at the end of the fit of the

longitudinal models.

3.6.4 The longitudinal Well-Being – Mental Health model

The competing set of models that were compared for the Well-Being –Mental

Health model are shown in Appendix J, Figure J.3, with the best fitting model shown

in Figure 3.6 in this chapter. The results of the comparisons are shown in Table 3.10

with the Chi-Squared statistics (and significance levels) and the degrees of freedom

for the models shown in Appendix J, Table J.11. It should be noted that the

Comparative Fit Index (CFI) was again satisfactory (CFI > .95) for each model

considered within the Well-Being-Mental Health model and did not provide a

distinct difference between models. There were differences between the Normed

Chi-Squared, RMSEA and AIC which were used to determine the best fitting model.

As the Well-Being and Mental Distress models, the Stability model was the least

well-fitting of the set (X2/df = 2.187) with the Reverse Causality model improving fit

(X2/df = 1.639). Whereas in the Well-Being and Mental Distress models, the

319

Causality and Reciprocal models were very similar, in the Well-Being-Mental Health

model, the Reciprocal model had the better fit, with a lower Normed Chi-Square

(X2/df = 0.969), a perfect fit given by the point estimate for the RMSEA and close fit

given by the confidence interval (RMSEA = .000, 90%CI = .000-.049), and being

more parsimonious (AIC = 202.85) than the Causality model.

For Model E, all the non-significant pathways from the Reciprocal model

were considered for trimming, using the same criteria (minimal β, p >.20) for

removal of a path. Although the Normed chi-squared is less than 1 (X2/df = .896),

overfitting was not likely as paths have been removed from the model rather than

added to improve fit. The Trimmed Reciprocal was considered the best fitting and

most parsimonious model, with the RMSEA =.000 (90%CI = .000-.038), the AIC

(184.78) having the lowest value and the ECVI (.94) indicating that the Trimmed

model was the mostly likely to be replicated in another sample of similar

participants. Whilst including true non-zero paths that may be significant will avoid

Type II errors, removing the trivial paths will guard against Type I errors, and chance

associations that should be disregarded. Figure 3.6 shows the Trimmed Reciprocal

model, E. The relative importance of the correlations, the auto-lagged and cross-

lagged paths (shown in Appendix J, Tables J.14 and J.19) are discussed in the next

section on the standardized regression weights and significance of the paths, at the

end of the comparisons of longitudinal model fit.

3.6.5 The longitudinal Work Engagement model

The competing set of models that were compared for the Work Engagement

model are shown in Appendix J, Figure J.4, with the best fitting model shown here in

Figure 3.7. The results of the comparisons are shown in Table 3.11, with the Chi-

squared statistics (and significance levels) and degrees of freedom for the models

320

IFwbmh1 PWFwbmh1 NSpwbmh1 OWBwbmh1 MIwbmh1

IFwbmh2 PWFwbmh2 NSPwbmh2 OWBwbmh2 MIwbmh2


e11

e21

e31

e41

e51

e61

e71

e81

e91

e101

e11 e12 e13 e14 e15

11111

Table 3.10

Results of longitudinal model testing for Well-Being-Mental Health models


A Stability 2.187 .988 .078 (.060-.096) 251.22 1.28 (1.13-1.46)

B Causality 1.298 .998 .039 (.000-.064) 206.32 1.05 (0.98-1.17)

C Reverse Causality 1.639 .995 .057 (.033-.079) 222.68 1.13 (1.03-1.27)

D Reciprocal 0.969 1.000 .000 (.000-.049) 202.85 1.03 (1.04-1.12)

E Trimmed 0.896 1.000 .000 (.000-.038) 184.78 0.94 (0.94-1.04) Note. X


2/df > 1, close to 1 indicates good fit, <1 possible overfit; CFI






Figure 3.6. The best fitting of the Well-Being-Mental Health model, E, the Trimmed

Reciprocal Note. IF: Individual Factors; PWF: Positive Workplace Factors; NSP: Negative Spillover; OWB:

Overall Well-being; MI: Mental illness; „wbmh‟: composite variables of Well-Being-Mental health

model; 1, 2, 3: Times 1, 2 and 3 respectively

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shown in Appendix J, Table J.11. It should be noted that the Comparative Fit Index

(CFI) is again satisfactory (CFI > .95) for each model considered within the Work

Engagement model and does not provide a distinct difference between models. There

were differences between the Normed Chi- Squared, RMSEA and AIC which were

used to determine the best fitting model. As with the Well-Being, Mental Distress

and Well-Being-Mental Health, the Stability model of the Work Engagement model

was the least well-fitting of the set of models, although the Stability model could be

considered to be well-fitting in its own right (X2/df = 1.879). The Reverse Causality

had improved fit (X2/df = 1.593) and parsimony (AIC = 143.76) over the Stability

model, with the Reciprocal improving the fit and parsimony further. The Causality

model however, was the best fitting of the set of models (X2/df = 1.221, RMSEA =

.033, 90%CI = .000-.066).

However, examination of the standardized regression weights in the Causality

model found that several paths were highly non-significant and had negligible

standardized regression weights (for example, IFwa1 WEwa2, β = -.007, p = .708;

and NSPwa1 WEwa2, β = -.013, p = .437). Therefore, the process of model

trimming was undertaken to determine if fit could be improved by removing paths

such as these. As the Reciprocal model still represents good fit of the model and in

line with the other models previously considered, Model E is based on the Reciprocal

model. All the non-significant paths were considered for trimming, using the same

criteria for removal (minimal β, p > .20) as previously. The Trimmed Reciprocal

model had improved fit (X2/df = 1.141, RMSEA = .027, 90%CI = .000-.061) over

the Causality model and also better parsimony (AIC = 128.50) and with the lowest

ECVI (0.65), having the greater likelihood that the model would be replicated in

another similar group of participants. Figure 3.7 shows the Trimmed Reciprocal

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IFwa1 PWFwa1 NSPwa1WEwa1

IFwa2 PWFwa2 NSPwa2 WEwa2


e11

e21

e31 e4

1

e51

e61

e71

e8

e9e10 e11 e12

11 1

1

1

Table 3.11

Results of longitudinal model testing for the Work Engagement model


A. Stability 1.879 .991 .067 (.042-.091) 151.64 0.77 (0.67-0.91)

B. Causality 1.221 .998 .033 (.000-.066) 132.62 0.67 (0.64-0.77)


D. Reciprocal 1.390 .997 .044 (.000-.078) 141.35 0.72 (0.67-0.82)

E. Trimmed 1.141 .999 .027 (.000-.061) 128.50 0.65 (0.63-0.75) Note. X


2/df > 1, close to 1 indicates good fit, <1 overfit; for CFI

(Comparative Fit Index), good fit >.950; for RMSEA (Root Mean Square Error of Approximation),

good fit if RMSEA < .050; 90%CI for RMSEA, range with lower end including .000 (indicates exact

fit) and upper range of < .08; AIC (Akaike‟s Information Criteria), lowest AIC is most parsimonious,

AIC of Saturated model = 156.00; ECVI (Expected Cross-Validation Index), lowest is most likely to

be replicated, ECVI of Saturated model = 0.79.

Figure 3.7. The best fitting of the Work Engagement model, the Trimmed Reciprocal Note. IF: Individual Factors; PWF: Positive Workplace Factors; NSP: Negative Spillover; WE: Work

Engagement ; „wa‟ composite variables of the Work Engagement model; 1, 2, 3: Times 1, 2, 3

respectively

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model. Again, the correlations, the auto-lagged and cross-lagged paths (shown in

Appendix J, Tables J.15 and J.20) are reported in the next section on the standardized

regression weights and significance of the paths, at the end of the discussion of

longitudinal model fit.

3.6.6 The longitudinal Integrated model

The competing set of models that were compared for the Integrated model are

shown in Appendix J, Figure J.5, with the best fitting model shown here in Figure 3.8

in this chapter. The results of the comparisons are shown in Table 3.12, with the Chi-

squared statistics (and significance levels) and the degrees of freedom for the models

shown in Appendix J, Table J.11. As with the previous models, the Comparative Fit

Index (CFI) is satisfactory (CFI > .95) for each model considered within the Well-

Being model and did not provide a distinct difference between models. There were

differences between the Normed Chi-Squared, RMSEA and AIC which are used to

determine the best fitting model. As with the Well-Being, Mental Distress, Well-

Being-Mental Health and the Work Engagement model the Stability model of the

Integrated, model was the least well-fitting of the set of models (X2/df = 2.079),

although its fit would be still acceptable if considered alone. In a similar pattern to

the previous models, the Reverse Causality model had improved ft (X2/df = 1.680)

and parsimony (AIC = 318.97) whilst the Causality and the Reciprocal models could

be considered similar in terms of fit, with nearly matching Normed Chi-Squared

statistics (1.346 and 1.342, respectively) and estimates of RMSEA (both .042),

although the Causality model did have a lower AIC (294.90) than the Reciprocal

model (306.45).

For Model E, all the non-significant paths of the Reciprocal model were

considered for removal, using the same criteria as for the previous models (minimal

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IFcm1 PWFcm1 NSPcm1 OWBcm1 MIcm1 WEcm1



e11

e21

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e111

e121

e13 e14 e15 e16 e17 e18

1 1 1 1 1 1

Table 3.12

Results of longitudinal model testing for the Integrated model


A. Stability 2.079 .988 .074 (.059-.089) 349.07 1.77 (1.59-1.99)

B. Causality 1.346 .997 .042 (.015-.062) 294.90 1.50 (1.39-1.65)


D. Reciprocal 1.342 .998 .042 (.005-.065) 306.45 1.56 (1.46-1.69)

E. Trimmed 1.269 .998 .037 (.000-.058) 287.91 1.46 (1.36-1.61) Note. X


2/df > 1, close to 1 indicates good fit, <1 overfit; CFI




of saturated model = 342.00; ECVI (Expected Cross-Validation Index), lowest is most likely to be


Figure 3.8. The best fitting of the Integrated models, the Trimmed Reciprocal model Note. IF: Individual Factors; PWF: Positive Workplace Factors; NSP: Negative Spillover; OWB:

Overall Well-Being; MI: Mental Illness; WE: Work Engagement; „cm‟ composite variables from the

Integrated model; 1,2,3: Times 1, 2 and 3 respectively

325

β, p >.20). The Trimmed Reciprocal model had improved fit (X2/df = 1.269,

RMSEA= .037, 90%CI = .000-.058) and parsimony (AIC = 287.91) over the

Causality and the Reciprocal models, and with the lowest ECVI (1.46), the Trimmed

Reciprocal model was the most likely to be replicated in a similar sample of

participants. Figure 3.8 showed the Trimmed Reciprocal model. The correlations, the

auto-lagged and cross-lagged paths (shown in Appendix J, Tables J.16 and J.21) are

reported in the next section on the standardized regression weights and significance

of the paths.

3.6.7 Synchronous correlations, standardized regression weights and significance of

paths in the longitudinal models

In the previous section, the Trimmed Reciprocal models were shown to

represent the best fit in each of the models examined. Removing trivial pathways has

allowed the true non-zero paths to be seen and this next section examined the relative

importance of paths within the models, comparing the cross-sectional, auto-lagged

and cross-lagged paths.

First, the synchronous correlations between the composite variables at each

time are shown in Appendix J, Tables J.12 to J.16. In summary, for all models, the

correlations were in the expected directions and mostly highly significant. Not

unexpectedly, where „predictor‟ and „outcome‟ variables were closely aligned in the

factor score weights and the calculations of the composite variables, for example,

between Individual Factor and Overall Well-Being, there were very strong

correlations between the composite variables. However, collapsing the predictor and

outcomes merely collapsed the processes into one variable, which did not allow any

understanding of how these variables may influence each other. In the Well-Being

model, the correlations range upwards from r = .216, p < .001 (OWBwb3

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↔WWBwb3) and the strongest associations were found between Individual Factors

and Overall Well-Being and between Positive Workplace Factors and Work Well-

Being (r‟s > .80, p < .001) at each time period. In the Mental Distress model,

correlations range upwards from r = -.229, p < .001 (PWFmi2 ↔ MImi2) and the

strongest correlations were found between Positive Workplace Factors and Burnout

(r‟s > -.90, p < .001) and between Individual Factors and Mental Illness (r‟s > .74, p

< .001) at each time period. In the Well-Being - Mental Health model, correlations

range upward from r = -.236, p < .001 (PWFwbmh3 ↔ MIwbmh3) and r = -.260, p <

.001 (PWFwbmh2 ↔ MIwbmh2). The strongest correlations were found between

Individual Factors and Overall Well-Being (r‟s > .93, p < .001), Individual Factors

and Mental Illness (r‟s > .72, p < .001) and Negative Spillover and Mental Illness (r‟s

> .78, p < .001), with most correlations ranging between r = .40 and .70. In the Work

Engagement model, the correlations range upward from r = -.294, p < .001 (PWFwa3

↔ NSPwa3) and r = -.302, p < .001 (PWFwa2 ↔ NSPwa2). The strongest

correlation was between Positive Workplace Factors and Work Engagement (r‟s >

.93, p < .001) and the balance of the associations range between r = .40 and .60.

Unlike the previous models, the Integrated model had correlations that were

weaker, although all correlations were still significant. For example, the correlations

between Positive Workplace Factors and Mental Illness at Time 2 (r = -.188, p

=.021) and at Time 3 (r = -.187, p = .010) were the smallest within the model. There

were similar correlations, although with greater significance, between Work

Engagement and Mental Illness at Time 2 (r = -.203, p = .005) and at Time 3 (r = -

.230, p = .002) and between Positive Workplace Factors and Negative Spillover at

Time 2 (r = -.208, p = .004). The strongest correlations were between Positive

Workplace Factors and Work Engagement (r‟s > .90, p < .001) and Individual

327

Factors an Overall Well-Being (r‟s > .90, p < .001) at each time, whilst the

correlations at each time between Individual Factors and Mental Illness and Negative

Spillover and Mental Illness ranged from r = .60 to .80.

Based on Cohen‟s (1988) estimation of the effect size of correlations, r >

.100 is a small effect size, r > .243 is a medium effect size, and r > .371 is a large

effect size. From the correlations described here, with the exception of the lowest

correlations in each model (which are medium-small), all correlations can be classed

as having moderate to very strong effect sizes. In the context of understanding the

dynamic relationships between the individual, their workplace and their well-being,

mental health and work engagement, these correlations indicated that the cross-

sectional component, which captured how the individual feels on the day, was

important to understanding the models.

Whilst the correlations are reasonably straight forward, there was a challenge

to describe and discuss the standardized regression weights of the five longitudinal

models in this section in a clear and concise manner. Adding numbers to the figures

of the best fitting models can overload the diagrams beyond reasonable levels. To

overcome these difficulties, the standardized regression weights and their

significance levels were presented in tables, which were given separately for each

model in Appendix J Tables J.17 to J.21. Rather than examine each model separately,

however, the tables of standardized regression weights for the paths in each of the

five models have been combined and the beta weights have been colour coded to

reflect their effect sizes to gauge the relative importance of the auto-lagged and

cross-lagged paths. As a general conclusion, the auto-lagged paths within each model

have greater beta weights than the cross-lagged paths, indicating that the stability of

a variable over time is important. Given also that the fit of the models was improved

328

by adding the auto-lagged Time 1 to Time 3 paths, it can be seen that variables at

Time 3 were dependent not only on the recent past (i.e. Time 2) but on levels of

those variable in the more distant past (i.e. Time 1) as well. Whilst the cross-lagged

paths had smaller beta weights than the auto-lagged paths, these paths were crucial to

models because it is the addition of these paths that provided the best fit of the

models in all cases.

For the following discussion on the beta weights of paths between variables,

it was possible to state the effect size of the standardized paths between variables (as

the standardized direct effects) and standardized regression weights between

variables were equal. For example, a 1 standard deviation increase in variable A (at

the head of the causal arrow) lead to a change in variable B (the end of the causal

arrow) represented by the effect size, which was expressed as a proportion of a

standard deviation (Arbuckle, 2006). For example, in the Well-Being model, the

direct effect of IFwb1 on IWwb2 is .685, such that as IFwb1 went up by 1 standard

deviation, IFwb2 increased by .685 standard deviations. The equivalent standardized

regression weight of the direct causal path for IFwb1 to IFwb2 was β = .685, p <

.001.

Interpretation of these standardized effect sizes is done was on similar metric

as effect sizes which were based on the standardized difference between group

means, that of small (d = .20), medium (d = .50) and large (d = .80) effect sizes

(Cohen, 1988). The current thesis will follow the rules given by Holmes-Smith et al.

(2006), such that the effect size of beta weights, β < .20, are weak effects, β weights

from .20 to .30 are mild effects, β weights from .30 to .50 are moderately strong

effects, β weights from .50 to .80 are strong effects, whilst β weights over .80 are

considered to be very strong effects. To make the varying effect sizes easier to see in

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a large table, rather than just a collection of numbers, the beta weights have been

colour coded, with grey for weak effects, green for mild effects, pink for moderately

strong effects and red for strong and very strong effect sizes. The increasing intensity

of the colour is designed to highlight the increasing strength, and therefore

importance, of the pathways.

To read Table 3.13, several points need to be considered. First, the Time 2

and Time 3 variables for all models have been collapsed into single columns,

labelled for example „IFxx2‟ and „IFxx3‟ for Individual Factors. The purpose of this

arrangement was to allow the longitudinal effect in one model to be compared with

another. For example, was the effect of the Individual Factor on Mental Illness

similar in the different models in which both appear? By collapsing the Mental

Illness variables at Time 2 in each model into one column, it can be seen that

Individual Factors at Time 1 reduced Mental Illness at Time 2 in the Mental Distress

model (β = -.162, p = .029), in the Well-Being-Mental Health model (β = -.213, p <

.001) and in the Integrated model (β = -.221, p =.001) and with similar results from

Time 2 to Time 3. This combination allowed the conclusion to be drawn that the

individual who had higher levels of the Individual Factor would generally have lower

levels of Mental Illness at a later time.

Second, for the outcome columns WExx2 and WExx3, these columns

collapse Work Well-Being, Work Engagement and Burnout from each of the

different models and can be distinguished by (W), (E) or (B) respectively after the

beta weights. The separation into rows, of course, allowed the models to be regarded

separately. As noted in the CFAs in the previous section, Work Well-Being, Work

Engagement and Burnout are similar constructs, with opposite loadings (Work Well-

Being and Work Engagement – positive; Burnout – negative) and are based on

330

highly similar scales.

Third, the table was arranged such that the auto-lagged (those paths between

the same variable over time) are shown on the leading diagonal (top left to bottom

right of the table) with the cross-lagged causality paths in the upper triangular matrix

(top right triangle of the table) and the cross-lagged reverse causality paths are in the

lower triangular matrix (bottom left triangle of the table).

3.4.8 Individual Factors in the longitudinal models

The auto-lagged paths have strong to very strong effects from Time 1 to Time

2, strong to moderately strong effects from Time 2 to Time 3, and mild to moderately

strong effect sizes from Time 1 to Time 3. Individual factors had a positive, weak

effect on Overall Well-Being over time in the Well-Being model and significant,

weak to mild positive effects from Time 2 to Time 3 on Overall Well-Being in the

Integrated and Well-Being- Mental Health models.

Individual Factors also had significant, weak to mild effects in the Integrated,

Well-Being- Mental Health models on reducing Mental Illness over time; in the

Mental Distress model, and Well-Being- Mental Health and Integrated models.

Individual Factors also had a weak, but significant effect on reducing Burnout in the

Mental Distress model.

3.6.9 Positive Workplace Factors in the longitudinal models

The auto-lagged paths for Positive Workplace Factors for Time 1 to 2 and

Time 2 to Time 3 have strong to very strong effects. For the auto-lagged paths from

Time 1 to Time 3, there are mild to moderately strong effects. The auto-lagged paths

for Positive Workplace Factors in the Mental Distress model were interesting as the

Time 1 to Time 2 path was very strong, the Time 1 to Time 3 was mild, whilst the

Time 2 to Time 3 was trivial and not included in the final model. A beta weight

331

above 1 did not negate the solution of the longitudinal model, as a linear dependency

would make an inadmissible solution in a CFA, but may indicate the presence of a

suppressor variable. It was not clear which variable could be acting as a suppressor

variable and this will require further investigation in the future.

There are weak effects from Time 1 to Time 2 for Positive Workplace Factors

to boost Overall Well-Being in the Well-Being and Integrated models and reduce

Mental Illness in the Mental Distress and Integrated models. However, Positive

Workplace Factors had greater, significant effects on Work Engagement, increasing

Work Well-Being and Work Engagement (weak to moderately strong effects in the

Well-Being, Work Engagement and Integrated models) and reducing Burnout with

moderately strong effects in the Mental Distress model.

3.6.10 Negative Spillover in the longitudinal models

The auto-lagged paths for Negative Spillover were all highly significant and

strong from Time 1 to Time 2, moderately strong to strong for Time 2 to Time 3 and

mild to moderately strong from Time1 to Time 3. Negative Spillover had a mild

effect that increased Mental Illness in the Mental Distress model, the Well-Being

Mental Health model and the Integrated model. Negative Spillover also had a weak,

negative effect on Work Engagement in the Integrated model.

3.6.11 Overall Well-Being in the longitudinal models

The auto-lagged paths for Overall Well-Being had very strong to strong

effects from Time 1 to Time 2, moderately strong to strong effects from Time 2 to

Time 3 and mild to moderately strong effects from Time 1 to Time 3. Overall Well-

Being had a positive weak to mild, significant effect on Individual Factors in the

Well-Being model, in the Well-Being - Mental Health model and in the Integrated

model. Overall Well-Being also led to a mild, significant reduction of Negative

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Table 3.13

Effect sizes of the standardized regression weights for the auto-lagged and cross-lagged paths for all the longitudinal models

„Input‟ „Outcome‟ variablesa

Variablesa

IFxx2 IFxx3 PWFxx2 PWFxx3 NSPxx2 NSPxx3 OWBxx2 OWBxx3 MIxx2 MIxx3 WExx2 WExx3

IFwb1 .685*** .304*** .143

IFmi1 .897*** .270*** -.162* -.048** (B)

IFwbmh1 .647*** .256*** -.213***

IFwa1 .867*** .240***

IFcm1 .633*** .269*** -.221**

IFwb2 .444***

IFmi2 .496***

IFwbmh2 .651*** .229*** -.175**

IFwa2 .653***

IFcm2 .616*** .176*** -.139*

PWFwb1 .860*** .395*** .077** .219*** (W)

PWFmi1 1.059*** .266*** -.142* -.331** (B)

PWFwbmh1 .840*** .361***

PWFwa1 .997*** .291*** .422** (E)

PWFcm1 .845*** .329*** .084** -.054 .166*** (E)

PWFwb2 .808*** .484*** (W)

PWFmi2 .352*** (B)

PWFwbmh2 .509***

PWFwa2 .679*** .334* (E)

PWFcm2 .736*** .348* (E)

NSPmi1 .631*** .291*** .125*

NSPwbmh1 .803*** .254*** .134*

NSPwa1 .749*** .271***

NSPcm1 .762*** .304*** .185***

NSPmi2 .441*** .065†

NSPwbmh2 .604*** .171*** † p < .10, * p < .05, ** p < .01, *** p < .001

Strong effect β > .50 moderately strong effect β= .30 to .50 mild effect β= .20 to .30 weak effect β< .20 Note. Auto-lagged paths on the leading diagonal; Causality paths in upper triangular matrix; Reverse Causality paths in lower triangular matrix a „Input‟ and „Outcome‟ variables refer to variables at the beginning and the end of the causal arrow, respectively

333

Table 3.13 (continued)

„Input‟ „Outcome‟ variablesa

Variablesa IFxx2 IFxx3 PWFxx2 PWFxx3 NSPxx2 NSPxx3 OWBxx2 OWBxx3 MIxx2 MIxx3 WExx2 WExx3

NSPwa2 .561***

NSPcm2 .540*** .094* -.015† (W)

OWBwb1 .184 .699*** .340***

OWBwbmh1 .259*** .882*** .288***

OWBcm1 .253*** .828*** .317***

OWBwb2 .181*** .589***

OWBwbmh2 . -.141** .357**

OWBcm2 -.115* .434***

MImi1 .179** -.119** -.106† .310** .244***

MIwbmh1 .050* -.136† .344*** .218***

MIcm1 .048*** .311*** .208***

MImi2 -.158*** .067** .102** .472***

MIwbmh2 -.179* .237***

MIcm2 -.113† .354***

WWBwb1 .054* .611***(W) .366*** (W)

WEwa1 -.150 .415** (E) .270*** (E)

WEcm1 .065* .651*** (E) .315*** (E)

WWBwb2 -.340***

WEwa2 -.114 .226 (E)

WEcm2 -.119 .184 (E)

BURNmi1 -.124* .315† .208** .468*** (B) .265*** (B)

BURNmi2 -.609*** .904*** (B) † p < .10, * p < .05, ** p < .01, *** p < .001

Strong effect β > .50 moderately strong effect β= .30 to .50 mild effect β= .20 to .30 weak effect β< .20 Note: The composite variables in the longitudinal models are IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, OWB: Overall Well-Being, MI:

Mental Illness; WWB (or W) Work Well-Being, WE (or E) Work Engagement, and BURN (or B): Burnout. WWB, WE & BURN are similar constructs (WE overall term) but with

opposite loadings; W, E or B indicates which outcome was used in that model; The letters after the name of the composite variables, „wb‟, „mi‟, „wbmh‟, „wa‟, „cm‟, indicate that

the variable is from the Well-being, Mental Distress, Well-Being-Mental Health, Work Engagement, and Integrated models respectively. „1‟, „2‟ and „3‟ indicate the variables at

Times 1, 2, and 3 respectively. The „xx‟ after the outcome variables indicate that all Time 2 or Time3 variables are collapsed into the one column, to allow comparison of β weights

between all models

Note. Auto-lagged paths on the leading diagonal; Causality paths in upper triangular matrix; Reverse Causality paths in lower triangular matrix a „Input‟ and „Outcome‟ variables refer to variables at the beginning and the end of the causal arrow, respectively

334

Spillover from Time 2 to Time 3 in the Well-Being – Mental Health and Integrated

models.

3.6.12 Mental Illness in the longitudinal models

Unlike the auto-lagged paths for Individual Factors, Positive Workplace

Factors, Negative Spillover and Overall Well-Being, the auto-lagged paths for

Mental Illness had only moderately strong effects from Time 1 to Time 2, mild to

moderate effects from Time 2 to Time 3 and mild effects from Time 1 to Time 3.

However, the mild effect of Mental Illness on Individual Factors was both

interesting and counterintuitive. There was no evidence that the effect of mental

illness had been enhanced by suppressor variables (i.e. negative suppression)

(Tabachnick & Fidell, 2001), as the correlations between the variables was greater

than the beta weights (r‟s > .530, β‟s < .180). From Mental Illness to Individual

Factors for Time 1 to Time 2, there was a significant positive influence in the Mental

Distress, Well-Being-Mental Health and Integrated models. However, the bivariate

correlations between Mental Illness at Time 1 and Individual Factors at Time 2 were

negative; r = -.649, p < .001 Mental Distress model; r = -.615, p < .001, Well-being

Mental Health model; and r = -.624, p < .001 in the Integrated model. These

relationships would indicate that Mental Illness at Time 1 would lead to higher levels

of the Individual Factors at Time 2.Although there was a positive effect from Time 1

to Time 2, in the Integrated model, the Time 2 to Time 3 relationships were in the

expected direction. Further research could try to separate which of the variables may

be involved with the negative suppression of Mental Illness, which may then in turn

lead to better understanding of the persistence of mental illnesses and whether the

individual could have indeed gained insights from having a mental illness.

Mental Illness had a mild, negative effect on Negative Spillover from Time 1

335

to Time 2 in the Mental Distress and Well-Being-Mental Health models, although, as

with the effect on Individual Factors, this was not an effect of negative suppression.

However, the effect was varied from Time 2 to Time 3, having a mild, positive effect

in the Mental Distress model and a mild negative effect in the Well-Being Mental

Health and in the Integrated models.

3.6.13 Work Engagement in the longitudinal models

Engagement and disengagement (i.e. burnout) in work did not have the

consistent patterns over time that are seen in the Individual Factors, Positive

Workplace Factors, Negative Spillover and Overall Well-Being factors and to a

certain extent, the Mental Illness factor. The auto-lagged paths were mostly highly

significant, first as Work Well-Being, showed strong effects from Time 1 to Time 2,

moderately strong effects from Time 1 to Time 3 but the trivial path between Time 2

and Time 3 was not included in the final Well-Being model. Second, as Work

Engagement, there was a moderately strong to strong effect from Time 1 to Time 2, a

weak to mild, but non-significant, effect from Time 2 to Time 3 and a mild to

moderately strong, significant effect from Time 1 to Time 3 in the Work Engagement

and Integrated models. Third, as Burnout, there was a moderately strong effect from

Time 1 to Time 2, a very strong effect from Time 2 to Time 3, yet only a mild effect

from Time1 to Time 3 in the Mental Distress model.

Work Well-Being and Work Engagement had weak, significantly positive

effects on Individual Factors from Time 1 to time 2 in the Well-being model and in

the Integrated model, whilst Burnout had a weak, negative effect on Individual

Factors in the Mental Distress model. There were weaker, counterintuitive

relationships between Work Engagement in its various forms and Positive

Workplace Factors, despite the strong positive correlations between the two

336

variables, which would discount the possibility that suppression was occurring

(Tabachnick & Fidell, 2001). There were negative, weak to mild effects on Positive

Workplace Factors in the Work Engagement model, weak to moderately strong

negative effects from Time 2 to Time 3 in the Well-Being and Integrated models.

Burnout also had a mixed influence with a moderately strong, although only

marginally non-significant positive effect for Time 1 to Time 2 and a strong, highly

significant negative influence from Time 2 to Time 3 on Positive Workplace Factors

in the Mental Distress model. In all forms, work engagement led to reduced

perceptions of the positive workplace over time, indicating that there is a complex

relationship between the conditions of work and the individual‟s feelings of

engagement or disengagement with their work. Further research is necessary to better

understand how and why strong positive synchronous relationships could lead to the

loss of engagement in work over time.

3.6.14 Gain and loss spirals

Whilst the hypothesis that largest influences on a variable over time were the

direct effects of the same variable on itself over time was supported, the summaries

for the effects of each composite variable show that there are many significant cross-

lagged paths between the variables, which supported the hypothesis of the presence

of gain and loss spirals. In this section, these are brought together to show where gain

and loss spirals were occurring. Whilst the gain and loss spirals are described

separately, these occur within the same models, so the final step considered the net

effect of gain and loss. Gain (or loss) spirals occur where variable A at Time 1

increased (or decreased) variable B at Time 2, then variable B at Time 2 increased

(or decreased) variable A at Time 3, leading to the individual having more or less

resources over time.

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Gain spirals can be seen between Individual Factors and Overall Well-Being

in three of the models, increasing these resources over time. First, in the Well-Being

model (IFwb1 to OWBwb2, β = .143, OBWwb2 to IFwb3, β = .184), second, in the

Well-Being –Mental Health model (OWBwbmh1 to IFwbmh2, β = .259, IFwbmh2 to

OWBwbmh3, β = .229) and third, in the Integrated model (OWBcm1 to IFcm2, β =

.253, IFcm2 to OWBcm3, β = .176). Gain spirals were also shown for Individual

Factors and Mental Illness in the Well-Being-Mental Health model (MIwbmh1 to

IFwbmh2, β = .050, IFwbmh2 to MIwbmh3, β = -.175) and in the Integrated model

(MIcm1 to IFcm2, β = .048, IFcm2 to MI3, β = -.139). A gain spiral occurred in this

case as Mental Illness will be decreased over time, although this did not account for

the negative effect of Mental Illness on Individual Factors at Time 3 in the Mental

Distress model.

Loss spirals can be seen in the Mental Distress model between Negative

Spillover and Mental Illness (NSPmi1 to Mimi2, β = .125, Mimi2 to NSPmi3, β =

.102), but the nature of the spiral was more complicated in the other models that

involved Negative Spillover and Mental Illness. In the Well-Being-Mental Health

and Integrated models, Negative Spillover had a consistent effect of increasing

Mental Illness from Time 1 to Time 2 (β = .134, WBMH; β = .185, Integrated) and

Time 2 to Time 3 (β = .171, WBMH; β = .094, Integrated) in both models. However,

the effect of Mental Illness to Negative Spillover was more varied, with Mental

Illness at reducing Negative Spillover in the Well-Being –Mental Health model

(MIwbmh1 to NSPwbmh2, β = -.136; MIwbmh2 to NSpwbmh3, β = -.179) and in

the Integrated model (MIcm2 to NSPcm3, β = -.113). As the beta weights of the

Negative Spillover paths were similar to those of the Mental Illness paths, the net

outcome appeared to be neither clearly a gain or loss in resources (i.e. a gain would

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be a reduction in mental illness), rather maintenance of the current level of

functioning.

The counterbalancing of positive influence and negative influence was seen

in other parts of the models. Positive Workplace Factors had a consistently positive

effect on increasing Work Engagement and reducing Burnout from Time 1 to Time 2

and Time 2 to Time 3 in all the models in which both variables appear (β‟s range

from .166 (Integrated) to .484 (Well-Being)). However, Work Engagement had a

mostly negative effect on Positive Workplace Factors (range: β = -.340, Time 2 to

Time 3, Well-Being model; to β= -.114, Time 2 to Time 3, Work Engagement

model). When considering the net outcome of these opposing influences, it appeared

that there was small net gain in resources (i.e. increases in Work Engagement), as the

Positive Workplace Factor paths were somewhat greater than the Work Engagement

paths. The downward trend from Work Engagement could be felt however, should

there be a loss of workplace resources and any changes in the individual‟s view of

the intrinsic reward that would decrease the value of the Positive Workplace Factors.

Within the models, there were mutual reinforcement effects over same time

period, rather than occurring over the longer time periods. For example, Individual

Factors and Overall Well-Being also reinforced each other between Time 1 and Time

2 (IFwb1 to OWBwb2, β = .143, OWBwb1 to IFwb2 = .184) as well as the further

gain from Time 2 to Time 3 (OWBwb2 to IFwb3, β = 181). There were also effects

that were counterbalanced between individual Factors and Burnout in the Mental

Distress model (IFmi1 to BURNmi2, β = -.048, BURNmi2 to IFmi3, β = -.124) and

the effect of Overall Well-Being on Negative Spillover was unopposed

(OWBwbmh2 to NSPwbmh3, β = -.141, OWBcm2 to NSPcm3, β = -.115).

The models show the presence of both gain and loss spirals, and the more

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Scale

Figure 3.9

Weighting of the auto-lagged and Cross-lagged paths in the integrated model Note. IF Individual Factors; PWF Positive Workplace Factors; NSP Negative Spillover; OWB Overall

Well-Being; MI Mental Illness; WE Work Engagement; „tm1‟ Time 1; „tm2‟ Time 2; „tm3‟ Time3

complex counterbalancing of the gain and loss of resources. It may not be reasonable

to „add‟ up the beta weights, but the net sum of all the paths would appear to be

slightly positive. As such, this would suggest that the usual progress of development

over time is for individuals to gradually gain in resources, where there are no

external events that „challenge‟ the individual. This gradual drift toward the positive

supplements the stability of functioning that was seen across time, seeing the gradual

accumulation of resources in the longer term. In summary, paths within and between

variables were both important to individual functioning over time. Using arrows of

differing widths, Figure 3.9 give a visible weighting to the different paths, from the

strongest to the weakest effect in the Integrated model. The resulting „web‟ gives a

sense of the relative importance of the paths; the constancy of the variables, with the

lighter, cross-linkages indicating where change may occur.

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3.6.15 Squared multiple correlations from the models

Whilst the models have good fit, the final consideration of the longitudinal

models was to whether the models explain sufficient variance in the composite

variables to be considered satisfactory. Table 3.14 showed the squared multiple

correlations for each composite variable, which was equivalent to the variance but

expressed as 0 to 1.0, rather than as a percentage. Time 1 was not included, as these

variables were considered the „cause‟ of the composite variable at the later times.

The squared multiple correlations allowed an estimate of the reliability of the

models, with the convention is that > .50 is acceptable (Holmes-Smith et al., 2006).

In all models, a large, substantial portion of the variance in each composite variable

was explained by the modelling, with the highest accounting for 82.5% of Individual

Factors and82.2% of Overall Well-Being at Time 3 in the Well-Being model. The

least amount of variance explained was for Burnout and Mental Illness with 42% and

42.2%, respectively of the variance at Time 3 explained which was still a large effect

(J. Cohen, 1992).The explanation of Individual Factors, Positive Workplace Factors,

Negative Spillover, Overall Well-Being and Work Engagement (also as Work Well-

Being) were considerably higher, indicating that the models were well able to capture

the longitudinal relationships.

3.6.16 Summary of the results of the longitudinal models

The results of this chapter have demonstrated that longitudinal relationships

could be successfully modelled in this data sample. The initial structural models at

Time 1 showed that the models were tenable. The confirmatory factor analyses then

established the measurement models that gave rise to the factor score weights, which

were used to construct the composite variables for use in the longitudinal models.

These composite variables overcome a major limitation of longitudinal models,

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Table 3.14

Squared Multiple Correlations for all models for Time 2 and Time 3 composite

variables

Time 2 Models

Composite variable WB MD WBMH WE Integrated

Individual Factors .802 .711 .758 .743 .780

Positive Workplace Factors .740 .668 .714 .714 .716

Negative Spillover ‡ .547 .498 .561 .607

Overall Well-Being .793 ‡ .778 ‡ .785

Mental Illness ‡ .670 .422 ‡ .478

Work Well-Being .655 ‡ ‡ ‡ ‡

Work Engagement ‡ ‡ ‡ .676 .667

Burnout ‡ .420 ‡ ‡ ‡

Time 3 Models

Composite variable WB MD WBMH WE Integrated

Individual Factors .825 .733 .788 .749 .795

Positive Workplace Factors .721 .668 .709 .685 .698

Negative Spillover ‡ .597 .567 .615 .621

Overall Well-Being .822 ‡ .819 ‡ .820

Mental Illness ‡ .496 .488 ‡ .483

Work Well-Being .627 ‡ ‡ ‡ ‡

Work Engagement ‡ ‡ ‡ .646 .640

Burnout ‡ .659 ‡ ‡ ‡

‡ Variable not included in the model

Note. Abbreviations of model names: WB – Well-Being; MD – Mental Distress;

WBMH Well-Being – Mental Health; WE – Work Engagement

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allowing measurement errors of the latent variables to be contained within these new

observed variables. The fit of the longitudinal models was tested with a set of five

models (A to E). In each case, the best fit was achieved by trimming trivial paths out

of the models (model E) to limit both Type I and Type II errors, achieving a balance

between not accepting chance associations (Type I errors) but not removing true non-

zero paths that were not significant in these particular models (Type II errors).

Further, the factor score weights illustrate the underlying linkages between

variables that are usually taken as being at either end of the causal arrow, as predictor

and outcome. For example, the construction of Individual Factors and Overall Well-

Being, and of Positive Workplace Factors and Work Engagement showed that these

constructs are intertwined, despite being conceived as separate constructs. Given the

linkages, individuals who are high in dispositional optimism will be high in life

satisfaction and psychological well-being, whilst those who are dedicated to their

work will experience greater opportunities to use their talents and skills, in a positive

workplace.

Collating the beta weights of all the models into one table allowed for the

consideration of what were the influential pathways over time, adding to the results

of the synchronous correlations. The strongest influences were between the variables

at each time and within the same variables over time, with weaker cross-lagged paths

between the variables over time. The mutual reinforcement of the variables over time

lends support for gain spirals, for example between Individual Factors and Overall

Well-being over time, and loss spirals, for example between Negative Spillover and

Mental Illness over time. However, there were also counterbalanced effects between

Positive Workplace Factors and Work Engagement in its various forms, which

indicate that the relationships were more complex than would be supposed from the

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strong bivariate correlations between the variables.

3.7 Discussion of the longitudinal models

The hypotheses for Study 2, that the longitudinal models would show

evidence of stability and change over time have been supported. The hypothesis that

the strongest influence on a variable would be it‟s previous levels was supported and

the modelling found that Individual Factors, Positive Workplace Factors, Negative

Spillover and Overall Well-Being had strong effects on themselves across time

whilst Mental Illness had more moderate effects and the effects of Work Engagement

was more variable. Further, the hypothesis that the cross-lagged paths would also be

influential was also supported. The modelling showed that gain and loss spirals were

present in the longitudinal modelling, with a slight positive net gain in the

individual‟s resources shown over time. Rather than a spiral (which implies a large,

noticeable effect), the net increase over time in the time frame of the current research

is more of a „drift‟, as the effects are weak and would not be seen until a number of

years had passed. The gradual accumulation of benefits from positive psychological

functioning has been found in longstanding longitudinal studies, such as the Study of

Adult Development (Peterson et al., 1988; Vaillant, 2002) where differences in

outcomes were not apparent until the participants had been studied for a number of

decades. The Nun Study similarly found that early positivity led to increased

longevity and better cognitive functioning five or six decades later (Danner et al.,

2001). Whilst the longitudinal models in the current study are over a short period

(less than 12 months), the net gain in resources could be expected to be ongoing into

the future, gradually accumulating until the gain was sufficiently large to be seen as a

difference between individuals. In this way, the longitudinal models may provide the

mechanism by which personality differences become manifest across a lifespan.

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The extensive results of the longitudinal models and the analyses that lead to

their establishment will be discussed in several parts. First, the results of the Time 1

SEMs are considered, as their framework reflected the relationships already seen in

the multiple regressions. Second, the CFAs are considered, particularly the CFA that

involved Burnout and Work Engagement as these results were in contradiction to the

prevailing European research on these constructs. The discussions on the CFAs are

followed by an examination of the factor score weights that were generated by each

CFA. The factor score weights were very revealing about the underlying

relationships between each of the indicator variables, and uncovered how some

variables, usually described as „cause and effect‟ were intimately entwined; to have

one is to have the other. The „entwined‟ variables were the individual difference and

well-being indicator variables, as well as work dedication and skill discretion that

were tightly linked together.

Finally, the models themselves are discussed. The stability and change

captured by the models provided insight into the mechanism by which resources may

be accumulated or lost over time. As hypothesized, the strongest relationships were

between the variables are each time (concurrent functioning) and within the same

variables over time, with mild to weak reciprocal relationships between the variables

over time. Importantly, these reciprocal relationships show the leverage points at

which psychological or management interventions can be made, therefore improving

the developmental outcomes for working adults.

3.7.1 Discussion of the Time 1 SEMs

The results of the Time 1 SEMs showed that satisfactory models can be

obtained from the data available in this thesis. Following on from the multiple

regressions in Chapter 3, the expected relationships were found between individual

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resources, the workplace resources and the negative spillover between work and

family domains. Individual Factors, measured as dispositional optimism and coping

self-efficacy, were positively related to Positive Workplace Factors, the supportive

working conditions that were measured as skill discretion, affective commitment and

job autonomy, but both were negatively related to Negative Spillover, measured as

the problems that spill over between work and family domains. Further, these three

factors then had the expected positive and negative effects on well-being, mental

illness, work engagement and burnout.

The final Time 1 model that combined the positive and negative outcomes

had a satisfactory fit and showed that Individual Factors, Positive Workplace Factors

and Negative Spillover had slightly different influences on well-being, work

engagement and the mental health outcomes. Individuals with higher levels of the

Individual Factor, that is more optimism and had greater self-efficacy, had greater

Overall Well-Being, as more satisfaction and purpose in their lives and an added

sense of professional efficacy in their work and less mental illnesses. As such, the

model added further confirmation to previous research, where both dispositional

optimism and coping self-efficacy were central to well-being and mental health

(Atienza et al., 2004; Chang, 1998; Hart et al., 2008; Jex & Bliese, 1999; Judge et al.,

1998; Major et al., 1998). Similarly, the link between Positive Workplace Factors,

with more job autonomy and more skill discretion, leading to high levels of Work

Engagement, as the enthusiasm and zest (work dedication) and absorption in one‟s

work, was similar to that found in previous research (for example, Bakker et al.,

2005; Schaufeli & Bakker, 2004). However, Positive Workplace Factors also

increased the individual‟s mental illnesses. This was unexpected, although it could be

speculated that increasing responsibilities and decision-making associated with more

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complex challenging jobs add to perceptions of mental illness, perhaps to stress in

particular.

Negative Spillover, as the problems or tiredness that come from one domain

to another, lead as expected to an increase in Mental Illnesses, and reductions in

Work Engagement, but did not have an influence of Overall Well-being. As the

regressions in Chapter 2 have shown, Negative Spillover can account for any lack of

social support at work. These results add further support to the link between adequate

workplace supports and increased work engagement and reductions in burnout

(Bakker et al., 2006; Houkes et al., 2003; Klusmann et al., 2008a). These results form

a solid basis to explore the longitudinal relationships between these variables, with

the differences in influences hinting that there may be more complex relationships in

the analyses that follow. The next step of the modelling process was to conduct

confirmatory factor analyses as the basis for the longitudinal models.

3.7.2 Confirmatory factor analyses

The Confirmatory Factor Analyses (CFAs) separately examined and formed

the basis for the five proposed longitudinal models. For four of the five analyses, the

Well-Being, Mental Distress, Well-Being-Mental Health, and Integrated CFAs, the

CFAs were easily established, well fitting and with few additional paths over and

above the relationships through the latent factors. However, the CFA for

Burnout/Work Engagement was unexpectedly difficult and required extensive

additional analyses to understand why the CFA with these two factors did not work

as initially proposed. Using burnout and work engagement as separate factors based

the two scales for Burnout (i.e. emotional exhaustion, cynicism and professional

efficacy) (Maslach et al., 1996) and Work Engagement (i.e. work vigour, work

dedication and work absorption) (Schaufeli et al., 2002), the initial CFA was

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hopelessly complicated in an effort to achieve any sort of reasonable fit. As such, the

CFA using the two scales was unacceptable and other solutions were explored, as

outlined in the Results of this chapter, with the single factor as the only satisfactory

solution.

Re-examining the original development of the work engagement scale, it was

seen that Schaufeli et al. (2002) had found that the best fit for Burnout and Work

Engagement was indeed two factors, but not as the two separate scales. The two

factors were the „core‟ of burnout as the first factor (i.e. emotional exhaustion and

cynicism) and the engagement scales (i.e. work vigour, dedication and absorption)

with professional efficacy as the second factor. When this arrangement of factors was

tried in the current analyses, the CFA was not successful and could not be defined as

the matrices were non-positive definite. That the two scales would not be separate

factors was surprising to say the least, as it appears contrary to published results. It

also raises questions about the initial scale development and why work engagement

was proposed without the addition of professional efficacy. It is puzzling that the

research would suggest a broader conception of work engagement than the

researchers themselves have found and used (for example, Schaufeli & Bakker,

2004; Schaufeli et al., 2008).

In this sample, these results indicate that the two-factor solution as proposed

by Schaufeli and colleagues (2002) can not be supported. Instead, the results indicate

that Work Engagement should be considered as a single factor or continuum with a

positive end (i.e. work engagement) and a negative end (i.e. burnout). The single

factor of work engagement-to-burnout of the current results is similar to Maslach and

Leiter‟s view that work engagement is the positive opposite of burnout (i.e. having

energy, involvement and professional efficacy). Work in these situations is

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challenging, meaningful and important (Maslach & Leiter, 1997; Maslach et al.,

2001), whereas burnout is the opposite and the loss of engagement is therefore

defined as the components of exhaustion (i.e. the loss of energy), cynicism (i.e. lack

of involvement) and the loss of professional efficacy. The previously meaningful job

has become uninviting and uninspiring (Maslach, 1998; Maslach et al., 1996;

Maslach & Leiter, 2008; Maslach et al., 2001). The current research would define the

positive end of the continuum similarly, but more broadly than Maslach‟s view. In

the current research, work engagement is measured as being high in work dedication

(the zest for work), work absorption (the focus of work), professional efficacy (the

competence the individual feels about work) and with a lack of cynicism (to not be

jaded by work and remain involved).

From the CFAs and the factor score weights, work dedication is central to the

continuum of work engagement, being part of both the calculation of work

engagement and the workplace factors and capturing the energy of work

engagement. Interestingly, the wording of the work dedication items (Schaufeli et al.,

2002) may also capture the intrinsic value of work (Baard et al., 2004). Individuals

are asked if their jobs are „inspiring‟, „challenging‟, and „meaningful‟ and if they are

„enthusiastic‟ and „proud‟ of their work. Agreeing with these items implies an

energetic approach to work, as zest and joy for the job at hand. Further, being

absorbed in work also implies an energetic approach to work, as being engrossed and

deeply interested in what is to be done. The loss of dedication, losing that energy,

that zest and focus, in addition to losing feelings of competence and a feeling of

worth in the work that they do will bring about the negative, burnt out end of the

continuum when the positive attributes are eroded and then lost. Taking in the

perspective of the Conservation of Resources theory (Hobfoll, 1989, 2001, 2002),

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burnout occurs as the result of the actual or potential loss of resources and the loss of

possible gains in resources (i.e. rewards are not obtained as could be expected for the

work done). Resources can be objects, work conditions, energy or personal

characteristics. Work engagement, as the single factor, captures the individual who is

highly engaged in their work and in work situations that allow some or all of the

individual‟s resources to be gained and replenished, by the intrinsic rewards from

challenging interesting work and the satisfaction of competence and „doing well‟.

Burnout will result where the individual is unable to replace resources lost through

difficult working conditions, for example, where workloads are high or the sense of

community is reduced or lost in the workplace. Coping requires resources and if not

restored, burnout will occur as positive emotions and motivations are lost (Hobfoll &

Freedy, 1993).

Returning to the general discussion on the CFAs, the next point is to consider

the additional paths that were added between the errors of the indicator variables path

to improve the fit of the CFAs. The correlations between the latent factors were all in

the expected directions, with like constructs being positively correlated (for example,

Individual Factors and Overall Well-Being) and opposite constructs being negatively

correlated (for example, Individual Factors and Negative Spillover). The additional

paths between the indicator variables indicate that there are „extra‟ relationships over

and above the main relationships through the latent factors. As such, it could be

considered that it is the extreme ends of a measure that are captured by the

correlations and which are not explained by the relationships between the latent

factors alone (Holmes-Smith et al., 2006).

In the Well-Being model, there is only one but in the other models there are

more additional paths that improved the fit, with the sign of the correlations shown in

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the diagrams of the CFAs. Whilst it is not ideal to have these paths and in a perfect

model they would not be necessary, it could be argued that these paths represent an

interesting insight into the relationships that are not accounted for by the dominant

relationships between the latent factor and their indicator variables (Holmes-Smith et

al., 2006). For example, in the Mental Distress CFA, there are additional positive

correlations between dispositional optimism and stress and between coping self-

efficacy and cynicism. These paths would be interpreted after the main relationships

(i.e. Individual Factors is negative correlated with both Mental Illness and Burnout)

and therefore, Individual Factors are associated with less Mental Illness and Burnout.

Over and above this however, the over-optimistic person may be more stressed, and

the person with greater self-efficacy could become more cynical, perhaps comparing

themselves to less capable others. This latter situation occurred among community

nurses in Norway, who were rated by their peers as thriving and highly engaged in

their challenging jobs. These high performing nurses judged other nurses by their

own standards and were often frustrated by others‟ perceived underperformance. The

nurses felt that they would be better do all the work themselves, increasing their

overload and fatigue and leading to burnout (Vinje & Mittelmark, 2007).

Again considering the Mental Distress CFA, there are other relationships that

extend beyond the relationships between the latent factors. For example, the

correlations between exhaustion and skill discretion could indicate that when taken to

the extreme, a job that requires a high level of creativity and skill usage can be tiring

or unsustainable, when that level is needed or maintained on a long-term basis. Given

the discussion in the previous paragraphs of the continuum between work

engagement and burnout, this link between skill discretion and exhaustion may be a

step in the decline of engagement that the individual has for their work. Where

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negative work-to-family spillover adds directly to stress and exhaustion, this could

indicate that not only is Negative Spillover is a general malaise, problems and

tiredness stemming from the workplace are tied directly to the individual‟s feelings

of stress and exhaustion.

These additional paths occurred in the other CFAs and can be viewed as

beneficial or detrimental additions to overall functioning, either reinforcing or

eroding the individual‟s resources. Examples of the beneficial pathways would be the

positive correlation between autonomy and professional efficacy and between

professional efficacy and psychological well-being (Integrated CFA) and the

negative correlations between affective commitment and firstly cynicism (Work

Engagement CFA) and secondly, exhaustion (Integrated CFA). These additions

could be explained as extra autonomy that directly enhances the individual‟s view of

themselves as competent at work, as well as feeling capable at work directly added to

their overall sense of psychological well-being. Further, the benefits of being

attached to one‟s work also have direct benefits to guard specifically against

exhaustion and cynicism.

Examples of the detrimental linkages would be positive correlations between

skill discretion and stress and skill discretion and negative work-to-family spillover

(both in Well-Being-Mental Health CFA) and work absorption and negative work-to-

family spillover (Work Engagement and Integrated CFAs). It could be considered

that the detrimental effects arise from „too much of a good thing‟, as was the case

with optimism and stress and coping self-efficacy and cynicism in the Mental

Distress model. These are speculative conclusions, but may explain why over-

confidence could be detrimental to the individual (Erhlinger et al., 2008) and why

realistic optimism protects self-esteem (Schneider, 2001) and focuses behaviour

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towards more realistic goals (Armor & Sackett, 2006; Armor & Taylor, 1998;

Aspinwall et al., 2002). The link between too much absorption in work and negative

work-to-family spillover was found also in the moderated regressions and may be

linked to not disengaging from work. Recent research has found that persisting with

work outside the usual working hours, rather than taking time for other activities lead

to more negative affect, more fatigue and less positive affect (Sonnentag & Bayer,

2006; Sonnentag & Zijlstra, 2006). Further research is necessary to understand if this

will provide the link between absorption in work and negative spillover.

In summary, the CFAs found that most of the relationships between the

indicator variables could be explained by the relationships between the latent factors.

The small number of addition paths directly between the indicator variables adds to

the explanations of psychological functioning by capturing what happens over and

above the main relationships, perhaps showing why excess in any domain may be

detrimental or indeed more beneficial.

3.7.3 Factor score weight from the CFAs

From the CFAs, the next step was to convert each of the latent variables into

observed variables that could be used in the longitudinal models. For example, the

latent variable Individual Factors became the observed variable, Individual Factors,

using the factor score weights generated by the CFAs. As noted previously, this step

was necessary to minimise the errors within the longitudinal models, which would

otherwise make the models extremely unwieldy and unstable (de Jonge et al., 2001;

Zapf et al., 1996). The factor score weights give an indication of the contribution of

the indicator variables to the new, observed composite variable, ranging from

substantial contributions to little or no effect from the indicator variables (Holmes-

Smith et al., 2006). As with the CFAs, this was mostly straight forward but with

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some extremely interesting patterns of associations. First, the straight forward factor

score weights. Across the models, Mental Illness was mainly the sum of the

depression and stress scores, Negative Spillover was the sum of the negative work-

to-family spillover and negative family-to-work spillover scores with the addition of

exhaustion score in the Integrated model, and Burnout (in the Mental Distress model)

was the sum of the exhaustion and cynicism scores.

However, there are interesting and unexpected combinations for the

calculations of the observed variables: first for Individual Factors and Overall Well-

Being and second, for Positive Workplace Factors and Work Engagement. Individual

Factors and Overall Well-Being were closely linked in their construction when they

occur in the same models. Individual Factors were the sum of dispositional optimism

and psychological well-being and life satisfaction, whilst Overall Well-Being was the

sum of life satisfaction and psychological well-being and dispositional optimism in

the Well-Being, Well-Being-Mental Health and Integrated CFAs. From the literature,

positive illusions and happiness are closely tied to good physical and mental health

and are adaptive in challenging situations (Taylor, Kemeny, Reed, Bower, &

Gruenewald, 2000), with realistic optimism and a positive self-bias being important,

consistent predictors of well-being (Shmotkin, 2005). Further, as shown by the

regressions in Chapter 2, dispositional optimism was a powerful resource for the

individual and was a predictor of many psychological outcomes. Hobfoll (2001,

2002) considered that resources occur together as resource caravans, reinforcing and

supporting each other over time. It is likely that the factor score weights are showing

that Individual Factor and Overall Well-Being do mathematically co-exist: to be

optimistic is to have well-being and to have well-being is to be optimistic. This

would support the concept of resource caravans as well as providing the mechanism

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for the previous findings that positive illusions and happiness are closely linked.

The other close linkage was shown for the Positive Workplace Factors and

Work Engagement, such that how an individual perceived the conditions of their job

was largely determined by their enthusiasm for the job. This added further to the

interesting results for Work Engagement, which have been discussed previously. In

the Mental Distress model with Burnout rather than Work Engagement, Positive

Workplace Factors was the sum of (the absence of) both cynicism and exhaustion as

well as the workplace resources themselves. In the other models, Positive Workplace

Factors was the sum of work dedication with supplements from the workplace

resources. For the individual, it appears that being able to use their skills and be

creative and have autonomy in their working conditions was enhanced by their

enjoyment of their work. Perhaps this weighting could be considered as the joy that

comes from a job that challenges one‟s skills and abilities. Work Engagement is

substantially focused on work dedication which is how much the individual enjoys

the challenges of their work and how much meaning, pride and enthusiasm the

individual gains from their work. In other words an engaged worker is an individual

who relishes their work, with minor input from skill discretion. As noted previously,

work dedication appears to share commonality with intrinsic rewards of work (Baard

et al., 2004). Perhaps what is measured by skill discretion, job autonomy and

affective commitment is filtered by the feelings of enthusiasm and joy of being able

to do a worthwhile job which may account for the close linkages between workplace

resources and enthusiasm for work. As with optimism and well-being, perhaps the

internal rewards and motivation that come from a challenging, interesting job are

only seen in jobs where skills and talents are challenged by that job. This seems to be

a circular argument but may explain the findings in the factor score weights and

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further research will allow a better understanding of the results.

Given these close linkages in the construction of these four observed

variables, the longitudinal analyses were rerun with the composite variables

combined (e.g. Positive Workplace Factors and Work Engagement combined into

Work Engagement only) rather than kept separate. However, these analyses did not

shed light on the reciprocal processes involved. Collapsing Individual Factors and

Overall Well-Being, and collapsing Positive Workplace Factors and Work

Engagement into single factors merely hid the processes, such that the Stability

model (i.e. auto-lagged paths only) was the best fit of the longitudinal data rather

than a model with any cross-lagged paths. As the purpose of the current thesis was to

explore the reciprocal relationships over time, keeping the variables separate allowed

the interplay over time to be seen, although this may make the results more

challenging to interpret. Further, although work dedication was an overwhelming

part of the motivation and affect associated with working, rerunning the CFA with

work dedication as the single indicator for the Work engagement waws not practical

in SEM, as one of the underlying mathematical assumptions is that latent factors

have two or more indicator variables (Byrne, 2001; Kline, 2006). The contributions

of the other indicator variables, although small, do add to the nuances of

understanding work engagement in its entirety and as such, were retained in the

subsequent analyses.

3.7.4 How factor score weights explain the relationships of the Integrated model

To illustrate how the factor score weights can explain the balance of the

relationships found in the CFA and the weighted contribution of each indicator

variable to the composite variables, the Integrated model will be spelt out in detail.

There are many similarities to the simpler CFAs, which is to be expected as the

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Integrated CFA is the combination of these earlier CFAs. As with the Well-Being

and Well-Being-Mental Health CFAs, the Individual Factors composite variable

showed that a large part of individual differences are due to the individual‟s

dispositional optimism and coping self-efficacy (weighted for the large number of

items in the scale) with support from the individual‟s happiness (as life satisfaction),

sense of purpose (as psychological well-being) and enjoyment of their work (as work

dedication) but with the dampening effect of any depression that they may have.

How the individual experiences their workplace (Positive Workplace Factors) was

strongly influenced by the enjoyment and sense of challenge that their work brought

(as work dedication), in addition to the conditions of their work, as the skills and

creativity that they can express (as skill discretion) and the control that they had over

work conditions (as job autonomy). Negative Spillover was the sum of the troubles

that the individual experiences that came from the problems and tiredness that were

domain-specific and from the work domain in particular and spillover was

exacerbated by the individual‟s feelings of exhaustion in general.

When considering the composite variables that represent the well-being and

mental health outcomes, the patterns of the factor score weights were also similar to

those of the previous CFAs. The individuals‟ Overall Well-Being came mainly from

their hedonic satisfaction (as life satisfaction) and sense of purpose (as psychological

well-being), with positive support from their optimism (as dispositional optimism),

their sense of managing difficult situations (as coping self-efficacy) and the skills

and creativity if their work (as skill discretion). Interestingly, Overall Well-Being

was lessened by greater levels of competence about the work that the individual did

(as professional efficacy) which would suggest that a narrow focus on succeeding at

one‟s work did not add to the individual‟s general sense of well-being. An

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individual‟s measure of Mental Illness came principally from the individual‟s level

of depression, with stress and anxiety following in importance, with an interesting

and substantial contribution of exhaustion to Mental Illness and a small buffering

from the individual‟s level of optimism (as dispositional optimism). As was the case

in the Work Engagement CFA, the zest and enthusiasm that the individual felt about

their work (as work dedication) dominated Work Engagement, with small

contributions from how involved the individual is (as work absorption), the sense of

competence about their (professional efficacy) and the skills and creativity in their

work (as skill discretion).

The description of how the factor score weights were used to construct the

composite variables for the longitudinal models is unusual but there is little literature

with which to compare it. CFAs are usually only included as the first step of the

longitudinal modelling process, rather than an informative window on the complexity

of psychological functioning. It is likely that the space requirements of a journal

article would limit the description and analyses of these relationships, despite the

usefulness to understanding the web of influences on each latent variable at that

particular time.

3.7.5 The longitudinal models

Following on from the Time 1 models and the CFAs, the final step was to

construct and analyse the longitudinal models. The longitudinal models allow the

calculation of the relative importance of three separate influences on individual

functioning. First are the concurrent influences, which have been explored in the

CFAs and which are measured by the synchronous correlations in the longitudinal

models. In this way the influences of the present time are seen. Second is the stability

of functioning over time, measured as the auto-lagged paths between the same

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variables over time. The last component of the model to be tested is the cross-lagged

paths which are the reciprocal relationships between variables over time, which will

show if and how change occurs over time. The results of the longitudinal models

show that the strongest paths within the models are the synchronous correlations and

the stability paths over time, with weak to mild paths between the composite

variables over time. In summary, how the individual functions over time is the sum

of their current self and situation (i.e. their personal and work place resources, any

negative spillover and their well-being, mental health and work engagement at the

present time), how these factors were in the near and distant past (i.e. carrying

forward past levels of each variable), with some reciprocal paths over time leading to

a drift toward more positive functioning. It is in the reciprocal paths that the gain and

loss spirals are seen, as these paths show how resources can be gained and lost over

time, with the stability of functioning acting as a solid foundation for these spirals to

occur.

The influences were compared using the set of four, non-nested models

(Stability, Causal, Reverse Causal and Reciprocal), with the additional step

(Trimmed) that removed the trivial paths from the model. Whilst the set of four

variations within a longitudinal models has provided a sound platform to examine the

important relationships within the models, trimming the models of trivial paths, an

innovation of the current thesis, goes further to clarify the paths that are influential.

Rather than accept a model as a whole, looking at each path allows those paths that

do not contribute to be removed and Type I errors to be avoided. The improvement

in fit without the model becoming overfitted, would indicate that this was successful,

statistically sound and a useful analytic strategy for future research. Previous

research has shown that both Causal (de Jonge et al., 2001; ter Doest & de Jonge,

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2006) and Reciprocal (Demerouti, Bakker et al., 2004; Hakanen et al., 2008) models

can be better fitting in different studies, over various time periods and this additional

step of trimming trivial paths may add to the understanding of the outcomes by only

leaving those paths that are important.

After establishing that the Trimmed models were the best fitting longitudinal

models, it became obvious that presenting the results would be more challenging

than conducting the analyses themselves. It is not possible to put the values for all

the pathways onto a diagram as this would become illegible, unless the diagram is

very large. To this end, the values of the paths have been collated into a single table

(Table 4.13, pages 404 and 405) and colour coded to show the relative strength of the

paths (i.e. from red: strong effect, to grey: weak effect). Another reason for collating

the paths in the various models was to facilitate an understanding of what are the

common relationships between the variables and to see if the same types of

relationships occur across models. Looking at the models separately gives an

indication of what is happening there but when taken together there is sense of

overall functioning. Individuals are not just the positive or not just negative outcomes

but the sum of all of these at the same time.

3.7.6 Stability and change in the longitudinal models

3.7.6.1 Stability in the longitudinal models. The stability of competent

development can be seen in the strength of the synchronous correlations and the

auto-lagged paths in the longitudinal models. The results of the synchronous

correlations and the auto-lagged paths illustrate the relative importance of concurrent

functioning (as captured by the correlations) and the constancy of each component

over time (as captured by the auto-lagged paths) of the longitudinal model. The

results for Individual Factors, Positive Workplace Factors, Negative Spillover and

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Overall Well-Being indicated that these variables were firmly anchored in the near

and more distant past whilst the Mental Illness was less robust and therefore more

likely to change over time. Work Engagement showed more variation depending on

how it was measured and further research is required to understand the construct.

The Stability models provided a sound basis for understanding longitudinal effects,

even though those models were less well fitting when compared to more complex

Causality, Reverse Causality and Reciprocal models. The strong link between the

variables across the time periods found in the current longitudinal models is similar

to that found in the literature, where in SEMs and multiple regression analyses, the

strongest predictor of a variable‟s time 2 score was the corresponding time 1 variable

(Barnett & Brennan, 1997; Dikkers et al., 2004; Kelloway et al., 1999; Mauno et al.,

2007). The stability of personality and well-being in the models has support from the

literature in the set-point of well-being (Fujita & Diener, 2005) and the constancy of

temperament (Vaillant, 2002), happy dispositions (Diener, Nickerson et al., 2002)

and core self-evaluations (Judge & Hurst, 2008) over many decades.

The stability of the workplace was also not unexpected and job conditions

would continue to be the same unless the individual changes their job entirely.

Constancy of job conditions have been shown for both Dutch police officers

(Dikkers et al., 2004) and employees (Demerouti, Bakker et al., 2004) and for

healthcare workers (de Jonge et al., 2001) when this is reported. The stability of

negative spillover should also follow the same logic: if the individual and their work

are reasonably stable, then the problems between domains are likely to be similar

over time. This stability of negative spillover was found over 3 months in Dutch

employees (Demerouti, Bakker et al., 2004), over six months in US employees

(Kelloway et al., 1999), over 12 months in dual earner couples (Huang, Hammer,

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Neal, & N.A., 2004) and Dutch police officers (Dikkers et al., 2004; van Hooff et al.,

2005) and over one year and over six years for Finnish employees (Rantenan,

Kinnunen, Feldt, & Pulkkinen, 2008). Exhaustion was also persistent for many of the

participants (de Jonge et al., 2001; Demerouti, Bakker et al., 2004; Rantenan et al.,

2008; ter Doest & de Jonge, 2006; van Hooff et al., 2005), whilst work engagement

was also stable over time for Finnish dentists and health care workers (Hakanen et

al., 2008; Mauno et al., 2007).

Depression, as part of the Mental Illness composite variable, was less

persistent over time in the current longitudinal models and this in the case where it

was measured in other longitudinal models (van Hooff et al., 2005) although stress

was more stable over time (Kelloway et al., 1999). The lack of persistence of

depression may be accounted for spontaneous remission of depression. In a meta-

analysis of depression among the wait-list control groups for antidepressant trials, it

was estimated that up to 20% of participants in the control groups (who did not take

any medication or have any treatment) were no longer considered depressed as their

symptoms had reduced substantially (Posternak & Miller, 2001). Understanding the

course of untreated depression would assist with treatment and the current models

would indicate that mental illnesses do not carry forward their effects as strongly as

do individual differences, well-being, work conditions and negative spillover.

It is possible to find stability in the reviewed literature but the emphasis of the

literature is on how change occurs over time, rather than the stability that has just

been described. Change can tell how resources are lost and gained and successful

development is achieved but the stability of the variables can show where the

individual starts from and how that level of functioning is maintained over time.

Both parts, of course are necessary to fully understand individual functioning.

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3.7.6.2 Change in the longitudinal models. The possibility of change over

time can be seen from the addition of the cross-lagged paths to the Stability models.

Whilst these paths are mostly weak to mild, these reciprocal relationships add

valuable and important explanatory power to the models and indeed, improve the fit

substantially over the Stability models. The literature has focused on the existence of

gain and loss spirals separately but the current research has shown that there can also

be mutual reinforcement and counterbalanced effects between variables between two

time periods, without the full spiral being shown. In the literature, both gain spirals

(Hakanen et al., 2006; Llorens et al., 2007; Salanova et al., 2006) and loss spirals (de

Jonge et al., 2001; Demerouti, Bakker et al., 2004) have been shown to occur over

time and gain and loss spirals are present in the current longitudinal models.

When looking at the gain spirals, a gain spiral was found in the Well-Being

model with Individual Factors at Time 1 increasing Overall Well-Being at Time 2

and Overall Well-Being increasing Individual Factors at Time 3. Further, there are

gain spirals in the Well-Being – Mental Health and Integrated models where Overall

Well-Being at Time 1 increased the Individual Factors at Time 12 and Individual

Factors at Time 2 increased Overall Well-Being at Time 3. The close link between

Individual Factors and Overall Well-being further suggests that personal resources

and well-being are a resource caravan (Hobfoll, 2002), closely linked at any one

time and reinforcing each other over time. There is also support for these close

linkages from the developmental and occupational longitudinal studies. In the Study

of Adult Development (Peterson et al., 1988; Vaillant, 2002) and the Nun Study

(Danner et al., 2001), positive emotions and behaviours in early life ere linked to

better psychological outcomes and cognitive function in later life. Similarly,

individuals with more positive core self-perceptions as adolescents and young adults

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were more satisfied with their jobs, had greater occupational status and better health

than their less positive peers 25 years later, as positive perceptions about themselves

led to the accumulation of advantage in these individuals‟ working lives (Judge &

Hurst, 2008). Further, active problem solving increased mastery over time among

employees (Thoits, 1994) and happy people view their lives as subjectively better

terms that unhappy people, perpetuating their positive view of the world (Abbe et al.,

2003; Lyubormirsky & Tucker, 1998). Dispositional optimism is an expression of

self-regulation, which carries the notion of feedback loops that individuals can use to

direct their behaviours (Carver & Scheier, 1998). Gaining confidence in one‟s

abilities is akin to increases in mastery that comes from successful completion of

tasks be seen to be assist the self-perpetuation of a positive view of self and one‟s

well-being. The gain spirals and mutual reinforcement of Individual Factors and

Overall Well-Being found in the current research could be a mechanism for the

resource caravan and the accumulation of well-being and the personal resources in

the longer term.

The evidence for loss spirals was not as common, with only one found in the

Mental Distress model with Negative Spillover at Time 1 increasing Mental Illness at

Time 2 and Mental Illness at Time 2 increasing Negative Spillover at Time 3.This

outcome is similar to previous research where a loss spiral of poor work situations

and work-home interference increased exhaustion over time (Demerouti, Bakker et

al., 2004). However, whilst negative spillover had consistently increased the

individual‟s level of mental illnesses as has been shown in previous research

(Kelloway et al., 1999; van Hooff et al., 2005), the effect of mental illnesses in

increasing the negative spillover that the individual experienced at a later time was

not as consistent. The longitudinal models also showed some evidence that the

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experience of a mental illness could reduce Negative Spillover in the future, in the

Well Being –Mental Health and Integrated models. This unexpected result, where a

benefit is gained from an earlier experience of mental illness was also found between

Mental Illness and Individual Factors in the Mental Distress, Well-Being-Mental

Health, and Integrated models. While Mental Illness was increasing Individual

Factors, at the same time, Individual Factors was reducing Mental Illnesses over

time. This can be considered as another gain in resources as the individual would

experience fewer mental illnesses at the later time.

Given that the correlations between Individual Factors, Negative Spillover

and Mental Illness were in the expected directions, it is not clear why these

counterintuitive results have occurred. Mental Illness at an earlier time increased the

level of the Individual Factors (e.g. the individual would have more dispositional

optimism at the later time) and reduced Negative Spillover at a later time

(particularly from Time 1 to Time 2). It could be speculated that the modelling may

capture some underling relationships that are considerably more complex than the

simple corresponding bivariate relationships. For example, following the experiences

of a mental illness, an individual may gain insight into themselves and grow

psychologically to be more optimistic or have greater self-efficacy or a mental illness

may provide insights that make problems at work or at home less salient or

troublesome. This would be similar to an individual who responds adaptively to a

challenging situation and grows as opposed to the individual who is overwhelmed by

the same type of challenge and is stressed by the event (Christopher, 2004). In the

initial data analyses for the multiple regressions, it was found that the scores for

dispositional optimism were mildly negatively skewed, indicating that the

participants in the current study were mostly optimistic. Further, the participants in

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the current study mostly fell in to the upper end of the „normal‟ ranges or into the

„mild‟ ranges of the published data for depression and stress (S. H. Lovibond & P. F.

Lovibond, 1995). As more optimistic individuals are more likely to favour adaptive

and problem-solving coping strategies, being realistic, and/or reframing challenging

situations (for example, Armor & Taylor, 1998; Aspinwall & Taylor, 1997;

Schneider, 2001), it may be reasonable to conclude that for the participants of this

study, this heightened sense of optimism had provided some tools by which they had

mitigated any lasting effects of any mental illness that they had experienced.

Similarly, given the milder levels of mental illness in the sample, the participants

would be less likely to be adversely affected by mental illnesses and better able to

use optimistic, adaptive strategies in their lives. Earlier in the CFAs, there were

additional paths between stress and optimism and these may be important to better

understanding this problem. The positive correlation could not only mean that the

over-optimistic may be more stressed as well as the more stressed individuals may be

more dispositionally optimistic.

As the longitudinal models represent a small snapshot in time, they measure

processes that have no clear beginning or end (Menard, 1991). It is possible that the

Time 1 to Time 2 pathways include the influence of all past actions and it would be

necessary for future research to include additional measurement times to separate out

these possible influences. The influence of Mental Illness toward Individual Factors

and Negative Spillover also warrants further exploration of the sequences or

progression of functioning after a mental illness, to understand the long-term

consequences and whether growth and insight can occur (Christopher, 2004),

whether a cycle of mental illness is inevitable and how spontaneous remission may

occur (Posternak & Miller, 2001). In this way, the interesting, puzzling positive

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effects of mental illnesses in these longitudinal models may be better understood.

Rather than gain or loss spirals, there was another interesting set of reciprocal

relationships that can be seen between Positive Workplace Factors and Work

Engagement. As was shown in the factor score weights, Positive Workplace Factors

measures the individual‟s enjoyment of a job to which they are attached and which

provides them with control and creativity over their work, whilst work engagement is

largely the individual‟s dedication, zest and joy found in their work. What makes

these cross-lagged paths interesting and puzzling was that Positive Workplace

Factors added to future engagement in work but engagement in work does not add to

how the job is viewed and experienced in the future. There seems to be a

counterbalancing mechanism between the two that takes suggests that higher

engagement in work may deplete then individual‟s resources (energy, enthusiasm,

motivation), while on the other hand, the workplace is providing opportunities to

replenish those resources. However, this was different to the previous research that

had found gain spirals between efficacy beliefs and engagement in students (Llorens

et al., 2007). There were also separate gain spirals between work engagement and job

resources and work engagement and personal initiative among Finnish dentists when

measured over three years (Hakanen et al., 2008). The results of the models in the

current research however indicate that in the current research, there was not mutual

reinforcement between Positive Workplace Factors and Work Engagement rather

there are opposing forces.

Returning to the scales themselves may show some features that could give

rise to these unexpected results. The endorsement of the wording of work dedication

(Schaufeli et al., 2002) implies a state of high energy, of being enthusiastic, focused

and full of one‟s job. The endorsement of work absorption (Schaufeli et al., 2002)

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has the same implication of becoming engrossed in work and not taking time out. As

noted in the CFAs, work engagement in the current thesis was found to be a

continuum from highly energetic to burnt out, going from high energy to exhaustion.

I would argue that for the individual who is agreeing strongly with the work

dedication and absorption scales is also likely to be overusing their mental and

physical energy in their engagement in work. The question would become then,

when does work engagement become problematic? What is it about highly dedicated

and absorbed employees that would lead to later reductions in the positive way that

they viewed their jobs? Also, what is the „cost‟ to mental and physical energy of

being highly engaged and dedicated to one‟s work?

There are some possible explanations in the literature to these questions,

which may operate separately or together. First, it could be expected that over

committed and highly engaged workers may be less inclined to switch off at night

and to keep working outside of their usual working hours. Sonnentag and colleagues

have recently shown that not detaching from work activities after hours leaves

individuals with insufficient recovery from their work, reducing well-being and

positive affect (Sonnentag & Bayer, 2006; Sonnentag & Zijlstra, 2006) and reducing

work engagement (Sonnentag, 2003). In addition, socialising on the weekend rebuilt

resources (Fritz & Sonnentag, 2005) as did relaxation or learning new skills during

vacations, whilst continuing to think about work increased exhaustion and

disengagement (Fritz & Sonnentag, 2006). It could be speculated that the work

engagement‟s negative effect on positive workplace factors in the models is mediated

by the highly engaged individual‟s lack of detachment from their interesting,

absorbing work which does not allow time for physical recovery and renewal or

involvement in other life roles.

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Another possibility may come from an over-identification with work. Intense,

challenging interesting work can be seen as a reflection of one‟s identity, such that

working hard to succeed proves one‟s worth (Hewlett & Luce, 2006). Rather than

just pursuing financial success or materialism (Kasser & Ryan, 1993, 1996;

Nickerson et al., 2003), these „extreme‟ careers are personally motivating but involve

many long work hours and limit relationships and outside interests (Hewlett & Luce,

2006). Also, regardless of how much they enjoyed the challenges of their work, these

individuals did acknowledge that such intensity could not be sustained without cost

to their physical and mental health (Hewlett & Luce, 2006). Further, investing and

finding meaning in the accomplishments of one‟s work can be problematic where

there is not a balance between work efforts and the manageability of life overall.

Highly engaged community nurses were more likely to become fatigued and burnt

out where they were not able to delegate tasks, accept the standard of work of others

or use their coping resources to reflect on their situation. The nurses showed the

paradox of being engaged that unless it was balanced with the rest of their life, work

engagement would lead to burnout (Vinje & Mittelmark, 2007). Rather than only an

absorbing commitment and identity tied to work outcomes and accomplishments,

individuals can achieve a balanced commitment to their work by defining themselves

in other ways, while maintaining an interest and enthusiasm for their work (Hallsten,

1993). Teachers who were high in both resilience and engagement maintained the

greater levels of occupational well-being (i.e. less exhaustion and more job

satisfaction) and were rated as better teachers by their students than other teachers

low in either dimensions and particularly those who were low in both engagement

and resilience (Klusmann, Kunter, Trautwein, Ludtke, & Baumert, 2008b). From

these findings, it may not be that work engagement is problematic, rather that it may

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not be balanced against all of the interests in individual‟s life.

Therefore, rather than the wholly positive conception of work engagement as

outlined by Schaufeli and colleagues (Schaufeli et al., 2002), I believe that the

longitudinal models have shown that high work engagement may be „too much of a

good thing‟ and represent a drain on mental and physical resources. Whether this

occurs from a lack of detachment and recovery from work or from over-

identification and absorption in work can not be determined from the current results.

However, these speculations may provide a basis for future research to understand

the mechanisms by which work engagement could have a downward influence on the

way that the workplace is viewed.

Another interesting aspect of the counterbalancing between the effects of

Positive Workplace Factors and Work Engagement across time may be that these

opposing influences may provide a mechanism whereby work engagement slides into

burnout. The net effect between the beta weights for positive workplace factors and

work engagement appeared to be (roughly) positive such that the enthusiasm for

good working conditions supports any loss of energy from being engaged in work.

However, should there be a change in working conditions (e.g. a new manager,

business conditions change or a new position) that altered the individual‟s enjoyment

of their work and then it is possible that the buffering that existed from Positive

Workplace Factors could be compromised. Maslach and Leiter proposed that burnout

results from work overload, lack of control, reward and fairness, loss of community,

and mismatch of values (Maslach & Leiter, 1997, 2008). Any loss of work

engagement could then escalate into burnout as a result, as the Positive Workplace

Factors were not sufficiently supportive to overcome the loss of energy that occurs.

The net effect over time would then become negative, leading to the erosion of

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engagement and eventually to burnout. Whilst this is speculation, it is possible that

by following change in the workplace as it occurs, it may be possible to see this

process unfolding. By examining the steps between engagement and burnout, it may

be possible to see how the conditions of the workplace can buffer the individual or

hasten the onset of burnout.

3.7.7. Limitations and strengths of Study 2

The limitations and strengths of Study 2 are similar to those outlined for

Study 1. The sample is largely university educated and mostly female. Another

limitation may be that the time lag between data collection may not reflect the time

over which change naturally occurs. Longer time frames and more measurement

times would strengthen the longitudinal analyses and the understanding of the

dynamic influences unfolding over time. The selection of different predictor

variables from the pool of variables outlined in Study 1 may also yield different

outcomes. Future analyses should include different populations of working adults

(i.e. broader occupational groups and more males) and narrow the focus of

longitudinal modelling, by testing the significant predictors of each outcome, rather

than the broad approach taken in this research. Further, information from other

sources should be included to reduce the reliance on the individual s the only source

and over comes any problems with common method variance. The strength of the

longitudinal modelling was the high proportion of variance explained in the

composite variables, which indicated that the models were adequately explaining the

relationships present in the data. Further, there are enough participants to give the

analyses sufficient power to find the significant pathways and ensure the robustness

of the findings.

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3.7.8 Conclusions

The Time 1 models, CFAs and longitudinal models show that the present, the

near and distant past are all important to understanding how the individual functions

now and into the future. Whilst the time frame of this thesis represent only a slice in

the passage of an individual‟s life, it gives some insight into processes that have no

clear beginning or end (Menard, 1991). The interactions between the variables in this

small time frame can provide a basis for understanding how the individual develops

over much longer time frames. In the Study of Adult Development (Vaillant, 2002),

there were no differences between the men from the ages of 25 to 45, but after that

time, the life paths diverged and clear differences were evident as a result of

psychological factors. Pessimistic men had poorer mental and physical health, more

stressful life events and died earlier (Peterson et al., 1988). By the end of their lives,

the individual with optimistic outlooks were much more likely to be classed as

„Happy and Well‟ and used mature defenses, were in good health, and had stable

marriages (Vaillant, 2002). These outcomes were the result of long standing patterns

of behaviour by the study participants and the advantages in old age were the results

of years of repeated, adaptive behaviours.

The reciprocal paths here represent mild to weak effects, which in substantive

terms would mean that these paths would have mild to negligible effects (Holmes-

Smith et al., 2006) and lack clinical significance (Kazdin, 2003) especially over the

time frame of the current analyses. However, as the literature has shown, individuals

have a lifespan to accumulate advantage and disadvantage and the small gains that

the models represent can eventually build into more resources that could be used to

meet the challenges of everyday lives. Individuals who are high in dispositional

optimistic and self-efficacy will contribute to their own resource accumulation over

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time by their everyday behaviours, engaging in adaptive problem solving,

maintaining better relationships with family and friends, using humour and the other

strategies identified through the literature described previously. The resource

caravans ensure that the benefits of having resources accrue together and reinforce

each other over time. These individuals are generative, as Bronfenbrenner

(Bronfenbrenner & Morris, 1998, 2006) foresaw and competent development is the

result. Life is somewhat like an ocean liner that steams ahead and can only slowly

change its direction, but will eventually end up somewhere else rather than straight

ahead. The choices and behaviours that the individual makes every day may not

seem to be momentous, but the models and previous research would indicate that

these will add into a life time of resources that will confer advantages to

psychological functioning.

3.7.9 Indications for where to target future inventions

Another interesting outcome of the longitudinal models is that they indicate

where interventions may be made to improve psychological functioning. In

particular, the influences of Individual Factors and Negative Spillover on Mental

Illnesses could indicate that acting on the components of these factors could act as

leverage points for interventions to improve psychological functioning. For

example, as Individual Factors lead to decreases in Mental Illness over time, a

psychological program to bolster the components of Individual Factors would be

likely to enhance this effect. Likewise, organizational strategies could focus on

reducing the incidence of negative spillover, to improve later levels of mental health.

From the multiple regressions, negative spillover (particularly work-to-family) was

reduced by greater managerial support for work-life issues and general social support

at work and more egalitarian gender role attitudes.

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Taking the results of Studies 1 and 2, with the results of a workplace

resilience building program (for example, the PAR program (Liossis, Shochet,

Millear, & Biggs, 2009; Millear, Liossis, Shochet, Biggs, & Donald, 2008)), future

preventative mental health programs can be used to target the various important

components that have been identified by the research. Understanding which variables

are influential over time, are important for future well-being, mental health and

burnout prevention programs. The current research indicated that both the individual

and the employer can take steps to improve employee conditions. By strengthening

the individual and giving employers the tools to improve employee conditions, future

prevention programs and intervention strategies can be used to improve the well-

being, mental health and work engagement of working adults.

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Chapter 4: Discussion of research findings and conclusions

The research program for the current thesis has been successfully based on

Bronfenbrenner‟s developmental equation, D f PPCT (Bronfenbrenner & Morris,

1998). Competent development has been shown to be the result of effective and

meaningful interactions between the generative individual and a supportive

environmental context that continue over time. Across the three studies, the person,

context and time components of the equation has been explored, highlighting their

significance toward understanding what leads to successful, competent development.

In the current research, this competent development has been measured by greater

well-being (higher life satisfaction and greater psychological well-being), better

mental health (as the absence of depression, anxiety and stress) and being

engagement in one‟s work (which includes the absence of burnout).

The two studies examined the working adult in cross-section and in the

longer term. Study 1 and 2 were linked through the use of the same sample of

participants. Study 1 was a cross-sectional survey of working adults and Study 2 was

a prospective panel study that built upon the initial wave of data collection. Study 1

identified a group of the most frequent, significant predictors of the outcomes, which

formed the basis of the longitudinal modelling. The significant predictors represented

the person and context components of the developmental equation, with the

modelling in Study 2 showing the stability and change of the outcomes, person and

context variables over time, therefore encapsulating all of the developmental

equation.

This chapter will first summarise which of the predictor variables were

important to capture each part of Bronfenbrenner‟s developmental equation. The

major findings of the research will then be considered, followed by the applications

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of the research, the limitations and strengths of the research and finally, the future

directions for research.

4.1 The developmental equation, D f PPCT

The discussion of the research will start with considering how well the

framework of Bronfenbrenner‟s equation served as the basis for the research

program. That the „letters‟ contributed to a better understanding of psychological

functioning and development shows that this theoretical approach is a valid

framework in which to examine the life of working adults. The research program has

been able to show the most important parts of the Person, Context and Time

components of the developmental equation, D f PPCT to achieving the highest levels

of psychological functioning. Each of the outcomes had a mosaic of predictor

variables and the significant predictors will be considered in light of each of the parts

of the equation.

4.1.1 P, the person: The generative disposition

In Study1, the hierarchical multiple regressions found that dispositional

optimism and coping self-efficacy were central to predicting the many outcomes.

Dispositional optimism, in particular, was a powerful resource that underpinned the

breadth of psychological functioning. Study 2 further showed the importance of the

person over time as the predictors of both Individual Factors and Overall Well-Being

were entwined at each time and reinforced each other over time with Individual

Factors also leading to decreases in Mental Illness over time.

4.1.2 P, the person: Their demand characteristics

Rather than acting as separate influences in Study 1, humour as a coping

strategy was found to be completely mediated by optimism and self-efficacy, rather

than act as a significant predictor on its own. As such, humour could best be

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considered as part of the suite of behaviours that could be used by a person who was

optimistic and had high self-efficacy rather than separate abilities to manage

distressing situations.

4.1.3 C, the context.

In Study 1, the significant contextual predictors of the outcomes came from

having more workplace resources and less negative spillover in both directions

between work and family roles. Being attached to a job that allowed the individual to

use their skills and abilities and allowed input into decision making were the

important workplace resources. However, negative work-to-family spillover, in

particular, mediated between the outcomes and feeling in control of time and not

feeling busy, having egalitarian gender role attitudes, more managerial support for

work-life issues and social support from supervisors and co-workers generally. In

Study 2, Positive Workplace Factors positively influenced Work Engagement over

time, but did not impact on Overall Well-Being or Mental Illness, whereas Negative

Spillover dampened Overall Well-Being and increased Mental Illness over time.

4.1.4 T, Time.

In Study 1, time was not overtly considered but in Study 2, the longitudinal

modelling allowed cause and effect over time to be established and gain and loss

spirals to be seen. For example, Individual Factors and Overall Well-Being were

closely aligned at each time and give mutual reinforcement over time, leading to a

gain spiral in personal resources. Positive Workplace Factors boosted Work

Engagement but Work Engagement dampened Positive Workplace Factors over time

which would indicate that being highly engaged in work has detrimental effects in

the longer term unless workplace conditions can offset any losses in dedication and

enthusiasm for work. Overall there was a slight positive drift over time, when all the

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variables were considered together in the Integrated longitudinal model. The

longitudinal models showed that there are leverage points for mental health

interventions which could be targeted by workplace mental health programs.

4.1.5 Summary of D f PPCT.

By taking control of their own life, the individual can craft a life path that is

most suited to their own needs. Competent developmental outcomes were most likely

where the person was optimistic and had high self-efficacy, worked in a job that they

were attached to and which allowed them to use their talents and without too much

negative spillover between their work and family domains. Over time, there was

evidence of both gain and loss spirals in resources. In this way, individuals had

greater well-being, better mental health and greater work engagement at any one time

and across time, although there were some indications that excessive work

engagement could be problematic in the long term.

4.2 Major findings

Bronfenbrenner‟s developmental equation has been very useful in framing the

research on the working adult and the previous section has illustrated the important

points around each of the equation‟s parts. In addition, the research program has a

number of major findings and some interesting „non-findings‟ that will be discussed

in more detail.

The most important finding to me is the confirmation that the „active person‟

is central to the way that life is lived, as shown in previous research (Carver &

Scheier, 1998; Thoits, 1994). Research about the work-life interface that focused

primarily on the conditions of work, family characteristics or spillover between roles

has missed the most important ingredient, that of the person who is „doing‟ that

work-life interaction. In both studies, the individual is strongly represented as the

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main driver of how life is experienced and not as a passive recipient of working

conditions or spillover between roles. Study 1 and 2 measured individual differences

among larger samples to understand the underlying relationships at one time and

across time. Throughout the research, the importance of the individual in their own

life is highlighted, first by their actions on job and life choices and second, as an

optimistic active person predicting greater well-being and better mental health at the

same time and across time, with mutual reinforcement. The active person can design

their own life by being organized and choosing jobs that suit themselves and their

families, using humour and being reasonable with their time and energy.

Why are dispositional optimism (in particular) and coping self-efficacy such

powerful personal resources? Optimism is linked to better problem solving,

persistence toward solvable goals and more pleasant interpersonal relationships

(Armor & Taylor, 1998; Aspinwall & Brunhart, 2000; Scheier et al., 1994) and being

proactive toward the future (Aspinwall, 2005; Aspinwall & Taylor, 1997). In solving

anagrams, optimists used problem-focused coping rather than avoidance coping to

reduce their stress levels whereas pessimists adopted avoidant strategies that did not

reduce their stress levels (Iwanaga et al., 2004). Optimism is seen as flexible self-

regulation that adapts and manages the changes that occur around the individual,

foreseeing possible problems in the future and preparing for what is ahead by using

proactive coping (Aspinwall & Taylor, 1997).

Optimism and self-efficacy appear to be habitual ways of thinking and

responding to challenges that are over-learnt, in the way that well learnt skills

become automatic. I would argue that the habits of problem solving and adaptability,

inherent in the optimistic way of doing things are so well learnt in everyday

situations that when something difficult happens, such as sudden job loss or financial

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problems, the response is automatic and adaptive. In the way that sports people,

soldiers and firemen train for the heat of competition or battle or an emergency, the

optimistic individual will respond to confronting problems in the habitual way that

they usually do, so that they can solve problems they face or accept the inevitable.

Masten and Reed (2002) differentiated resilient and maladaptive children on the

basis of two dimensions of competence (competent or vulnerable) and adversity

(high or low). In situations of high adversity, competent children became resilient,

whilst vulnerable children behaved maladaptively. It is reasonable to expect that

competent adults, as defined by Bronfenbrenner‟s developmental equation

(Bronfenbrenner & Morris, 1998) and represented by dispositional optimism and

coping self-efficacy would also act resiliently when they faced challenges in their

lives. The usual habits of a competent life can then translate into a resilient

personality when faced with adversity, providing resources and reserves to overcome

losses. The results of the analyses and of the PAR program show that preventative

programs should be directed to improve individual functioning and that these

valuable, resilient skills can be taught.

The second group of major findings comes from the longitudinal models.

Using and extending previous research from Europe (for example, de Jonge et al.,

2001; Demerouti, Bakker et al., 2004; Llorens et al., 2007), the longitudinal models

in the current thesis are a novel extension to previous analytic processes. Removing

trivial paths in the models has clarified the influential pathways and allows the

analyses to move beyond accepting and interpreting the best fitting model. The

analytic process leading up to and including the final longitudinal models has given a

number of important findings. First is the close link between the individual and well-

being at any time and across time; second is the net effect between the individual and

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negative spillover on mental illness; and third is the exploration of work

engagement-burnout continuum and the effects of work engagement on workplace

resources.

The confirmatory factor analyses generated the factor score weights to

calculate the composite variables used for the longitudinal models and provided

insight into the underlying relationships between the indicator variables. The

calculations for the composite variables, Individual Factors and Overall-Well-Being

showed that dispositional optimism, life satisfaction, psychological well-being and

coping self-efficacy are entwined to the extent that all occur together, although in

differing combinations in the calculations of the respective composite variables. This

is an interesting result as the causal „arrow‟ is nearly always that dispositional

optimism leads to greater well-being rather than these co-occurring. In addition to the

cross-loadings in their calculations, the longitudinal analyses also showed that there

was reinforcement between Individual Factors and Overall Well-Being over time as a

gain spiral of resources. The strong links between individual differences and well-

being provide a mechanism for resource caravans (Hobfoll, 2002) to be established

and maintained over time. Following on from the conclusions about dispositional

optimism and coping self-efficacy as resources, well-being becomes tied to how the

individual views themselves rather than only as a summation of satisfaction with life

domains (Easterlin, 2006), which accords with previous research (Leonardi,

Sopazzafumo, & Marcellini, 2005; Shmotkin, 2005).

Whilst Individual Factors has mutually beneficial effect on well-being, it also

reduces the level of mental illness an individual experiences across time. In contrast,

Negative Spillover, measuring the problems and tiredness that flowed between roles,

had a consistently negative effect on mental illness over time. The net effect of

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Individual Factors and Negative Spillover on Mental Illness over time was slightly

positive, i.e. the individual had less mental illness. In addition there was another

intriguing finding: Mental Illness at Time 1 had positive effects at Time 2, by adding

to Individual Factors (i.e. increasing dispositional optimism and well-being) and

reducing negative spillover. I can only speculate that this counterintuitive result was

linked to the milder levels of mental illness among the participants and that they have

used their experiences to gain insight into themselves. In addition, mental illnesses

were not strongly persistent over time which may explain why individuals with

depression may have spontaneous remission (Christopher, 2004). Further research is

necessary to understand the experiences of mildly depressed individuals, whether

their depression would intensify or dissipate over time and what the influences on

this process may be.

Possibly the most unexpected results were the confirmatory factor analyses of

burnout and work engagement. The Utrecht Work Engagement Scale (Schaufeli et

al., 2002) was developed and proposed to be distinct from the Maslach Burnout

Inventory (Maslach et al., 1996) but in the current research, the two scales could not

be used as separate scales in the same analysis. Unlike the two factors found in

European research (for example, Schaufeli & Bakker, 2004; Schaufeli et al., 2008),

the current results showed that engagement and burnout were opposite ends of a

single factor or continuum. The single factor is in accordance with the alternative

way that burnout and work engagement are seen by Maslach and colleagues (2001).

When the two scales were used together in the Work Engagement and Integrated

longitudinal models, the solution was inadmissible and could not be improved. The

Burnout and Work Engagement scales were therefore reduced to a single

combination of Work Engagement, being defined as work dedication, work

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absorption and professional efficacy. In the Well-Being and Mental Distress

longitudinal models, the scales were used separately (as Work Well-Being and

Burnout, respectively) and successfully in the models.

After establishing work engagement as a single factor, the calculations of the

composite variables for the longitudinal models brought another interesting cross-

loading between work dedication (capturing the zest and enthusiasm for work) and

the workplace resources. From the factor score weights, Work Engagement was

largely due to work dedication, the enthusiasm and zest for work. The interesting link

however was that Positive Workplace Factors relied not only on the workplace

resources but was again strongly influenced by the individual‟s enjoyment of their

work. As such, greater dedication or enthusiasm for work was likely to occur in jobs

which the individual is attached to and which allow the individual to use their talents

and make their own decisions, a somewhat circular argument. These links make the

results of the longitudinal modelling more complex to understand. As noted in the

section on Time, Positive Workplace Factors consistently boosted Work Engagement

over time yet Work Engagement dampened Positive Workplace Factors. This

counterbalancing suggests that being constantly highly engaged in work is not

necessarily beneficial to the individual and may be „too much of a good thing‟.

Further, the counterbalancing may provide a mechanism for the loss of engagement

to lead to burnout. If the buffering offered by the Positive Workplace Factors is lost

by worsening job conditions (e.g. a new manager or new corporate structures), the

downward pressure from over commitment to work will not be countered, leading

possibly to burnout. Work dedication appears to have mostly positive effects on the

individual, by supporting appreciation of working conditions but balanced against

too much involvement and absorption in work that may run down physical and

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mental reserves. The mind-body connection was beyond the scope of the current

study but should be considered in future research of work engagement.

4.3 Interesting non-findings

There were a number of findings that were interesting because they were not

significant. Firstly, gender was not a significant predictor of the outcomes with the

only difference being that women enjoyed their work more than men. Given the

breadth of the outcomes measured and the diversity of predictors included in the

analyses, it can be concluded that men and women in the current sample do not differ

on important aspects of psychological functioning. They may have differently shaped

lives with different work hours and caring responsibilities, but they are neither more

nor less satisfied with their lives and have similar levels of psychological well-being,

work engagement and burnout. Perhaps more important than gender per se was

having an egalitarian gender role attitude, which were important to how much

negative work-to-family spillover the individual experienced. Without the fairness

implicit in egalitarianism, the individual experiences more negative work-to-family

spillover which was a significant predictor of many outcomes, including emotional

exhaustion and cynicism. Including gender role attitudes in future research would

shed more light on how egalitarian attitudes toward gender protect against negative

spillover. Further, the loss of fairness in the workplace is linked to increased burnout

(Maslach, 2006; Maslach & Leiter, 1997) and it would be useful to compare fairness

with egalitarian gender role attitudes, to understand the similarities and differences

between the two constructs.

Second, the presence of children did not negatively impact on their parents by

reducing psychological functioning compared to non-parents. Indeed for cynicism,

children provided a buffer toward its incidence and the greater family demands from

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younger children protected against anxiety. Both outcomes may be due to younger

and more children providing an alternative view and value to life that is not

contingent on work. Parental role commitment, even among individuals who did not

have children themselves, was a significant predictor of life satisfaction and

psychological well-being. The value of children, present or future, should be

considered in future research to understand if children and the perception of the

parental role is linked to the developmental of generativity (McAdam et al., 1993),

an important psychosocial outcome linked to mentoring the next generation.

Contrary to the headlines in the popular press, the length of the working week

was not of particular importance to any of the outcomes. In the current sample, hours

could not be considered responsible for any „unexpected tragedy‟ (Relationships

Forum Australia, 2007) or part of any „work-life collision‟ (Pocock, 2003). Working

hours was only a predictor of work vigour and only in the presence of suppressor

variables. The analyses found that it is the nature of the working conditions that is far

more important to understanding the individual‟s life that simply the hours they

work. As noted in Chapter 3, an hour in a job you do not like is too long, whilst 60

hours in an interesting, challenging and absorbing job may fly past. High levels of

work engagement may be problematic as noted in the longitudinal models but is

more important to consider engagement and workplace conditions rather than

looking at hours alone. Jobs come in many shapes and sizes, with differing

combinations of interest, challenge, workplace resources and annoyances, and hours

alone do not capture the complexity of the workplace experience.

4.4 Applications of the research

The research program has shown the importance of each part of

Bronfenbrenner‟s developmental equation. Both the multiple regressions and the

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longitudinal studies offered insight into the leverage points at which personal,

psychological, or managerial changes could be targeted. In both analyses, the results

have identified that bolstering the individual, improving workplace conditions and

minimising negative spillover will each have influences on increasing well-being,

mental health and work engagement immediately and into the future. In research that

I have been concurrently involved with, the Promoting Adult Resilience (PAR)

program targeted the leverage points identified in Studies 1 and 2 to target the

individual‟s strengths. In doing so, coping self-efficacy was increased and

maintained over time, in addition to reducing stress and depression and increasing

work-life fit over time among resource sector employees (Millear et al., 2008). A

second, shorter trial of the PAR program was undertaken with similar results among

government employees (Liossis et al., 2009).

Workplace resources were both directly and indirectly important to the

outcomes. The direct effects have been shown with the Positive Workplace Factors

and the indirect effect is those variables that were mediated by negative work-to-

family spillover. The effects of job social support generally and managerial support

for work-life issues were mediated by negative work-to-family spillover. Both forms

of support are more responsive to the direct efforts of supervisors and managers than

as a general corporate culture (T. D. Allen, 2001; Behson, 2002; C. A. Thompson et

al., 1999), indicating that this is an important and effective area that employers can

directly support and encourage among their staff. Taking steps to minimise negative

work-to-family spillover would benefit the employer by reducing losses due to

mental illness, through the lost productivity from absenteeism and presenteeism

(Andrews et al., 2001; Hawthorne et al., 2003). Minimising negative work-to-family

spillover benefits the employee by increasing job satisfaction, work engagement and

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reducing the burden of mental illness and burnout.

An extension of the considerations of the workplace resources, job autonomy

and skill discretion, is the recognition that an individual can face the conflict between

their employer‟s need for flexibility in their commercial operations and the

individual‟s need for flexibility in their own interests (Costa & Sartori, 2005). This

conflict can lead to more negative spillover as well, when managers do not support

their employees‟ family and life requirements (C. A. Thompson et al., 1999). In a

globally competitive marketplace, organizations may need to be open for business

„24/7‟ to be responsive to customers‟ needs and to be competitive. Employees are

expected to be responsive to change, constantly available and are often paid

extremely large salaries for their work (Hewlett & Luce, 2006). The attractions of

ambition, status and income do not change that these intense working conditions can

leave little time for personal matters. On the other hand are the jobs that are

personally flexible, such that the individual is more available for their families and

responsive to family‟s changing needs (Costa & Sartori, 2005). However, these jobs

are less likely to be highly paid and can limit career opportunities and can be

resented by other employees if someone has to cover for the employee‟s absence (C.

A. Thompson et al., 1999). In counselling individuals who may be faced with the

dilemma of organisational versus personal flexibility, it would be helpful to take into

account personal ambition, such that individuals can come to recognise their own

level of ambition, which could „drive‟ them to work long hours. Gaining a greater

understanding of their own needs and values allowed the participants to chose jobs or

craft careers that were challenging and interesting but not onerous. Designing

individual therapy programs or more general prevention programs should take into

account the individual‟s needs in the context of the flexibility that their work

388

provides. A proviso however should be a recognition that ambition exists and that

working long hours need not be problematic if it is in balance with the individuals‟

other interests and concerns.

The results of the research highlight that interventions for the individual and

the workplace are important to sound psychological functioning. Interventions that

bolster the individual can sit alongside managerial actions that promote workplaces

where jobs provide resources that increase work engagement and limit negative

spillover between work and family roles. Managers can use these results to direct

their efforts to support their employees‟ work-life choices through the structure and

content of their jobs, to the benefit of both parties.

4.5 Future research

The research program of the current thesis has answered many questions but

more questions have arisen. As noted previously in this chapter, egalitarian gender

roles and their links to negative work-to-family spillover and burnout warrant further

research to better understand the linkages, whilst children, present and future, and

parent role commitment provide an interesting possible connection with the

development of generativity. The counterintuitive finding of mental illness can boost

the individual and reduce negative spillover opens up a window on the reasons why

an individual may recovery from a mental illness without intervention and may be

linked to post-traumatic growth. Of further research interest is the lack of persistence

of mental illness over time in contrast to the strength of the effect of well-being over

time. This could be studied by a longitudinal study that involved diaries, more

frequent measurement times or interviews with individuals that followed mildly or

moderately depressed individuals to assess the way that depression or stress unfold

and may be resolved over time.

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The close linkages between individual differences and well-being warrant

further investigation to understand how these are interwoven and how they contribute

to resource caravans. In the context of a lifespan, understanding the mechanism for

accumulating advantages for a successful old age are important as the population is

aging. Hobfoll‟s Conservation of Resources (Hobfoll, 1989, 2001, 2002) theory has

been a very useful addition to the research by focusing attention on the gain and loss

of resources over time. The next phase of research would be a longitudinal study that

links behaviours and affect of dispositional optimism with well-being, using event

sampling, diaries or interviews to capture how the mutual reinforcement is occurring.

Another area where close linkages were seen was between work dedication

and the workplace resources. Future research is necessary to understand at which

point high levels of work engagement becomes problematic taking into account

personal preferences (including ambition and individual differences) for work and

family. Recent research has included the calculation of curvilinear effects for the risk

assessment of work-related health (Karanika-Murray, Antoniou, Michaelides, &

Cox, 2009) which has increased the variance explained by the analyses. Including

curvilinear effects may allow closer examination of work engagement to assess

whether higher levels are „too much of a good thing‟. There is limited evidence of

cross-sectional curvilinear effects but not in longitudinal analyses of the effects of

job characteristics (de Jonge & Schaufeli, 1998) but this research did not include

work engagement.

Further research is also needed to better understand the factorial relationships

between burnout and work engagement and why the results of the current thesis

diverge from the published European research. It is possible that the diversity of

occupations on the current sample has diffused some unaccounted or unknown

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occupational factor that is salient for the helping professions (for example, police and

health care workers used in previous research) but which is not as salient across

occupations more generally. One example could be the importance of decisions that

are involved in a job. For example a nurse making a mistake may seriously harm a

patient, whereas a salesperson making a mistake only annoys their customer. To

explore this possibility, comparing occupational work pressures (for example, task

emotiveness and responsibility for others; Roe & Zijlstra, 2000) may tease out

whether the importance of the decisions that the job requires influences the factorial

structure of burnout and work engagement. More critical responsibilities and risks in

a job (e.g. as a policeman or nurse) that lacks adequate supports may make the

change from burnout to work engagement more sudden than the linear decline

implied by a single factor or the curvilinear possibilities mentioned in the previous

paragraph. A possible framework for work engagement to burnout could be a

nonlinear, cusp catastrophe as proposed by Carver and Scheier (1998, p297) to

explain persistence toward goals. For work engagement, the change from

engagement to burnout (a large drop in engagement) would come from only a small

reduction in resources and can be thought of as the „straw that broke the camel‟s

back‟. Figure 4.1 shows the possible relationships between the individual‟s resources

(x-axis) and their engagement (z-axis) which would change with the importance of a

task or work (y-axis). At low importance or pressure from work decisions (i.e. low

„risk‟ occupations), there is a continuous relationship between resources and

engagement (Path B on Figure 4.1). In contrast, when the occupation has high

importance or pressure for decisions (i.e. high „risk‟ occupations), the relationship

could become suddenly discontinuous (i.e. the cusp or „fold‟ between work

engagement and burnout) (Path A in Figure 4.1). Work engagement would change to

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Figure 4.1

Proposed cusp catastrophe for the relationship between work engagement and

burnout. Diagram from http://users.fmg.uva.nl/hvandermaas/cusp.GIF

burnout very suddenly rather than a gradual decline implied by the continuum of

Path B. High engagement (high scores on x, y and z axes) would be evident in

individuals more personal and workplace resources, even where occupations are

more challenging (high scores on x and y axes, lower scores on z axis). However, the

loss of confidence would be seen to have different effects in different types of

occupations, being more sudden in more pressured jobs, leading to burnout (low

scores on x, y and z axes). Research is necessary to test this proposal by finding the

quantitative differences in work pressure across occupations which can then test the

way the burnout develops from work engagement, either as a steady progression or a

sudden change. The steady progression would represent the back surface as work

engagement peters out into burnout. The sudden change from engaged to burnout

http://users.fmg.uva.nl/hvandermaas/cusp.GIF

392

would be represented by the cusp catastrophe (i.e. the fold) at the front of the surface.

Understanding the progression of work engagement to burnout will further inform

psychological and managerial practices to prevent burnout developing in the future.

Negative spillover between work and family domains proved to be important

with strong and pervasive negative effects on the outcomes. There was evidence of

both mediation and moderation involving negative spillover that warrants further

investigation, as does the link between negative spillover and exhaustion which may

indicate that the effect of burnout is not confined to feelings about work but the

family domain as well. It was interesting that positive and negative spillover were

not particularly related in the current data which limits conclusions about work-life

balance being the sum of positive and negative spillover. It may be useful in future

research to consider these as having separate, unrelated effects rather than

complementary effects. Future research should also consider that although negative

spillover was important, it was buffered by the presence of personal resources and

the absence of workplace resources. It would be useful to establish the level of

resources a person can have before they are overwhelmed by negative spillover and

these findings could be added to psychological and managerial practices to buffer the

individual from negative spillover.

A last area for future research to be considered is to gain a better

understanding of positive spillover. The predictors of positive work-to-family

spillover were the same workplace resources that led to more competent

development and show similarities to Greenhaus and Powell‟s (2006) recent

theorising on work-family enrichment. The workplace resources are possibly

indicative of the beneficial, affective path between work and family roles and future

research can extend on this platform to understand how the workplace resources

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transfer positive affect to the family domain. Positive family-to-work spillover

captured the support from home that encourages the individual at work and was an

important predictor of life satisfaction and psychological well-being. Research could

be extended to understand the overlap between positive family-to-work spillover and

other forms of social support generally.

4.6 A final word

Study 1 found generally that the best psychological outcomes were more

likely when the individual is optimistic and confident in themselves, works in a job

that they are attached to and that allows them to decide how to use their talents and

skills and where there are lower levels of negative spillover between their work and

family roles. Study 2 added to this by showing the linkages and influences between

personal and workplace resources, negative spillover and psychological functioning

over time. There are many folk sayings about personal mastery but perhaps the Dalai

Lama said it most succinctly, „happiness is not ready made, it is the result of our

actions‟. My final word of this thesis is that you should be active in your own life

with all its ups and downs, it is the only one you have.

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Appendices

Appendix A: Call for volunteers from the university alumni

Dear member of the alumni,

PRIZES TO BE WON!!

WELL-BEING AND WORK-BALANCE STUDY

Volunteers are required for email survey

Happy at home and happy at work? If you are – or if you aren‟t – Prue Millear, a

PhD student in QUT‟s School of Psychology and Counselling wants to hear from

you. She is exploring the relationships between the individual, their work and

personal responsibilities, and their well-being and work-life balance.

To fully understand how the relationships develop over time, you will be asked to

complete the surveys at three time points; now (June, 2007), in 3 months time

(September, 2007), and 6 months after that (February, 2008). By completing all three

surveys, you will be in a draw to win a great prize, an „Accor Hotels Gift Cards‟,

valued at $250. The gift voucher can be redeemed at one of Accor‟s Hotels, such as

Novotel, Sofitel, Grand Mecure, All Seasons or Ibis Hotels across Australia. The

winners will be notified at the end of the project by email.

If you choose to participate, it is requested that you participate in the study once only

at each time point. The survey should take approximately 25 to 30 minutes to

complete. You can exit the survey and return at a later time to the same point, but

you must use the same computer to do so. Completion and submission of the

questionnaire will be taken as your consent to participate in the „Well-being and the

Work-Life Interface‟ Study.


Ethical considerations for this research

Please be assured that your answers will remain completely confidential and

anonymous. Because the study is longitudinal, email addresses of volunteers are

retained for the length of the project, BUT will be kept separate from any data

collected, so there is no link between answers and any particular person.

Participation is voluntary and you are free to withdraw from the study at any time

without comment or penalty. There are some risks associated with this project with

some questions that could be considered sensitive. QUT provides for limited free

counselling for research participants of QUT research projects, who may experience

some distress as a result of their participation in the research. Should you wish to

access this service, please contact the Clinic Receptionist of the QUT Psychology

Clinic on 07 3864 4578. Please indicate that you are a research participant.

Should you have any questions or comments regarding the ethical nature of this

research, please do not hesitate to contact Prue Millear. Alternatively, you can

contact Queensland University of Technology‟s Research Ethics Officer on 3864

2340.

Thank you once again for your interest and your time in completing this survey.

Please direct any questions regarding the research to

Mrs Prue Millear

PhD Student,

School of Psychology and Counselling,

Faculty of Health, Q.U.T., Carseldine, 4034,

Email: [email protected]



443

Appendix B: Call for volunteers from the public hospital

Dear member of staff,

WELL-BEING AND WORK-BALANCE STUDY

Volunteers are required for email survey!!

PRIZES TO BE WON! Prue Millear is researching well-being and work-life balance of Australian working adults

for her PhD in the School of Psychology and Counselling at QUT. The aim of the study is to

explore the relationships between the individual, their work and personal responsibilities,

and their well-being and work-life balance. To fully understand the relationships involved,

this is a longitudinal study and you will be asked to complete the surveys at three time

points; now (November, 2006), in 3 months time (February, 2007), and 6 months after that

(August, 2007). Every person who completes all three surveys will be in a draw to win

one of 4 Accor Hotel Gift Vouchers, valued at $250 each. The gift vouchers can be

redeemed at one of Accor‟s Hotels, such as Novotel, Sofitel, Grand Mecure, All Seasons or

Ibis Hotels across Australia. The winners will be notified at the end of the project by email.

All responses are confidential at all times and data will be collated into group data before

being used in the analysis and reports. Individuals will not be identified at any stage.

If you choose to participate, it is requested that you participate in the study once only at

each time point. The survey should take approximately 25 to 30 minutes to complete. Please

note that once you leave the survey, your answers are considered finished and cannot be

edited. Completion and submission of the questionnaire will be taken as your consent to

participate in the „Well-being and the Work-Life Interface‟ Study.

Click on this link to go to the survey:


Ethical considerations for this research

Please be assured that your answers will remain completely confidential and anonymous.

Data is collated for analysis and only group data is reported. Because the study is

longitudinal, email addresses of volunteers are retained ONLY for the length of the project,

BUT will be kept separate from any data collected, so there is NO link between answers and

any particular person. Participation is voluntary and you are free to withdraw from the study

at any time without comment or penalty.

There are some risks associated with this project with some questions that could be

considered sensitive. QUT provides limited free counselling for research participants of

QUT projects, who may experience some distress as a result of their participation in the

research. Should you wish to access this service, please contact the Clinic Receptionist of the

QUT Psychology Clinic on 07 3864 4578. Please indicate that you are a research participant.

Should you have any questions or comments regarding the ethical nature of this research,

please do not hesitate to contact Prue Millear. Alternatively, you can contact Queensland

University of Technology‟s Research Ethics Officer on 3864 2340 or the RBWH HREC on

3636 5490 or 3636 6132.

Thank you once again for your interest and your time in completing this survey.

Please direct any questions regarding the research to

Mrs Prue Millear

PhD Student,

School of Psychology and Counselling,

Faculty of Health, Q.U.T., Carseldine, 4034

Email: [email protected]



444

Appendix C. Time 2 Call to action

The email to both groups was identical, apart from the URL link to the survey.

Hi everyone!

Thank you for taking part and completing the first Well-being and Work-Life

Balance survey in September. We greatly appreciate your assistance and time. Your

participation in this survey will be of great value in developing our understanding of

work-life integration.

Could you please take the time to complete the survey for the second time? It should

only take about 25-30 minutes. In approximately 3-4 months we will ask you to

complete the survey for us for the final time. Each of the surveys is exactly the same

to allow comparisons of the questions across time.

Remember that by completing all three surveys, you will be in a draw to win one of 4

„Accor Hotels Gift Cards‟, valued at $250 each. The gift vouchers can be redeemed

at one of Accor‟s Hotels, such as Novotel, Sofitel, Grand Mecure, All Seasons or Ibis

Hotels across Australia. The winners will be notified at the end of the project by

email.

Please follow this link to go to the Time 2 survey

http://www.surveymonkey.com/s.asp?u=388242752463 (university alumni group)

OR

http://www.surveymonkey.com/s.asp?u=976363177082 (hospital group)

If you haven‟t completed the survey, you can exit the survey and return to it at a later

time, using the same computer.

Thanks again for your interest and involvement in this research!

Regards,

Prue Millear



445

Appendix D: Time 3 Call to action

The email to both groups was identical, apart from the URL link to the survey

Hi everyone,

Thank you for taking part and completing the first and second „Well-being and

Work-Life Balance‟ surveys. We greatly appreciate your time and input, as

preliminary analysis of the data looks really interesting! Your continued participation

in this survey will greatly add to developing our understanding of work-life

integration. All the information remains strictly confidential and no one can be

identified from any of the results. Your email addresses will not go to any other party

and will be deleted at the end of the research.

This will be the third and final survey that I will ask you to complete for the „Well-

being and Work-Life Balance‟ research project. It should only take about 25-30

minutes and each of the surveys is exactly the same to allow comparisons of the

questions across time.

Please follow this link to the survey:

http://www.surveymonkey.com/s.asp?u=995053602965 (university alumni group)

OR

http://www.surveymonkey.com/s.asp?u=148833602983 (hospital group)

Remember to give your email address at the completion of the survey, so that you

can be in a draw to win one of 4 „Accor Hotels Gift Cards‟, valued at $250 each. The

gift vouchers can be redeemed at one of Accor‟s Hotels, such as Novotel, Sofitel,

Grand Mecure, All Seasons or Ibis Hotels across Australia. The winners will be

notified at the end of the project by email. Enjoy!

If you haven‟t completed the survey, you can exit the survey and return to it at a later

time, using the same computer.

Thanks again,

Prue Millear



446

Appendix E: Second and third reminder calls to action

The second call to action took similar forms at each time

At Time 2:

Dear member of staff,

Thank you to everyone who has already completed the survey! Your help is

greatly appreciated. If you haven‟t had a chance yet, you can still be involved.

Follow this link to go to the survey: ….

At Time 3

Hi everyone,

This is another call to take part in the third and final survey for the „Well-being and

Work-Life Balance‟ research project. It should only take about 25-30 minutes and

each of the surveys is exactly the same to allow comparisons of the questions across

time.

Thanks to everyone who has already completed the survey for the third time!

If you haven‟t had a chance yet, please follow this link to go to the Time 3 of the

Well-being and Work-Life Balance survey:

The third and final call to action took similar forms each time

At Time 2

Hi everyone!

Thanks to everyone who has already completed the survey for the second time. Your

help is greatly appreciated and everyone‟s participation is important to understanding

the relationships between ourselves, our work and our well-being.

This is your final chance to do the Time 2 survey! Please follow this link

At Time 3

Hi everyone,

Nearly everyone has completed the third survey and many thanks to those of you

who have! If you haven‟t already done so, please take this final opportunity to follow

this link to the Time 3 of the Well-being and Work-Life Balance survey:

447

Appendix F: Measures used in Study 1 and 2

* Indicates that an item was reverse scored

F.1Measures of P, the Person: Measures of the generative disposition

Dispositional optimism: Life Orientation Test –Revised (LOT-R); Scheier, Carver

& Bridges (1994)

Please indicate how much you agree or disagree with each statement (1, strongly

disagree to 5, strongly agree)

1. In uncertain times, I usually expect the best.

2. If something can go wrong for me, it will *

3. I‟m always optimistic about my future

4. I hardly ever expect things to go my way *

5. I rarely count on good things happening to me *

6. Overall, I expect more good things to happen to me than bad.

Coping Self-Efficacy; Chesney, Chambers, Taylor, Johnson, & Folkman (2003)

When things aren't going well for you, or when you're having problems, how

confident or certain are you that you can do the following (1, I cannot do this at all,

4, I am moderately certain I can do this, to 7, I am certain I can do this)

1. Keep from getting down in the dumps

2. Talk positively to yourself

3. Sort out what can be changed, and what can not be changed

4. Get emotional support from friends and family

5. Find solutions to your most difficult problems

6. Break an upsetting problem down into smaller parts

7. Leave options open when things get stressful

8. Make a plan of action and follow it when confronted with a problem

9. Develop new hobbies or recreations

10 Take your mind off unpleasant thoughts

11. Look for something good in a negative situation

12. Keep from feeling sad

13. See things from the other person's point of view during a heated argument

14. Try other solutions to your problems if your first solutions don‟t work

15. Stop yourself from being upset by unpleasant thoughts

16. Make new friends

17. Get friends to help you with the things you need

18. Do something positive for yourself when you are feeling discouraged

19. Make unpleasant thoughts go away

20. Think about one part of the problem at a time

21. Visualize a pleasant activity or place

22. Keep yourself from feeling lonely

23. Pray or meditate

24. Get emotional support from community organizations or resources

25. Stand your ground and fight for what you want

26. Resist the impulse to act hastily when under pressure

Time Management Scale, Perceived Control of Time subscale, Macan (1994)



1 I feel in control of my time

2 I find it difficult to keep to a schedule because others take me away from my work*

3 I underestimate the time it would take to accomplish tasks *

448

4 I must spend a lot of time on unimportant tasks *

5 I find myself procrastinating on tasks that I don‟t like but have to be done *

Life Role Salience Scales, Amatea et al. (1986), the Reward and Commitment

subscales for Occupational, Parental, and Marital Roles



Occupational Role Reward Value

1 Having work / a career that is interesting and exciting to me is my most important

life goal

2 I expect my job/career to give me more real satisfaction than anything else I do

3 Building a name and reputation for myself through work/career is not one of my

life goals *

4 it is important to me to have a job/career in which I can achieve something of

importance

5 It is important to me to feel successful in my work/career

Occupational Role Commitment

1 I want to work, but I do not want a demanding career *

2 I expect to make as many sacrifices as are necessary in order to advance in my

work/career

3 I value being involved in a career and expect to devote time and effort needed to

develop it

4 I expect to devote significant amount of time to building my career and developing

the skills necessary to advance my career

5 I expect to devote whatever time and energy it takes to move up in my job/career

field

Instructions: The next questions ask about parenting and marriages. Please answer

the questions as they apply to you. If you don’t have children or are not married or

in a relationship at the moment, please tick N/A. Note, children were defined as the

parent‟s „natural, adopted, step, or foster son/s or daughter/s‟.

Parental Role Reward Value

1 Although parenthood requires many sacrifices, the love and enjoyment of children

of one‟s own are worth it all

2 If I chose not to have children, I would regret it

3 It is important to me to feel that I am or will be an effective parent

4 The while idea of having children and raising them is not attractive to me *

5 My life would be empty if I never had children

Parental Role Commitment

1 It is important to me to have some time for myself and my own development,

rather than have children and be responsible for their care *

2 I expect to devote a significant amount of my time and energy to the rearing of

children of my own

3 I expect to be very involved in the day-to-day details of rearing children of my own

4 Becoming involved in the day-to-day details of rearing children involve costs in

other areas of my life which I am unwilling to make *

5 I do not expect to be very involved in childrearing *

Marital Role Reward Value

1 My life would be empty if I never married

2 Having a successful marriage is the most important thing in life to me

3 I expect marriage to give me more real personal satisfaction than anything else in

my life

449

4 Being married to a person I love is more important then anything else

5 I expect the major satisfactions in my life to come from my marriage relationship

Marital Role Commitment

1 I expect to commit whatever time is necessary to make my marriage partner feel

loved, supported and cared for

2 Devoting a significant amount of my time to being with and doing things with a

marriage partner in not something that I expect to do *

3 I expect to put a lot if time into building and maintaining a martial relationship

4 Really involving myself in a marriage relationship involves costs in other areas of

my life that I am unwilling to accept *

5 I expect to work hard to build a good marriage relationship even if it means

limiting my opportunities to pursue other personal goals

Egalitarian Gender Role Attitudes, (Moen 2003)



1 It is usually better for everyone if the man is the main provider and the woman

takes care of the family *

2 It is more important for a wife to help her husband‟s career than have one herself *

3 A preschool child is likely to suffer if his or her mother works *

4 A working mother can have just as good relationship with her children as mother

who does not work

F.2Measures of P, the person: Measures of demand characteristics

Social Skill Scale; Ferris, Witt, & Hochwarter (2001)



1 I find it easy to put myself in the position of others

2 I am keenly aware of how I am perceived by others

3 In social situations, it is always clear to me exactly what to say and do

4 I am particularly good at sensing the motivations and hidden agendas of others

5 I am good at making myself visible with influential people in my organization

6 I am good at reading other people‟s body language

7 I am able to adjust my behaviour and become the type of person dictated by the

situation

Coping Humor Scale; Martin & Lefcourt (1983)



1 I often lose my sense of humour when I am having problems *

2 I have found that my problems have been greatly reduced when I try to find

something funny in them

3 I usually look for something comical to say when I am in tense situations

4 I must admit my life would probably be a lot easier if I had more of a sense of

humour

5 I have often felt that if I am in a situation where I have to either laugh or cry, it‟s

better to laugh

6 I can usually find something to laugh or joke about even in trying situations

7 It has been my experience that humour is often an effective way of coping with

problems

F.3 Measure of C, the Context: Measures of workplace conditions

Job Autonomy; Voydanoff (2004)


450


1 How often do you have a choice in deciding how you do your tasks at work?

2 How often do you have a choice in deciding what tasks you do at work?

3 How often do you have a say in decisions about your work?

4 How often do you have a say in planning your work environment – i.e. how your

workplace is arranged or how things are organized?

Skill Discretion; Schwarz, Pieper & Karasek, (1988)



1 In your job, do you keep learning new things?

2 Does your work require a high level of skill?

3 Does your work require creativity?

4 Is your work repetitious? *

5 Can you develop new skills with your work?

6 Does your job have variety?

Job Social Support; Van Ypern & Hagedoorn (2003)



1 Can you rely upon your immediate supervisor when things get tough at work?

2 If necessary, can you ask your immediate supervisor for help?

3 Can you rely on your co-workers when things get tough at work?

4 If necessary, can you ask your co-workers for help?

Work-Family Culture Scale, Managerial support subscale; Thompson, Beauvais,

& Lyness (1999)



Please indicate how much you agree or disagree with each statement

1. In this organization, employees can easily balance their work and family lives

2. In the event of a conflict, managers understand when employees have to put their

families first

3. In this organization, it is generally ok to talk about one‟s family at work

4. Higher management in this organization encourages supervisors to be sensitive to

employees‟ family and personal needs

5. In general, managers in this organization are quite accommodating of family-

related needs

6. In this organization, it is very hard to leave during the workday to take care of

personal or family matters *

7. This organization encourages employees to set limits on where work stops and

home life begins

8. Middle managers and executives in this organization are sympathetic toward

employees‟ child care responsibilities

9. This organization is supportive of employees who want to switch to less

demanding jobs for family reasons

10. Middle managers and executives in this organization are sympathetic toward

employees with eldercare responsibilities

11. In this organization, employees are encouraged to strike a balance between their

work and family lives

Affective Commitment Scale; Allen & Meyer (1990)



451

1 I would be very happy to spend the rest of my career with this organization

2 I enjoy discussing my organization with people outside it

3 I really feel as if this organization‟s problems are my own

4 I think that I could easily become attached to another organization as I am to this

one *

5 I do not feel like „part of the family‟ at this organization *

6 I do not feel „emotionally attached‟ to this organization *

F.4 Measures of C, the context: Measures of the work-life interface

Work-family Spillover; Grzywacz & Marks (2000)



1 Your job reduces the effort you can give to activities at home

2 Stress at work makes you irritable at home

3 Your job makes you feel too tired to do the things that need attention at home

4 Job worries or problems distract you when you are at home

5 The things you do at work help you deal with personal and practical issues at home

6 The things you do at work make you a more interesting person at home

7 The skills you use on your job are useful for the things you have to do at home

8 Having a good day on your job makes you a better companion when you get home

9 Responsibilities at home reduce the effort you can devote to your job

10 Personal or family worries and problems distract you when you are at work

11 Activities and chores at home prevent you from getting the amount of sleep you

need to do your job well

12 Stress at home makes you irritable at work

13 Talking with someone at home helps you deal with problems at work

14 The love and respect you get at home makes you confident about yourself at work

15 Your home life helps you relax and feel ready for the day‟s work

16 Providing for what is needed at home makes you work harder at your job

Negative Work-Family Spillover: items 1, 2, 3, 4; Positive Work-Family Spillover:

items 5, 6, 7, 8; Negative Family-Work Spillover: items 9, 10, 11, 12; Positive

Family-Work Spillover: items 13, 14, 15, 16

F.5 Measures of D, the developmental outcomes

F.5.1 Well-Being

Satisfaction with Life Scale, Diener (SWLS), Emmons, Larsen & Griffin (1986)



1. In most ways, my life is close to ideal

2. The conditions of my life are excellent.

3. I am satisfied with my life

4. So far I have got the important things I want in life

5. If I could live my life again, I would change almost nothing

Ryff’s Psychological Well-Being Scale, Ryff (1989) Please indicate how much you agree or disagree with each statement (1, strongly


1. I have confidence in my opinions, even if they are different form the way that

most people think

2. I tend to be influenced by people with strong opinions *

3 I judge myself by what I think is important, not by the values of what others think

is important.

452

4. I am good at managing the responsibilities of my daily life

5. The demands of everyday life often get me down *

6. In general, I am in charge of the situation in which I live

7. People would describe me as a giving person, willing to share my time with others

8. Maintaining close relationships has been difficult and frustrating for me *

9. I have not experienced many warm and trusting relationships with others. *

10. When I look at the story of my life, I am pleased with how things have turned out

so far

11. In many ways, I feel disappointed about my achievements in life *

12. I like most parts of my personality.

13. Some people wander aimlessly through life, but I am not one of them

14. I live life one day at a time and don‟t really think about the future *

15. I sometimes feel as if I‟ve done all there is to do in life *

16. For me, life has been a continual process of learning, changing, and growth

17. I think it is important to have new experiences that challenge how you think

about yourself and the world.

18. I gave up making personal improvements or changes in my life a long time ago *

F.5.2 Mental Illness

Depression, Anxiety, & Stress Scale (DASS-21); Lovibond, & Lovibond (1995)

Please read each statement and circle a number 0, 1, 2 or 3 which indicates how

much the statement applied to you over the past week. There are no right or wrong

answers. Do not spend too much time on any statement (0, Didn‟t apply to me at all;

2, Applied to me to some degree, or some of the time; 4, Applied to me to a

considerable degree, or a good part of time; 6, Applied to me very much, or most of

the time)

1 I found it hard to wind down

2 I was aware of dryness of my mouth

3 I couldn't seem to experience any positive feeling at all

4 I experienced breathing difficulty (eg, excessively rapid breathing, breathlessness

in the absence of physical exertion)

5 I found it difficult to work up the initiative to do things

6 I tended to over-react to situations

7 I experienced trembling (eg, in the hands)

8 I felt that I was using a lot of nervous energy

9 I was worried about situations in which I might panic and make a fool of myself

10 I felt that I had nothing to look forward to

11 I found myself getting agitated

12 I found it difficult to relax

13 I felt down-hearted and blue

14 I was intolerant of anything that kept me from getting on with what I was doing

15 I felt I was close to panic

16 I was unable to become enthusiastic about anything

17 I felt I wasn't worth much as a person

18 I felt that I was rather touchy

19 I was aware of the action of my heart in the absence of physical exertion (eg,

sense of heart rate increase, heart missing a beat)

20 I felt scared without any good reason

21 I felt that life was meaningless

(Depression: items 3, 5, 10, 13, 16, 17, 21; Anxiety: items 2, 4, 7, 9, 15, 19, 20;

Stress: items 1, 6, 8, 11, 12, 14, 18)

453

F.5.3 Burnout

Burnout, Maslach Burnout Inventory – General; Maslach, Jackson, & Leiter (1996)



1 I feel emotionally drained from my work

2 I feel used up at the end of the workday

3 I feel tired when I get up in the morning and have to face another day on the job

4 Working all day is really a strain for me

5 I can effectively solve the problems that arise in my work

6 I feel burnt out by my work

7 I feel I am making an effective contribution to what this organization does

8 I have become less interested in my work since I started this job

9 I have become less enthusiastic about my work

10 In my opinion, I am good at my job

11 I feel exhilarated when I accomplish something at work

12 I have accomplished many worthwhile things in this job

13 I just want to do my job and not be bothered

14 I have become more and more cynical about whether my work contributes to

anything

15 I doubt the significance of my work

16 At my work, I feel confident that I am effective at getting things done

(Exhaustion: items 1, 2, 3, 4, 6; Cynicism: items 8, 9, 13, 14, 15; Professional

Efficacy: items 5, 7, 10, 11, 16)

F.5.4 Work engagement

Utrecht Work Engagement Scale; Schaufeli, Salanova, Gonzalez-Roma, & Bakker,

(2002)



1 When I get up in the morning, I feel like going to work

2 At my work, I feel bursting with energy

3 At my work I always persevere, even when things do not go well

4 I can continue working for long periods at a time

5 At my job, I am very resilient, mentally

6 at my job, I feel strong and vigorous

7 To me, my job is challenging

8 My job inspires me

9 I am enthusiastic about my job

10 I am proud of the work that I do

11 I fond the work that I do full of meaning and hope

12 When I am working, I forget everything else around me

13 Time flies when I am working

14 I get carried away when I am working

15 It is difficult to detach myself from my job

16 I feel happy when I am working intensely

(Vigour: items 1, 2, 3, 4, 5, 6; Dedication: items 7, 8, 9, 10, 11; Absorption: items 12,

13, 14, 15, 16)

454

Appendix G: Simple slopes of the moderated regression analyses

Figure G.1. Simple slopes for the moderating influence of negative work-to-family spillover

(on left) and negative family-to-work spillover (on right) on the relationship between

dispositional optimism and depression. „Low‟ is -1SD and „High‟ is +1 SD from the mean.


on the relationship between coping self-efficacy and depression (on the left) and anxiety (on

the right). „Low‟ is -1SD and „High‟ is +1 SD from the mean.

455

Figure G.3. Simple slopes for the moderating influence of negative family-to-work spillover

on the relationship between dispositional optimism and anxiety. „Low‟ is -1SD and „High‟ is

+1 SD from the mean.

Figure G.4. Simple slopes for the moderating influence of negative family-to-work spillover

on the relationship between job autonomy and emotional exhaustion (on the left) and job

autonomy and cynicism (on the right). „Low‟ is -1SD and „High‟ is +1 SD from the mean.

456


on the relationship between affective commitment and professional efficacy. „Low‟ is -1SD

and „High‟ is +1 SD from the mean.


on the relationships between skill discretion and work absorption (on the right). „Low‟ is -

1SD and „High‟ is +1 SD from the mean

457

positive workplace factors individual factors

overall well-being

Dispositional optimism

e1

1

Coping self-efficacy

e2

Skill discretion

e7

life satisfaction

e9

e14

Psychological

well-being

e19

work well-being

Work dedication

e21

1

e24

Work absorption

e25

1

1 1

1

1

1

Job autonomy

e81

1

1

1

1

1

Appendix H: Results of the Time 1structural equation modelling

H.1 SEM with positive outcomes of overall well-being and work well-being

From the initial hypothesized model of the three exogenous variables and the two

endogenous variables, the modification indices and statistical significance of paths

were used to respecify the model to find the best fit for the data. In this way, negative

spillover was removed from the model, along with work vigour, work satisfaction

and affective commitment. The final structural model has the following fit and

parsimony indices, X2/df = 1.758, CFI = .992, RMSEA = .041 (90% CI = .011-.066).

As shown in Figure H.1, the final, best fitting model found that individual factors and

positive workplace factors were significantly correlated (r = .46, p <.001), and

individual factors were responsible for overall well-being (β = .95, p < .001), and

positive workplace factors were responsible for work well-being (β = .92, p < .001).

Figure H.1. Early SEM model exploring the positive, well-being outcomes

458

From the squared multiple correlations of the endogenous variables, 90.3% of the

variance of Overall Well-Being and 84.4% of the variance of Work Well-Being were

explained by the final model. The beta weights for the Time 1 positive outcomes

model are shown in Table H.1. The squared multiple correlations for the indicator

variables, as the square of the standardized regression weights (β) between each

latent and its indicator variables, represent the reliability of the indicator variables in

the model. Acceptable squared multiple correlations > .30 and equate to β > .548

(Holmes-Smith et al., 2006). In the model of positive outcomes, as all the beta

weights were significant and were greater than .61, except for job autonomy, β =

.51, the squared multiple correlations show that the indicators are suitable

representations of the latent variables. The correlation between the measurement

Table H.1

Standardized regression weights (β) and squared multiple correlations (SMC) in the

positive outcomes model

From latent factors to observed indicator variables

Latent Factor Observed indicator variable β SMC

Individual Factors dispositional optimism .691*** .447

Individual Factors coping self-efficacy .746*** .557

Positive Workplace Factors skill discretion .816*** .661

Positive Workplace Factors job autonomy .506*** .256

Overall Well-Being life satisfaction .748*** .559

Overall Well-Being psychological well-being .869*** .755

Work Well-Being work dedication .977*** .954

Work Well-Being work absorption .616*** .379

Standardized regression weights (β) between the latent variables

From To β

Individual Factors Overall Well-Being .950***

Positive Workplace Factors Work Well-Being .919***

* p < .05, ** p <.01, *** p < .001

459

error for Job Autonomy and Individual Factors shows that there is a relationship

between job autonomy and Individual Factors (r = .18, p < .001) that is over and

above the relationships that are explained through the latent variable, Positive

Workplace Factors. Whilst this relationship is not as strong as the direct paths

between the latent factors, the path suggests that having autonomy or control about

the tasks of one‟s job may add to one‟s general perceptions of personal effectiveness.

H.2 Early SEM of the negative outcomes of mental illness and burnout

To complement the positive outcomes of the first model, the second, early

SEM explored that negative outcomes of mental illness and burnout, although two

models were required to reasonably explain the negative outcomes. In a similar

manner to the positive outcomes model, the model of negative outcomes were

hypothesized to start with the same three exogenous latent variables (Individual

Factors, Positive Workplace Factors, and Negative Spillover) and with the two

endogenous latent variables, Mental Illness, as the indicator variables of depression,

anxiety and stress, and Burnout, as the indicator variables, exhaustion, cynicism, and

professional efficacy. The exogenous variables were drawn as correlated and with

each having a causal effect on the two endogenous variables, which were also

considered to be correlated to each other. Using the Modification Indices and the

statistical significance of the pathways, the model was respecified to increase the fit

of the model. The result of the first part of the SEM of negative outcomes, that did

not include stress and exhaustion, is shown in Figure H.2 which has acceptable fit,

X2/df = 2.309, CFI = .970, RMSEA = .053 (90% CI = .038 - .069).

Individual Factors had a significant influence on Mental Illness (β = -.48, p <

.001), Negative Spillover had a positive influence on both Mental Illness (β = .48, p

< .001) and Burnout (β = .32, p < .001), while Positive Workplace Factors had a

460

positive workplace factors

Skill discretion

e1

1

1 Job autonomy

e21

Affective commitment

e31

individual factors

Dispositional

Optimisme41

1

Coping

self-efficacye5

1

mental illness Anxiety e71 1

Depression e81

burnout

Professional

efficacy

e9

1

Cynicism

e10

1

negative spillover

Negative

Work-family

spillover

e12

1

1

Negative

Family-work

spillover

e131

e14

1

e15

1

1

Figure H.2. Early SEM for the negative outcomes of Mental Illness and Burnout,

Part A

Table H.2

Correlations in Part A of the negative outcomes Time 1 SEM

r p

Individual Factors ↔ Positive Workplace Factors .417 < .001

Individual Factors ↔ Negative Spillover -.496 < .001

Negative Spillover ↔ Positive Workplace Factors -.353 < .001

Negative Spillover ↔ professional efficacy .276 < .001

Depression ↔ cynicism .290 .002

Job autonomy ↔ coping self-efficacy .151 .016

Negative work-to-family spillover ↔ skill discretion .235 < .001

* p < .05, ** p <.01, *** p < .001

461

larger negative influence on Burnout (β = -.82, p < .001). There is a positive

correlation between Individual Factors and Positive Workplace Factors (r = .42, p <

.001) and negative correlations between Negative Spillover and Individual Factors (r

= -.50, p < .001) and Positive Workplace Factors (r = -.35, p < .001). In addition to

the pathways through the latent factors, job autonomy and coping self-efficacy are

positively correlated (r = .15, p = .016), as are cynicism and depression (r = .29, p

<.001). Interestingly, skill discretion and negative work-family spillover are also

positively correlated (r = .23, p < .001) and professional efficacy is positively

correlated with Negative Spillover (r = .28, p < .001). From the squared multiple

correlations of the endogenous variables, 95.6% of the variance of Burnout and

68.3% of the variance of Mental Illness are explained by the model. As noted in the

positive outcome model, the squared multiple correlations, as the square of the

standardized regression weights, also represent the reliability of the indicator

variables for the appropriate latent variables, and values greater than .30 are

acceptable (Holmes-Smith et al., 2006). All the beta weights for the first part of the

model of the negative outcomes, as shown in the Table H.3 are greater than .60 and

highly significant (p < .001), the squared multiple correlations for all indicator

variables are greater than .35 and therefore acceptable. The squared multiple

correlations were also substantial, with 95.6% of the variance of Burnout and 68.3%

of the variance of Mental Illness being explained by the model.

Considering the importance of stress and exhaustion in the work-life

literature, these outcomes were considered in a second model of negative outcomes.

Using the same initial conditions of three exogenous variables and constructing a

latent variable with stress and exhaustion as indicator variables, the same model

respecification process lead to a final fitted model, with stress and exhaustion

462

Table H.3

Standardized regression weights (β) and squared multiple correlations (SMC) for

indicator variables in Part A of the negative outcomes model







Positive Workplace Factors affective commitment .610*** .372

Negative Spillover negative work-to-family .694*** .481

Negative Spillover negative family-to-work .613*** .376

Burnout‡ cynicism -.834*** .696

Burnout‡ professional efficacy .639*** .408

†

Mental Illness depression .865*** .748

Mental Illness anxiety .642*** .412

Standardized regression weights (β) for latent variables

From To β

Individual Factors Mental Illness -.476***

Positive Workplace Factors Burnout‡ .816***

Negative Spillover Burnout‡ -.323***

Negative Spillover Mental Illness .479***

* p < .05, ** p <.01, *** p < .001

Note: †Calculated as .315 in AMOS;

‡ As an exception to other variables, low scores indicate high

levels of Burnout, high scores indicate absence of Burnout, such that increasing Negative Spillover

leads to decreasing scores for Burnout scale but greater symptoms of Burnout

463

Stress

e4

1Exhaustion

e5

1

individual factors


e6

negative spillover

Negative

work-family

spillover

e9

Dispositional

optimsm

e10

11

Negative

family-work

spillover

e11

1

1

1

1

as separate outcomes, as shown in Figure H.3 as „Negative outcomes, Part B‟. This

second model had acceptable fit indices, X2/df = 1.616, CFI = .997, RMSEA = .037

(90% CI = .000 - .089).

Negative Spillover lead to both exhaustion (β = .784, p < .001) and stress (β =

.552, p < .001) whilst Individual Factors reduced stress (β = -.224, p < .001) alone.

Again, Negative Spillover and Individual Factors were negatively correlated (r = -

.360, p < .001) as in the Part A model. In addition to the pathways through the latent

factors, dispositional optimism and exhaustion are negatively correlated (r = -.210, p

< .001) and negative work-to-family spillover is negatively correlated with

Individual Factors (r = -.148, p =.005), and positively correlated with stress (r = .107,

p = .043). From the squared multiple correlations, 44.4% of the variance of stress and

61.5% of the variance of exhaustion is explained by the model. As shown in the

Appendix, the standardized regression weights, and therefore the squared multiple

correlations, for the indicator variables of Negative Spillover and Individual Factors

Figure H.3. Early SEM for the negative outcomes for Mental Illness and Burnout,

Part B

464

are acceptable. Although negative family-to work spillover is below .30 (β = .484, p

< .001), it remains a significant indicator variable.

Table H.4

Standardized regression weights and squared multiple correlations in Part B of the

negative outcomes model








From To β

Individual Factors Stress -.224***

Negative Spillover Stress .552***

Negative Spillover Exhaustion .784***

* p < .05, ** p <.01, *** p < .001

Correlations r p


Individual Factors ↔ negative family-to-work spillover -.148 .005

Negative work-to-family spillover ↔ stress .107 .043

Dispositional optimism ↔ exhaustion -.210 < .001

465

H.3 Time 1 SEM combining the positive and negative outcomes

With the early structural models establishing that the well-being and mental

health outcomes could be modelled in this data, the next step was to combine the

three models to find if these would form a tenable overall model that included all

outcomes. As the integration of the positive and negative outcomes came at the end

of the modeling process, it was able to take into account the exploration of work

engagement and burnout together, which is reported and discussed in detail in the

next section on CFAs. Interestingly, although Burnout, in particular, and Work

Engagement have been widely used separately in research, the results of the CFA,

Burnout and Work Engagement are better represented as one factor, which will be

called Work Engagement in this thesis. From the CFA, the new single-factor has

only four indicator variables of work dedication, work absorption, professional

efficacy, and cynicism. As with the separate models of positive and negative

outcomes, this integrated model has the three exogenous latent variables of

Individual Factors (as dispositional optimism and coping self-efficacy), Positive

Workplace Factors (as skill discretion, job autonomy, and affective commitment),

and Negative Spillover (as negative work-to-family spillover and negative family-to-

work spillover) which are correlated with other and with each having a causal

influence on the three endogenous latent variables of Mental Illness (as depression,

anxiety and stress), Work Engagement (as work dedication, work absorption,

professional efficacy and cynicism) and Overall Well-Being (as life satisfaction and

psychological well-being). The Modification Indices and statistical significance of

the pathways indicated that cynicism should be removed from the model. The final

model in shown in Figure H.4, and had acceptable fit, X2/df = 2.473, CFI = .965,

RMSEA = .057 (90% CI = .046 -.068). From the model it can be seen that Individual

466

positive workplace

factors

job autonomy

e21

skill discretion

e3

1

1

individual

factors

dispositional optimism

e4

1

1

coping self-efficacy

e51

negative

spillover

Neg Work-Family spillover

e61

Neg Family-Work spillover

e7

overall

well-being

life satisfaction

e8

1

1

psychological well-being

e9

work engagement

work dedication

e11

work absorption

e12

professional efficacy

anxiety depression

stress

e15

e17

e18

e19e20

e211

mental illness

1

e22

1

1

1

1

1

1

1

1

1

1 1

1

Factors lead to greater Overall Well-Being (β = .939, p < .001) and mitigated Mental

Illness (β = -.440, p = .001), Positive Workplace Factors lead to greater Work

Engagement (β = .617, p < .001), yet also added to Mental Illness (β = .176, p=

.017), and Negative Spillover lead to Mental Illness (β = .564, p < .001) and reduced

Work Engagement (β = -.172, p = .001). From the squared multiple correlations,

88.1% of the variance of Overall Well-being, 50.1% of Work Engagement and

65.6% of Mental Illness is explained by the model. As with the models with the

positive and negative outcomes separately, Individual Factors and Positive

Workplace Factors are positively correlated (r = . 547, p < .001) and Negative

Spillover is negatively correlated to both Individual Factors (r = -.555, p < .001) and

Positive Workplace Factors (r = -.416, p < .001). In

Figure H.4. Combination of early SEMs to integrate positive and negative outcomes

467

addition to these relationships, negative work-to-family spillover is directly and

positively correlated with stress (r = .374, p < .001), stress and anxiety are directly

correlated (r = .339, p < .001), skill discretion and Work Engagement are positively

correlated (r = .547, p < .001), as are professional efficacy and Individual Factors

Table H.5

Standardized regression weights (β) and squared multiple correlations (SMC) in the

combined outcomes model









Overall Well-Being life satisfaction .746*** .557

Overall Well-Being psychological well-being .867*** .752

Work engagement work dedication .985*** .969

Work engagement work absorption .611*** .373

Work engagement professional efficacy .525*** .275

Mental Illness depression .909*** .826

Mental Illness anxiety .616*** .380

Mental Illness stress .731*** .535 * p < .05, ** p <.01, *** p < .001

468

(r = .189, p < .001). The standardized regression weights for all the indicator

variables are highly significant (p < .001) and greater than .60, with only professional

efficacy being less, at β = .53, which indicates that the squared multiple correlations

for the indicator variables form a reliable basis for the model (Holmes-Smith et al.,

2006).

Table H.6


From To β p

Individual Factors Overall Well-Being .939 <.001

Individual Factors Mental Illness -.443 < .001

Positive Workplace Factors Work engagement .623 < .001

Positive Workplace Factors Mental Illness .180 .017

Negative Spillover Work engagement -.166 .001

Negative Spillover Mental Illness .566 < .001

* p < .05, ** p <.01, *** p < .001

Correlations for the positive and negative models r p

Individual Factors ↔ Positive Workplace Factors .547 < .001


Positive Workplace Factors ↔ Negative Spillover -.416 < .001

Individual Factors ↔ professional efficacy .189 < .001

Work engagement ↔ skill discretion .547 < .001

Stress ↔ negative work-to-family spillover .374 < .001

Stress ↔ anxiety .339 < .001

469

individual

factors

Dispositional optimsm e111

Coping self-efficacy e21

positive

workplace factors

Skill discretion e311

Job autonomy e41

work

well-being

Work dedication e611

Work absorption e71

overall

well-being

Psychological well-being e81 1

Life satisfaction e91

Appendix I: Confirmatory Factor Analyses for the longitudinal models

I.1 CFA for the Well-Being model

Figure I.1. CFA for Well-Being model

Table I.1

Standardized regression weights (β) and squared multiple correlations (SMC) for the

Well-Being model CFA

Latent variable Observed variable β SMC

Individual Factors (IF) dispositional optimism .692*** .479

Individual Factors (IF) coping self-efficacy .746*** .557

Positive workplace factors (PWF) skill discretion .817*** .667

Positive workplace factors (PWF) job autonomy .557*** .311

Work Well-Being (WWB) work dedication .970*** .941

Work Well-Being (WWB) work absorption .627*** .393

Overall Well-Being (OWB) life satisfaction .866*** .544

Overall Well-Being (OWB) psychological well-being .737*** .750

* p < .05, ** p < .01, *** p < .001

470

Table I.2

Correlations between latent factors and between observed variables in the Well-

Being CFA Latent factors IFwb PWFwb WWBwb OWBwb

IFwb 1 .476*** .465*** .929***

PWFwb 1 .917*** .526***

WWBwb 1 .461***

OWBwb 1

Correlations between observed variables r p

Job autonomy ↔ Life satisfaction .177 .039

* p < .05, ** p < .01, *** p < .001

471

individual factorsDispositional optimism e11

1


positive

workplace factors


Job autonomy e41

Affective commitment e51

negative

spillover

Neg work-family spillover e611

Neg family-work spillover e71

burnout

Exhaustion e811

Cynicism e91

Professional efficacy e101

mental illness

Stress e1111

Anxiety e121

Depression e131

I.2 CFA for the Mental Distress model

Figure I.2. CFA for Mental Distress model

Table I.3

Standardized regression weights (β) and squared multiple correlations (SMC) for the

Mental Distress model


Individual Factors (IF) Dispositional optimism .725*** .526


Positive Workplace Factors (PWF) skill discretion .614*** .377

Positive Workplace Factors (PWF) job autonomy .632*** .399

Positive Workplace Factors (PWF) affective commitment .674*** .454

Negative Spillover (NSP) negative work-family spillover .659*** .434

Negative Spillover (NSP) negative family-work spillover .671*** .451

Burnout exhaustion .742*** .550

Burnout cynicism .859*** .738

Burnout professional efficacy -.583*** .340

Mental Illness (MI) depression .900*** .810

Mental Illness (MI) anxiety .647*** .419

Mental Illness (MI) stress .767*** .588

* p < .05, ** p < .01, *** p < .001

472

Table I.4

Correlations between latent factors and between observed variables in the Mental

Distress CFA

Latent variables IF PWF NSP Burnout MI

IF 1 .424*** -.490*** -.617*** -.712***

PWF 1 -.603*** -.916*** -.369***

NSP 1 .816*** .688***

Burnout 1 .631***

Mental Illness 1


Dispositional optimism ↔ stress .113 .013

Coping self-efficacy ↔ cynicism .150 .026

Skill discretion ↔ job autonomy .129 .060

Skill discretion↔ exhaustion .234 <.001

Negative work-family spillover ↔ exhaustion .460 < .001

Negative work-family spillover ↔ stress .302 < .001

Exhaustion ↔ professional efficacy .100 .022

Exhaustion ↔ stress .145 .017

Professional efficacy ↔ depression .197 .007

Anxiety to stress .348 < .001

* p < .05, ** p < .01, *** p < .001

473

Individual Factors

Positive

Workplace Factors

Negative

Spillover

Overall

Well-Being

Mental Illness

Dispositional optimism e111



Job autonomy e41

Affective commmitment e51

Neg Work-Family spillovere611

Neg Family-Work spillovere71

Psychological well-being e81 1


Stress e1011

Anxiety e111

Depression e121

I.3 CFA for the Well-Being – Mental Health model

Started with CFA, developing on from Well-being and Mental Health Problems

models

Figure I.3. The CFA for the Well-Being Mental Health model

Table I.5

Correlations between latent factors and observed variables in the CFA

Latent factors IF PWF NSP OWB MI

IF 1 .463*** -.506*** .922*** -.694***

PWF 1 -.463*** .538*** -.306***

NSP 1 -.529*** .737***

OWB 1 -.602***

MI 1


Dispositional optimism ↔ stress .119* .015

Skill discretion ↔ negative work-family spillover .244 < .001

Skill discretion ↔ stress .190 < .001


Anxiety to stress .328 < .001

* p < .05, ** p < .01, *** p < .001

474

Table I.6

Standardized regression weights (β) and squared multiple correlations for the Well-

Being- Mental Health model


Individual Factors (IF) Dispositional optimism .694*** .481







Overall Well-Being (OWB) Psychological well-being .862*** .743





* p < .05, ** p < .01, *** p < .001

475

Individual factors

Positive Workplace

Factors

Negative Spillover

Work Engagment

Burnout

Dispostional optimism e11 1


Job autonomy e311

Skill dsicretion e41




Work vigour e811

Work dedication e91

Work absorption e101

Exhaustion e1111

Cynicism e121

Professional Efficacy e131

I.4 CFA for Burnout and Work Engagement, which becomes Work Engagement

Figure I.4. First, unsuccessful CFA for Burnout – Work Engagement

Fit indices for the first unsuccessful CFA, shown in Figure L.4, X2/df = 2.360, CFI =

.985, RMSEA = .054 (90% CI = .038 - .071).

476

Work Engagement

Work vigour e11

1

Work dedication e21

Work absorption e31

Exhaustion e41

Cynicism e51


Work Engagement

Work vigour e11

1

Work dedication e21

Work absorption e31

Cynicism e41


Examining Burnout and Work Engagement

First, a one-factor solution was proposed with all six observed variables,

shown in Figure I.5. With six components, the fit indices were X2 (5) = 17.2, X

2/df =

3.4340, CFI = .990, RMSEA = .073 (90%CI = .037-.112). However, exhaustion was

removed from the model, as the squared multiple correlation was only .13,

considerably less than the acceptable level of .30. The final model, with five

observed variables was well-fitting, X2/df = .933, CFI = 1.000, RMSEA = .000 (90%

CI = .000 - .077) and is shown in Figure I.6. Second, a two-factor solution was

proposed with the latent factors, Burnout and Work Engagement, measured by the

observed variables for each scale. With two factors, as shown in Figure L.7

Figure I.5. Burnout and Work Engagement as one-factor, all observed variables

Figure I.6. The final model for Burnout and Work Engagement, as a one-factor

solution, with five components

477

core burnoutExhaustion e11

1

Cynicism e21

engagement

Work vigour e31

1

Work dedication e41

Work absorption e51


Engagement

Burnout

Work vigour e111

Work dedication e21

Exhaustion e311

Cynicism e41

Work absorption e51


, fit was poor (X2/df = 17.656, CFI = .933, RMSEA = .190, 90% CI = .157- .226))

and could not be improved without making the solution inadmissible, such that the

correlations between burnout and work engagement became greater than 1 (r = -

1.003). Another two-factor solution, shown in Figure I.8, was proposed by Schaufeli

et al., 2002, with burnout as the core dimensions of exhaustion and cynicism, and

work engagement as the remaining four observed variable. However, the fit indices

indicated that this was not an admissible solution as the covariance matrix is not

positive definite.

Figure I.7. Work Engagement and Burnout as two factors based on the separate

scales

Figure I.8. „Core‟ of Burnout and rest of indicators as part of Work Engagement

478

Individual Factors

Positive Workplace

Factors

Negative Spillover

Work Engagement




1

Job autonomy e41



Work dedication e91

1




Cynicism e141

Figure I.9. Final CFA for Work Engagement model, using one-factor Work

engagement

Table 1.7

Standardized regression weights (β) and squared multiple correlations (SMC) for

Work Engagement model









Work engagement (WE) work dedication .889*** .790

Work engagement (WE) work absorption .642*** .412

Work engagement (WE) professional efficacy .588*** .346

Work engagement (WE) cynicism -.788*** .620

* p < .05, ** p < .01, *** p < .001

479

Table I.8

Correlations between latent factors and between observed variables in the Work

Engagement CFA

Latent factors IFwa PWFwa NSPwa WEwa

IFwa 1 .458*** -.443*** .480***

PWFwa 1 -.400*** .912***

NSPwa 1 -.421***

WEwa 1


Skill discretion ↔ work dedication .519 < .001

Skill discretion ↔ negative work-family spillover .150 .001

Negative work-family spillover ↔ work absorption .257 < .001

Cynicism↔ affective commitment -.242 < .001

Cynicism ↔ absorption .378 < .001

Cynicism ↔ negative work-to-family spillover .327 < .001

* p < .05, ** p < .01, *** p < .001

480

Individual Factors

Positive Workplace

Factors

Negative Spillover

Overall Well-Being

Mental Illness

Work Engagement




Job autonomy e41


Neg Work-Family spillover e611

Neg Family-Work spillover e71

Psychological well-being e811


Depression e1011

Anxiety e111

Stress e121

Work dedication e131

1



Exhaustion e161

I.5 CFA for the Integrated model

Figure I.10. CFA for the Integrated model

X2/df = 2.784, CFI = .954, RMSEA = .062 (90%CI = .053-..072)

481

Table I.9

Standardized regression weights (β) and squared multiple correlations(SMC) for the

Integrated model

Latent variable Observed variable β MC








Negative Spillover (NSP) exhaustion .834*** .696

Overall Well-Being (OWB) psychological well-being .858*** .737





Work Engagement (WE) work dedication .982*** .963

Work Engagement (WE) work absorption .607*** .368

Work Engagement (WE) professional efficacy .532*** .283

* p < .05, ** p < .01, *** p < .001

482

Table I.10

Correlations between the latent factors and between the observed variables in the

CFA for the Integrated model

Latent factors IF PWF NSP OWB MI WE

IF 1 .425*** -.471*** .922*** -.685*** .429***

PWF 1 -.300*** .495*** -.262*** .938***

NSP 1 -.506*** .663*** -.366***

OWB 1 -.608*** .421***

MI 1 -.307***

WE 1

Observed variables r p

Job autonomy ↔ professional efficacy .193 < .001

Affective commitment ↔ exhaustion -.321 < .001


Negative work-family spillover ↔ work absorption .154 .002

Psychological well-being ↔ professional efficacy .227 < .001

Anxiety ↔ stress .325 < .001

* p < .05, ** p < .01, *** p < .001

483

Appendix J: Results of the longitudinal models

Means, standard deviations and correlations between the composite variables

Table J.1

Means, SD, and range of the composite variables in the Well-Being longitudinal

model

N Min Max Mean SD

IFwb1 198 10.29 25.61 19.55 3.00

IFwb2 198 10.93 26.04 19.49 3.17

IFwb3 198 12.33 25.95 19.69 3.11

PWFwb1 198 8.75 25.43 18.71 3.54

PWFwb2 198 8.84 25.54 18.53 3.61

PWFwb3 198 8.82 25.47 18.44 3.61

OWBwb1 198 29.85 64.92 49.98 7.21

OWBwb2 198 27.88 65.81 49.82 7.60

OWBwb3 198 32.12 65.61 50.17 7.43

WWBwb1 198 6.21 26.03 18.97 4.40

WWBwb2 198 6.69 26.17 18.87 4.49

WWBwb3 198 6.24 26.15 18.67 4.44

Note. IF: Individual Factors, PWF: Positive Workplace Factors, OWB: overall Well-Being, WWB:

Work Well-Being; „wb‟ composite variables in the Well-Being model; 1, 2, 3 = times 1, 2, 3

respectively

484

Table J.2

Means, SD, and range of the composite variables in the Mental Distress longitudinal

model

N Min Max Mean SD

IFmi1 198 0.18 16.20 10.08 3.05

IFmi2 198 1.72 16.62 9.99 3.05

IFmi3 198 0.96 16.63 10.28 3.16

PWFmi1 198 -0.41 11.72 5.79 2.42

PWFmi2 198 -0.77 11.76 5.60 2.52

PWFmi3 198 0.12 11.59 5.62 2.58

NSPmi1 198 0.18 8.32 3.82 1.61

NSPmi2 198 0.48 8.26 3.77 1.52

NSPmi3 198 0.59 8.39 3.68 1.63

MIllness1 198 -5.63 24.24 2.51 5.49

MIllness2 198 -5.63 21.04 2.32 4.91

MIllness3 198 -5.90 20.97 2.07 5.61

Burnout1 198 -10.87 5.32 -3.69 3.37

Burnout2 198 -11.68 5.66 -3.52 3.37

Burnout3 198 -11.46 5.17 -3.65 3.56

Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover; „mi‟

composite variables in the Mental Distress model; 1, 2, 3 = times 1, 2, 3 respectively

485

Table J.3

Means, SD, and range of the composite variables in the Well-Being Mental Health

longitudinal model

N Min Max Mean SD

IFwbmh1 198 6.92 19.90 14.58 2.58

IFwbmh2 198 6.03 20.09 14.57 2.65

IFwbmh3 198 7.65 20.03 14.73 2.67

PWFwbmh1 198 8.75 20.35 13.91 2.43

PWFwbmh2 198 7.11 20.26 13.81 2.51

PWFwbmh3 198 7.15 20.31 13.88 2.58

NSpwbmh1 198 0.67 9.13 4.07 1.53

NSPwbmh2 198 1.06 7.99 3.97 1.42

NSPwbmh3 198 0.69 8.41 3.89 1.53

OWBwbmh1 198 26.73 61.64 46.19 7.18

OWBwbmh2 198 23.62 62.50 46.10 7.51

OWBwbmh3 198 28.72 62.09 46.47 7.41

MIwbmh1 198 -5.84 23.07 1.86 5.29

MIwbmh2 198 -5.58 18.34 1.62 4.74

MIwbmh3 198 -6.00 19.23 1.39 5.38

Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, OWB:

overall Well-Being, MI: Mental Illness; „wbmh‟ composite variables in the Well-Being-Mental Health

model; 1, 2, 3 = times 1, 2, 3 respectively

486

Table J.4

Means, SD, and ranges of composite variables in the Work Engagement longitudinal

model

N Min Max Mean SD

IFwa1 198 5.17 18.76 13.18 2.85

IFwa2 198 5.04 19.66 13.12 2.96

IFwa3 198 5.69 19.75 13.43 2.98

PWFwa1 198 4.90 18.40 11.98 2.83

PWFwa2 198 4.31 18.67 11.89 2.91

PWFwa3 198 3.52 18.67 11.92 2.97

NSPwa1 198 -1.45 4.82 1.33 1.29

NSPwa2 198 -1.58 5.43 1.24 1.30

NSPwa3 198 -1.76 4.88 1.16 1.28

WEwa1 198 2.17 20.14 12.29 3.94

WEwa2 198 1.70 20.26 12.21 4.02

WEwa3 198 1.01 20.43 12.21 3.98

Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, WE: Work

Engagement; „wa‟ composite variables in the Work Engagement model; 1, 2, 3 = times 1, 2, 3

respectively

487

Table J.5

Means, SD and ranges of the composite variables in the Integrated longitudinal

model

N Min Max Mean SD

IFcm1 198 7.09 22.57 16.45 3.06

IFcm2 198 7.10 22.72 16.43 3.14

IFcm3 198 8.68 22.73 16.62 3.13

PWFcm1 198 10.26 25.95 19.50 3.49

PWFcm2 198 10.49 26.16 19.38 3.58

PWFcm3 198 9.73 26.03 19.22 3.55

NSPcm1 198 1.33 12.20 6.25 2.10

NSPcm2 198 1.55 12.01 6.24 2.03

NSPcm3 198 1.41 12.28 6.08 2.08

OWBcm1 198 23.43 58.71 42.27 7.11

OWBcm2 198 20.05 58.27 42.17 7.39

OWBcm3 198 24.46 58.52 42.53 7.20

MIcm1 198 -4.86 23.88 3.02 5.28

MIcm2 198 -4.65 20.47 2.83 4.74

MIcm3 198 -5.15 20.30 2.60 5.35

WEcm1 198 5.82 25.92 18.82 4.50

WEcm2 198 6.10 25.96 18.72 4.60

WEcm3 198 5.99 25.97 18.51 4.56

Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, OWB:

Overall Well-Being, MI: mental Illness, WE: Work Engagement; „cm‟ composite variables in the

Integrated model; 1, 2, 3 = times 1, 2, 3 respectively

488

Table J.6

Correlations between the composite variables used for the longitudinal Well-Being model

1 2 3 4 5 6 7 8 9 10 11 12

1 IFwb1 1 .895*** .864*** .582*** .494*** .479*** .982*** .879*** .854*** .515*** .421*** .409***

2 IFwb2 1 .869*** .569*** .584*** .537*** .833*** .987*** .880*** .494*** .499*** .466***

3 IFwb3 1 .517*** .493*** .556*** .857*** .888*** .986*** .448*** .410*** .481***

4 PWFwb1 1 .861*** .817*** .591*** .579*** .528*** .967*** .814*** .768***

5 PWFwb2 1 .817*** .495*** .587*** .496*** .828*** .968*** .779***

6 PWF wb3 1 .477*** .546*** .557*** .782*** .767*** .971***

7 OWbwb1 1 .887*** .867*** .495*** .403*** .387***

8 OWbwb2 1 .891*** .488*** .479*** .459***

9 OWBwb3 1 .445*** .400*** .460***

10 WWBwb1 1 .823*** .771***

11 WWBwb2 1 .767***

12 WWBwb3 1

* p < .05, ** p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, OWB: overall Well-Being, WWB: Work Well-Being; „wb‟ composite variables in the Well-Being model; 1,

2, 3 = times 1, 2, 3 respectively

489

Table J.7

Correlations between the composite variables in the Mental Distress longitudinal model

1 2 3 4 5 6 7 8

1 IFmi1 1 .843*** .781*** .513*** .461*** .469*** -.634*** -.496***

2 IFmi2 1 .852*** .483*** .549*** .495*** -.535*** -.610***

3 IFmi3 1 .418*** .455*** .528*** -.558*** -.583***

4 PWFmi1 1 .816*** .739*** -.663*** -.552***

5 PWFmi2 1 .764*** -.531*** -.631***

6 PWFmi3 1 -.579*** -.608***

7 NegSp1 1 .735***

8 NegSp2 1

(Continued) 9 10 11 12 13 14 15

1 IFmi1 -.481*** -.829*** -.594*** -.538*** -.711*** -.594*** -.577***

2 IFmi2 -.521*** -.649*** -.787*** -.598*** -.626*** -.718*** -.617***

3 IFmi3 -.667*** -.643*** -.704*** -.828*** -.580*** -.620*** -.719***

4 PWFmi1 -.482*** -.451*** -.438*** -.336*** -.937*** -.788*** -.686***

5 PWFmi2 -.474*** -.383*** -.469*** -.341*** -.765*** -.947*** -.705***

6 PWFmi3 -.695*** -.412*** -.465*** -.470*** -.732*** -.770*** -.944***

7 NSPmi1 .695*** .816*** .597*** .569*** .857*** .660*** .678***

8 NSPmi2 .760*** .563*** .801*** .625*** .669*** .821*** .722***

9 NSPmi3 1 .538*** .606*** .819*** .607*** .623*** .869***

10 MIllness1 1 .614*** .566*** .717*** .537*** .542***

11 MIllness2 1 .673*** .580*** .704*** .610***

12 MIllness3 1 .493*** .518*** .715***

13 Burnout1 1 .814*** .750***

14 Burnout2 1 .786***

15 Burnout3 1

* p < .05, ** p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover; „wb‟ composite variables

in the Well-Being model; 1, 2, 3 = times 1, 2, 3 respectively

490

Table J.8

Correlations between the composite variables in the Well-Being- Mental Health longitudinal

Model

2 3 4 5 6 7 8

1 IFwbmh1 .865*** .827*** .605*** .542*** .523*** -.572*** -.406***

2 IFwbmh2 1 .879*** .600*** .649*** .582*** -.473*** -.521***

3 IFwbmh3 1 .544*** .555*** .606*** -.512*** -.514***

4 PWFwbmh1 1 .853*** .810*** -.550*** -.438***

5 PWFwbmh2 1 .827*** -.462*** -.507***

6 PWFwbmh3 1 -.483*** -.474***

7 NSpwbmh1 1 .703***

8 NSPwbmh2 1

9 10 11 12 13 14 15

1 -.411*** .978*** .860*** .844*** -.789*** -.599*** -.527***

2 -.445*** .868*** .982*** .886*** -.615*** -.769*** -.579***

3 -.598*** .828*** .860*** .979*** -.633*** -.703*** -.785***

4 -.454*** .667*** .640*** .592*** -.419*** -.416*** -.367***

5 -.471*** .590*** .691*** .593*** -.376*** -.466*** -.392***

6 -.594*** .567*** .612*** .650*** -.389*** -.448*** -.458***

7 .667*** -.537*** -.471*** -.508*** .824*** .561*** .562***

8 .742*** -.393*** -.484*** -.497*** .529*** .810*** .628***

9 NSPwbmh3 1 -.411*** -.434*** -.563*** .511*** .582*** .842***

10 OWBwbmh1 1 .884*** .864*** -.694*** -.569*** -.503***

11 OWBwbmh2 1 .889*** -.585*** -.682*** -.535***

12 OWBwbmh3 1 -.614*** -.667*** -.689***

13 MIwbmh1 1 .608*** .570***

14 MIwbmh2 1 .678***

15 MIwbmh3 1

* p < .05, p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, OWB: Overall Well-

Being, MI: Mental Illness; „wbmh‟ composite variables in the Well-Being-Mental Health model; 1, 2, 3 = Times 1,

2, 3 respectively

491

Table J.9

Correlations between the composite variables in the Work Engagement longitudinal model

1 2 3 4 5 6 7 8 9 10 11 12

1 IFwa1 1 .873*** .819*** .564*** .512*** .508*** -.582*** -.477*** -.474*** .556*** .503*** .502***

2 IFwa2 1 .875*** .521*** .578*** .523*** -.530*** -.567*** -.535*** .504*** .561*** .515***

3 IFwa3 1 .467** .498** .569*** -.543*** -.552*** -.629*** .442*** .467*** .560***

4 PWFwa1 1 .856*** .778*** -.484*** -.366*** -.357*** .967*** .830*** .747***

5 PWFwa2 1 .823*** -.427*** -.423*** -.380*** .816*** .972*** .793***

6 PWFwa3 1 -.463*** -.437*** -.487*** .740*** .782*** .973***

7 NSPwa1 1 .760*** .716*** -.459*** -.418*** -.453***

8 NSPwa2 1 .788*** -.354*** -.427*** -.445***

9 NSPwa3 1 -.321*** -.352*** -.466***

10 WEwa1 1 .827*** .740***

11 WEwa2 1 .782***

12 WEwa3 1

* p < .05, ** p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, WE: Work Engagement; „wa‟ composite variables in the Work Engagement model;

1, 2, 3 = times 1, 2, 3 respectively

492

Table J.10

Correlations between the composite variables in the Integrated longitudinal model

1 2 3 4 5 6 7 8 9

1 IFcm1 1 .877*** .840*** .558*** .472*** .471*** -.531*** -.396*** -.399***

2 IFcm2 1 .881*** .544*** .562*** .523*** -.462*** -.475*** -.424***

3 IFcm3 1 .486*** .461*** .531*** -.483*** -.448*** -.532***

4 PWFcm1 1 .847*** .804*** -.353*** -.312*** -.248***

5 PWFcm2 1 .806*** -.304*** -.338*** -.238***

6 PWFcm3 1 -.336*** -.359*** -.384***

7 NSPcm1 1 .773*** .720***

8 NSPcm2 1 .778***

9 NSPcm3 1

10 OWBcm1

11 OWBcm2

12 OWBcm3

13 MIcm1

14 MIcm2

15 MIcm3

16 WAcm1

17 WAcm2

18 WAcm3

* p < .05, ** p < .01, *** p < .001

493

Table J.10 (continued)

10 11 12 13 14 15 16 17 18

1 IFcm1 .970*** .858*** .842*** -.778*** -.591*** -.530*** .509*** .408*** .409***

2 IFcm2 .869*** .978*** .881*** -.624*** -.744*** -.569*** .488*** .488*** .464***

3 IFcm3 .829*** .863*** .970*** -.644*** -.691*** -.768*** .438*** .394*** .477***

4 PWFcm1 .567*** .551*** .506*** -.388*** -.395*** -.317*** .974*** .806*** .757***

5 PWFcm2 .477*** .559*** .475*** -.330*** -.402*** -.291*** .820*** .975*** .766***

6 PWFcm3 .470*** .529*** .542*** -.368*** -.434*** -.389*** .782*** .768*** .977***

7 NSPcm1 -.538*** -.466*** -.493*** .740*** .565*** .536*** -.398*** -.332*** -.356***

8 NSPcm2 -.404*** -.481*** -.454*** .540*** .721*** .554*** -.346*** -.387*** -.402***

9 NSPcm3 -.397*** -.427*** -.518*** .536*** .572*** .732*** -.279*** -.264*** -.438***

10 OWBcm1 1 .883*** .864*** -.696*** -.567*** -.497*** .479*** .389*** .384***

11 OWBcm2 1 .890*** -.586*** -.679*** -.532*** .472*** .455*** .450***

12 OWBcm3 1 -.623*** -.668*** -.665*** .435*** .389*** .453***

13 MIcm1 1 .620*** .581*** -.417*** -.333*** -.365***

14 MIcm2 1 .677*** -.404*** -.413*** -.439***

15 MIcm3 1 -.332*** -.290*** -.414***

16 WAcm1 1 .815*** .766***

17 WAcm2 1 .760***

18 WAcm3 1

* p < .05, ** p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, OWB: Overall Well-Being, MI: Mental Illness; WE: Work Engagement; „cm‟

composite variables in the Integrated model; 1, 2, 3 = times 1, 2, 3 respectively

494

Table J.11

Chi-squared, degrees of freedom and significance in all longitudinal models for each competing set of models

Stability, A Causality, B Reverse Causality, C Reciprocal, D Trimmed Reciprocal, E

Model Χ2 (df) p Χ

2 (df) p Χ

2 (df) p Χ

2 (df) p Χ

2 (df) p

Well-Being 95.3 (36) <.001 35.4 (28) .158 40.8 (28) .056 24.0 (20) .241 32.7 (29) .291

Mental Distress 96.9 (60) .002 60.6 (48) .105 74.3 (48) .009 43.4 (36) .186 47.5 (44) .334

Well-Being-Mental Health 131.2 (60) <.001 62.3 (48) .080 78.7 (48) .003 34.9 (36) .552 44.8 (50) .682

Work Engagement 67.6 (36) .001 36.6 (30) .189 47.8 (30) .021 33.3 (24) .097 36.5 (32) .267

Integrated 187.1 (90) <.001 96.9 (72) .027 121.0 (72) <.001 72.4 (54) .048 93.9 (74) .059

Note. X2 (df) Discrepancy function b/w sample & implied models with associated degrees of freedom; if p > .05, sample & implied models not different;

495



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Model A Stability Model B Causality Model C Reverse Causality

Model D Reciprocal Model E Trimmed

Figure J.1. Set of competing models compared in the Well-Being model, with the best fitting

model, Model E

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model 4 all reciprocal pathways mental illness and burnout,

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best fitting model, Model E

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fitting model, Model E

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Figure J.5. The set of models compared by the Integrated model, with model E, the best

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500

Appendix J: Synchronous correlations in each model

Table J.12

Synchronous correlations of the variables in the Well-Being model

Time 1

IFwb1 PWFwb1 OWBwb1 WWBwb1

IFwb1 1 .582*** .982*** .515***

PWFwb1 1 .591*** .967***

OWB1 1 .495***

WWB1 1

Time 2


IFwb2 1 .453*** .964*** .369***

PWFwb2 1 .426*** .943***

OWBwb2 1 .296***

WWBwb2 1

Time 3


IFwb3 1 .402*** .961*** .327***

PWFwb3 1 .388*** .953***

OWBwb3 1 .283***

WWBwb3 1

* p < .05, ** p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, OWB: overall Well-Being, WWB: Work Well-

Being; „wb‟ composite variables in the Well-Being model; 1, 2, 3 = times 1, 2, 3 respectively

501

Table J.13

Synchronous correlations between variables at each time period of the Mental Distress model

Time 1

IFmi1 PWFmi1 NSPmi1 BURNmi1 MImi1

IFmi1 1 .513*** -.634*** -.711*** -.829***

PWFmi1 1 -.663*** -.937*** -.451***

NSPmi1 1 .857*** .816***

BURNmi1 1 .717***

MImi1 1

Time 2


IFmi2 1 .399*** -.551*** -.624*** -.738***

PWFmi2 1 -.491*** -.914*** -.229***

NSPmi2 1 .770*** .715***

BURNmi2 1 .572***

MImi2 1

Time 3


IFmi3 1 .317*** -.532*** -.571*** -.783***

PWFmi3 1 -.567*** -.922*** -.271***

NSPmi3 1 .809*** .727***

BURNmi3 1 .591***

MImi3 1

* p < .05, ** p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: negative Spillover, BURN: burnout, MI:

Mental Illness; „mi‟ composite variables in the Mental Distress model; 1, 2, 3 = times 1, 2, 3 respectively

502

Table J.14

Synchronous correlations between variables in the Well-Being- Mental Health model

Time 1


IFwbmh1 1 .605*** -.572*** .978*** -.789***

PWFwbmh1 1 -.550*** .667*** -.419***

NSPwbmh1 1 -.537 *** .824***

OWBwbmh1 1 -.694***

MIwbmh1 1

Time 2


IFwbmh2 1 .471*** -.511*** .946*** -.729***

PWFwbmh2 1 -.391*** .530*** -.260***

NSPwbmh2 1 -.414*** .795***

OWBwbmh2 1 -.537***

MIwbmh2 1

Time 3


IFwbmh3 1 .403*** -.535*** .936*** -.759***

PWFwbmh3 1 -.455*** .483*** -.236***

NSPwbmh3 1 -.451*** .789***

OWBwbmh3 1 -.551***

MIwbmh3 1

* p < .05, ** p < .01, *** p < .001 Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, OWB: overall Well-

Being, MI: Mental Illness; „wbmh‟ composite variables in the Well-Being-Mental Health model; 1, 2, 3 = times

1, 2, 3 respectively

503

Table J.15

Synchronous correlations between variables at each time period of the Work Engagement

model

Time 1


IFwa1 1 .564*** -.582*** .556***

PWFwa1 1 -.484*** .967***

NSPwa1 1 -.459 ***

WEwa1 1

Time 2


IFwa2 1 .425*** -.429*** .383***

PWFwa2 1 -.302*** .939***

NSPwa2 1 -.305***

WEwa2 1

Time 1


IFwa3 1 .420*** -.419*** .415***

PWFwa3 1 -.294*** .949***

NSPwa3 1 -.254***

WEwa3 1

p < .05, ** p < .01, *** p < .001

Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, WE: Work

Engagement; „wa‟ composite variables in the Work Engagement model; 1, 2, 3 = times 1, 2, 3 respectively

504

Table J.16

Synchronous correlations between variables in the Integrated model

Time 1


IFcm1 1 .558*** -.513*** .970*** -.778*** .509***

PWFcm1 1 -.353*** .567*** -.388*** .974***

NSPcm1 1 -.538*** .740*** -.398***

OWBcm1 1 -.696*** .479***

MIcm1 1 -.417***

WEcm1 1

Time 2


IFcm2 1 .410*** -.425*** .938*** -.678*** .349***

PWFcm2 1 -.208** .382*** -.166* .964***

NSPcm2 1 -.428*** .627*** -.270***

OWBcm2 1 -.529*** .257***

MIcm2 1 -.203**

WEcm2 1

Time 3


IFcm3 1 .354*** -.457*** .902*** -.723*** .327***

PWFcm3 1 -.375*** .363*** -.187** .969***

NSPcm3 1 -.426*** .620*** -.425***

OWBcm3 1 -.480*** .259***

MIcm3 1 -.230**

WEcm3 1

† p < .10, * p < .05, ** p < .01, *** p < .001

Note. IF: Individual Factors, PWF: Positive Workplace Factors, NSP: Negative Spillover, OWB: Overall Well-

Being, MI: mental Illness. WE: Work Engagement; „cm‟ composite variables in the Integrated model; 1, 2, 3 =

times 1, 2, 3 respectively

505

IFwb1 IFwb2 IFwb2 IFwb3

Standardized regression weights of the auto-lagged and cross-lagged paths for the models

Table J.17

Standardized regression weights for auto-lagged and cross-lagged paths in the Well-Being model

„Input‟ variables a „Outcome‟ variables

a

IFwb2 IFwb3 PWFwb2 PWFwb3 OWBwb2 OWBwb3 WWBwb2 WWBwb3

IFwb1 .685*** .304*** .143

IFwb2 .444***

PWFwb1 .860*** .395*** .077** .219***

PWFwb2 .808*** .484***

OWBwb1 .184 .699*** .340***

OWBwb2 .181*** .589***

WWBwb1 .054* .611*** .366***

WWBwb2 -.340***

† p < .10, * p < .05, ** p < .01, *** p < .001

Note. Auto-lagged paths on the leading diagonal; Causality paths in upper triangular matrix; Reverse Causality paths in lower triangular matrix

Note: a „Input‟ and „Outcome‟ variables refer to variables at the beginning and the end of the causal arrow, respectively, as shown by the explanatory diagram

„Input‟ β = .685*** „Outcome‟ „Input‟ β = .444*** „Outcome‟

506

IFmi1 IFmi2 IFmi2 IFmi3

Table J.18

Standardized regression weights for the auto-lagged and cross-lagged paths of the Mental Distress model

„Input‟ „Outcome‟ variable a

Variable a IFmi2 IFmi3 PWFmi2 PWFmi3 NSPmi2 NSPmi3 MImi2 MImi3 BURNmi2 BURNmi3

IFmi1 .897*** .270*** -.162* -.048**

IFmi2 .496***

PWFmi1 1.059*** .266*** -.142* -.331**

PWFmi2 .352***

NSPmi1 .631*** .291*** .125*

NSPmi2 .441*** .065†

MImi1 .179** -.119** -.106† .310** .244***

MImi2 -.158*** .067** .102** .472***

BURNmi1 -.124* .315† .208** .468*** .265***

BURNmi2 -.609*** .904*** † p < .10, * p < .05, ** p < .01, *** p < .001

Note. Auto-lagged paths on the leading diagonal; Causality paths in upper triangular matrix; Reverse Causality paths in lower triangular matrix a „Input‟ and „Outcome‟ variables refer to variables at the beginning and the end of the causal arrow, respectively, as shown by the explanatory diagram


507

IFwbmh1 IFwbmh2 IFwbmh2 IFwbmh3

Table J.19

Standardized regression weights for the auto-lagged and cross-lagged paths of the Well-Being - Mental Health model

„Input‟ „Outcome‟ variables

Variables a IFwbmh2 IFwbmh3 PWFwbmh2 PWFwbmh3 NSPwbmh2 NSPwbmh3 OWBwbmh2 OWBwbmh3 MIwbmh2 MI3wbmh

IFwbmh .647*** .256*** -.213***

IFwbmh2 .651*** .229*** -.175**

PWFwbmh1 .840*** .361***

PWFwbmh2 .509***

NSPwbmh1 .803*** .254*** .134*

NSPwbmh2 .604*** .171***

OWBwbmh1 .259*** .882*** .288***

OWBwbmh2 . -.141** .357**

MIwbmh1 .050* -.136† .344*** .218***

MIwbmh2 -.179* .237***

† p < .10, * p < .05, ** p < .01, *** p < .001


a „Input‟ and „Outcome‟ variables refer to variables at the beginning and the end of the causal arrow, respectively, as shown by the explanatory diagram


508

IFwa1 IFwa2 IFwa2 IFwa3

Table J.20

Standardized regression weights for the auto-lagged and cross-lagged paths of the Work Engagement model

„Input‟ „Outcome‟ variables a

Variable a IFwa2 IFwa3 PWFwa2 PWFwa3 NSPwa2 NSPwa3 WEwa2 WEwa3

IFwa1 .867*** .240***

IFwa2 .653***

PWFwa1 .997*** .291*** .422**

PWFwa2 .679*** .334*

NSPwa1 .749*** .271***

NSPwa2 .561***

WEwa1 -.150 .415** .270***

WEwa2 -.114 .226

† p < .10, * p < .05, ** p < .01, *** p < .001




509

IFcm1 IFcm2 IFcm2 IFcm3

Table J.21

Standardized regression weights for the auto-lagged and cross-lagged paths for the Integrated model

„Input‟ „Outcome‟ variables a

Variables a IFcm2 IFcm3 PWFcm2 PWFcm3 NSPcm2 NSPcm3 OWBcm2 OWBcm3 MIcm2 MIcm3 WEcm2 WEcm3

IFcm1 .633*** .269*** -.221**

IF2cm .616*** .176*** -.139*

PWFcm1 .845*** .329*** .084** -.054 .166***

PWFcm2 .736*** .348*

NSPcm1 .762*** .304*** .185*** -.022*

NSPcm2 .540*** .094* -.015†

OWBcm1 .253*** .828*** .317***

OWBcm2 -.115* .434***

MIcm1 .048*** .311*** .208***

MIcm2 -.113† .354***

WEcm1 .065* .651*** .315***

WEcm2 -.119 .184

† p < .10, * p < .05, ** p < .01, *** p < .001




510

Appendix K: Terms and glossary for Study 2, Longitudinal modelling

Figure K.1

The set of non-nested longitudinal models that were compared in Study 2

Table K.1

An explanation of the non-nested models used in the longitudinal models

Model name Pathways in model

Stability (A) Synchronous correlations between errors of variables at the

same time and auto-lagged paths between same variables

over time

Causality (B) Stability + cross-lagged paths from „predictors‟ to „outcomes‟

over time

Reverse Causality (C) Stability + cross-lagged paths from „outcomes‟ to „predictors‟

over time

Reciprocal (D) Stability + Causality + Reverse causality models

Trimmed (E) Reciprocal model with trivial paths, β < .10 and p < .20

removed to show true non-zero pathways

Designation of time in the models

Time 1 „tm1‟ or „1‟



Notes on the SEM figures:

Double-headed arrows indicate correlations between the two variables

Single headed arrows indicate the direction of causal influence, from „cause‟

to „effect‟

„e‟ indicates the measurement error for the variable

511

Table K.2

The assessment of good fit and parsimony of the CFAs and the longitudinal models

Fit indices Range of good fit and parsimony

X2/df 1.00 – 3.00

CFA 0.95 – 1.00

RMSEA (point estimate) Perfect fit = .00; Close fit ≤ .05;

Reasonable fit between .05 and .08;

Mediocre fit between .08 and .10;

Poor fit ≥ .10

RMSEA (95% CI) Close fit if lower bound estimate < .05;

Reasonable fit if upper bound estimate < .08

Poor fit if upper bound estimate > .10

AIC Lowest estimate is most parsimonious model

ECVI Lowest estimate is model most likely to be replicated

in similar samples

Table K.3

Variables in Study 2, Longitudinal modelling

Factor label Latent Variable Indicator variables

IF Individual Factors Dispositional optimism


PWF Positive Workplace Factors Job autonomy

Skill discretion

Affective commitment

NSP Negative spillover Negative work-to-family spillover

Negative family-to-work spillover

OWB Overall well-being Life satisfaction

Psychological well-being

MI Mental Illness Depression

Anxiety

Stress

WWB Work Well-Being Work dedication

Work absorption

BURN Burnout Emotional exhaustion

Cynicism

Professional efficacy

WE Work Engagement Work dedication

Work absorption

Professional efficacy

Table K.4

Names of the models and the latent variables used in the CFAs

Model Label of latent factors in model Model Postscript

Well-Being IF, PWF, OWB, WWB wb

Mental Distress IF, PWF, NSP, MI, BURN mi

Well-Being-Mental Health IF, PWF, NSP, OWB, MI wbmh

Work Engagement IF, PWF, NSP, WE wa

Integrated IF, PWF, NSP, OWB, MI, WE cm

adapting to the work-life interface: the influence of individual differences, work...

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