State Versus Trait Conceptualisation of Psychological Capital.

Jacqueline Louise Thomas

BA. (Hons Psychology)

This thesis is presented for the degree of Doctor of Philosophy of The University of

Western Australia

School of Psychology

2014

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Abstract

A recent stream of research termed Positive Organisational Behaviour, has

resulted in a field of research focused upon an individual’s Psychological Capital

(PsyCap), a higher order construct comprised of hope, optimism, self-efficacy and

resilience. PsyCap has been be defined as “an individual’s positive psychological

state of development and as characterised by: (1) having confidence (self-efficacy) to

take on and put in the necessary effort to succeed at challenging tasks; (2) making a

positive attribution (optimism) about succeeding now and in the future; (3)

persevering towards goals and, when necessary, redirecting paths to goals (hope) in

order to succeed; and (4) when beset by problems and adversity, sustaining and

bouncing back and even beyond (resiliency) to attain success” (Luthans, Youssef, &

Avolio, 2007, p. 3). Higher levels of PsyCap within an individual have been linked to

a number of desirable outcomes including positive work attitudes such as job

satisfaction, engagement and affective commitment, behaviours such as increased

performance and increased well-being (Avey, Reichard, Luthans, & Mhatre, 2011;

Luthans, Avolio, Avey, & Norman, 2007).

PsyCap has been described as a ‘state-like’ construct, meaning that the

construct is more stable over time than a pure state but less stable than a trait. It is

measured via the PsyCap Questionnaire, termed the PCQ-24, which has a reported

test-retest reliability of .52 over a period of two weeks (Luthans, Avolio, et al.,

2007). For an organisation, the advantage of a state construct able to result in

desirable outcomes is that the organisation may be able offer training or bring about

interventions to increase that construct, thereby improving organisational

performance. Alternatively, if a construct is stable over time and related to positive

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outcomes, organisations might be able to select employees already possessing the

desired level of the trait, thereby reducing expenditure on training and interventions.

A review of the literature concerning each of the individual PsyCap

components (hope, optimism, self-efficacy and resilience) revealed that each has

been successfully conceptualised and measured as both state and trait constructs.

This thesis aimed to determine if it was possible to create pure state and pure trait

versions of the PCQ-24, and to assess the validity of the trait version as an employee

selection tool. Phase one of the research was concerned with the design of these two

scales. The scale designed to measure a state construct was termed the Moment

Specific PsyCap scale, while the scale designed to measure a trait construct was

termed the Generalised PsyCap scale.

In phase two, the stability and validity of these scales over time was assessed

via longitudinal studies. It was hypothesised that the Moment Specific PsyCap scale

would meet the criteria for a state and that the Generalised PsyCap scale would meet

the criteria for a trait. This phase included two studies, one using a sample of

employees and one using student samples. The results showed that the Generalised

PsyCap scale met the criteria for a trait scale, but the Moment Specific scale met

neither the criteria for a state or trait scale, indicating that similarly to the PCQ-24 it

is still best described as ‘state-like’. In addition this research showed that the two

scales demonstrated acceptable internal reliability and predictive, concurrent,

convergent and discriminant validity. Finally, the second study of phase two revealed

that significant order effects occur when the two scales are completed one after the

other, such that responses to the second scale are consistent with those of the first.

Therefore it was concluded that the two scales should not be completed by

individuals within a single testing session.

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Finally, the third phase of this study aimed to assess the validity of the

Generalised PsyCap scale as an employee selection tool. In this study participants

completed the Generalised PsyCap scale on their first day of employment at a new

job. At the end of a three-month training period, and again six months later at the end

of their probation period, participants again completed the Generalised PsyCap scale

together with scales required to assess the Generalised PsyCap scale’s predictive

validity. The results showed that the Generalised PsyCap scale did not meet the

criteria for classification as a trait in this final study, and did not demonstrate

predictive validity. However, Generalised PsyCap scores were related to desirable

outcomes (such as increased job satisfaction, commitment and engagement and

reduced intention to turnover and need for recovery) when the scales were

administered at the same point in time. In contrast to the studies in phase two,

Generalised PsyCap scores did not significantly correlate with performance in the

final study.

Overall, while the results of this study indicate that it is possible to measure a

more stable version of the PsyCap construct than that measured via the PCQ-24,

situational or environmental influences involved in the transition to a new job alter

levels of this construct rendering it an invalid employee selection tool. A greater

understanding of the factors that influence an individual’s PsyCap during this

transition period would be beneficial to organisations to allow them to ensure

maximal levels of PsyCap are maintained during this transition. Additionally, this

research suggests that increased levels of PsyCap may be beneficial to performance

only in some job types, and that research across a wider range of jobs is required to

understand for which occupations increased levels of PsyCap may be desirable and

for which it may not.

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Table of Contents

Abstract.........................................................................................................................3

Table of Contents..........................................................................................................6

Acknowledgments.........................................................................................................7

Chapter 1: General Introduction..................................................................................9

Chapter 2: Assessment of the PCQ-24 and Development of the Generalised and

Moment Specific PsyCap Scales................................................................................47

Chapter 3: Psychometric Assessment of the Generalised and Moment Specific

PsyCap scales………………………..........................................................................60

Chapter 4: Assessment of order and rating scale effects for Generalised and

Moment Specific PsyCap scales.................................................................................89

Chapter 5: Validity of Generalised PsyCap as an Employee Selection Tool…….142

Chapter 6: General Discussion................................................................................170

References.................................................................................................................188

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Acknowledgments

I would like to thank all those who have supervised me along the PhD

journey for the various contributions they have given. Thanks must go to Dr Elliot

Wood for his guidance during the early phases of refining the research question and

devising the strategy. I would also like to thank Professor David Morrison for his

input throughout the entire process, and particularly for his contribution in shaping

the structure of the thesis. Finally, I would like to sincerely thank Professor Mark

Griffin for taking me on as a student at the completion of my PhD. I have appreciated

your support as an advisor throughout the PhD, and would like to particularly thank

you for the feedback you offered on the final draft.

I offer my heartfelt thanks to Professor Colin MacLeod, who although not in

a supervisory role, has been an invaluable source of both academic and at times

emotional support and encouragement throughout my thesis. Your wisdom,

enthusiasm for research, and willingness to give your time and energy to others are

all traits I admire greatly.

Trying to conduct research in the ‘real world’ can be a challenging thing for a

student, and I would like to acknowledge the input of two individuals- Augie and Eli,

who have helped this research reach fruition. Thank you Augie for your belief in the

value of the research and introductions you gave me, and to Eli for the time and

effort you put in to organising me many data collection opportunities, as well as

experiences to give me insight into the working environment I was studying. I will be

forever grateful to the two of you.

I would also like to acknowledge the input of the Department of Corrective

Services (DCS), Western Australia. Employees of the department have been obliging

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participants and valuable sources of information, all willing to give their time to

support my research. Please note, that although willing supporters of my research,

the material included within this thesis cannot be considered as endorsed by DCS or

an expression of the policies of DCS.

I’ve been fortunate enough to meet, enjoy the company of, and draw support

and advice from some amazing office buddies; Danny, Pippa and Ben and pseudo

office buddies; Ann and Jason, through the course of my research. These are the

friends that have been there to bounce ideas off, share their knowledge, fix my

computer issues, offer comfort during the stressful times, share fun during the dull

times, celebrate the highs and pick me up during the lows. In short- you guys have

made the last few years bearable during the worst times and a lot of fun during the

best.

On the home front, I’d like to thank my husband Trav and my parents Jenny

and Wayne for the support they have offered throughout my studies. Thank you for

offering a listening hear, advice, and any help I asked for, but perhaps more so, thank

you for the silent support and strength you always offer, and for not asking questions

when you sensed that I didn’t want to answer them. A special thanks to my Dad, who

has also dared to answer all the academic questions I have asked, and proof read

many assignments in the early days- all with the knowledge that my responses to

feedback were not always positive. I couldn’t have wished for a better environment

in which to study, learn and live, and I thank both my Mum and Dad for creating and

providing it.

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CHAPTER ONE: GENERAL INTRODUCTION

Positive Psychology

Interest in positive psychology increased when the American Psychological

Association’s president Martin Seligman called upon psychology to reorient itself

towards the study of human strengths (Seligman, 1999). Seligman argued that before

the first world war psychology had three missions; to cure mental illness, to make the

lives of all people more productive and fulfilling and to identify and nurture high

talent (Seligman & Csikszentmihalyi, 2000). However, historical and economic

reasons, coupled with a human tendency for negative emotions to override positive

ones, resulted in psychology’s adoption of a ‘disease model’ of psychology in the

post war era. Within this model, the focus of psychology became repairing damage

rather than studying the factors that allow people and communities to flourish and

excel. Following this disease model, the discipline of psychology as a whole

developed a strong negative bias in its areas of study.

In recent times however, there has been in a flurry of activity in the study of

positive psychology (Seligman, Steen, Park, & Peterson, 2005). Positive psychology

is “an umbrella term for the study of positive emotions, positive character traits, and

enabling institutions” (Seligman et al., 2005, p. 410). It has been defined as “the

study of the conditions and processes that contribute to the flourishing or optimal

functioning of people, groups, and institutions” (Gable & Haidt, 2005, p. 104). It

addresses the two neglected missions of psychology; making the lives of all people

more productive and fulfilling, and identifying and nurturing talent.

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Positive psychology in organisational psychology

It can be argued that similarly to all fields of psychology, research in

organisational psychology has tended to focus upon deficits rather than positive

aspects of organisational life (Caza & Caza, 2008; Luthans, 2002a, 2002b). Some

examples of this include more attention being devoted to negative rather than

positive affectivity, stress and burnout rather than eustress and resistance to change

as opposed to celebration of change (Luthans, 2002a).

Positive psychology, when translated into an organisational setting has

largely followed two separate but complimentary streams of research; positive

organisational scholarship (POS) and positive organizational behaviour (POB). POS

investigates ways in which organisations and their members flourish and prosper. Its

focus is upon the dynamics in organisations that contribute to human strengths and

thriving as well as the cultivation of positive states in groups and organisations

(Dutton, Glynn, & Spreitzer, 2006). Alternatively, Fred Luthans (2002b) proposed

the discipline of POB and defined it as “the study and application of positively

oriented human resource strengths and psychological capacities that can be

measured, developed, and effectively managed for performance improvement in

today’s workplace” (p. 59). Thus, POB places a stronger emphasis upon

understanding the strengths of an individual and how they can be developed and

contribute to the success of an organisation, whereas POS places more emphasis

upon organisational factors that will result in success for the organisation.

In accordance with the POB definition, a psychological capacity must meet a

number of criteria to qualify for inclusion in the discipline. First, it must be positive.

This notion aims not to exclude all negative research from organisational

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psychology, but to add a discipline that places an emphasis upon the positive

(Luthans, Youssef, et al., 2007). This reflects the views within the general movement

of positive psychology that a more balanced approach to research must begin by

correcting the current imbalance between positive and negative topics. Next, for

inclusion in POB, a construct must have extensive research foundations and be able

to be validly measured. Luthans argues that this criterion differentiates POB research

from other positively focused books of the more ‘self-help’ variety (Luthans,

Youssef, et al., 2007). Additionally, in order to be classified as a POB construct, a

construct must be developed and managed at the individual (rather than

organisational) level. This is a criterion which differentiates POB from POS. Finally,

the POB definition requires that the capacities are state-like, and therefore open to

development. This criterion distinguishes POB from other positive approaches to

organisational psychology that may focus upon psychological traits.

Luthans and colleagues identified four constructs that meet the criteria for

inclusion in POB; hope, optimism, resilience and self-efficacy (Luthans, 2002b;

Luthans, Youssef, et al., 2007). They additionally argued that these four constructs

combine to form the higher order construct they term Psychological Capital

(PsyCap), which also meets the criteria for classification as a POB construct. While

there has been a flurry of research about PsyCap’s predictive validity and the

potential to increase an individual’s PsyCap, little longitudinal research has been

conducted and as such there remain some unanswered questions around how stable

an individual’s level of PsyCap remains over extended periods of time.

In accordance with the POB criteria, Luthans and colleagues define the

PsyCap constructs as being ‘state-like’ and open to development (Luthans, Avolio, et

al., 2007).The focus of this thesis is upon whether this definition or a more pure

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definition such as state or trait would be more accurate. This is considered

particularly important due to the differential uses for the state versus trait qualities of

an individual in organisational settings. This chapter will next discuss the importance

of the state versus trait distinction for organisational psychology and review the way

in which states and traits have been classified within POB and other streams of

research. A clear understanding of how a state and trait is defined is important to

establish criteria as to how to define the PsyCap construct. Following this, each of

the POB constructs will be introduced in more detail before discussing their stability

over time. The review of the individual PsyCap constructs will show why there is

reason to consider the potential for PsyCap to be classified as either a pure state or a

pure trait.

The importance of the state versus trait distinction in organisational psychology

Positive organisational behaviour is focused upon the psychological strengths

that an individual can possess which may result in positive work performance or

positive work attitudes such as high job satisfaction and organisational commitment.

If there is a particular strength that an employee can possess which predicts positive

job performance or work attitudes, an organisation may choose between two

different methods of ensuring this strength is present within its employees. The first,

and the method encouraged in POB research, is to develop the strength in existing

employees. Alternatively, rather than investing in interventions to increase the

individual strengths of employees, organisations may choose to select employees

already high in the strength. One factor which must be considered when deciding

between these two methods is how stable the strength is in individuals. If the strength

is known to vary over time then it may be beneficial to develop it within existing

employees. However, if it is stable over time and resistant to attempts to change it,

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then an organisation will benefit from selecting employees already possessing the

desired level of the strength. This is an important choice for organisations and it is

important that the stability of a construct over time is well understood to make this

decision effectively. However, the discussion below will show that the way in which

the stability of the POB constructs have been described is not sufficiently clear for

organisations to be certain which strategy is the best one to adopt.

The classification of states and traits

Wright (2007) states that the role of time or timing has not been well

considered in practical organisational research and that “nowhere is this lack of

awareness more evident than in the failure to provide a legitimate temporal

distinction between what constitutes a state as opposed to a trait” (p. 179). Despite

the lack of a clear temporal distinction between state and trait, Wright does claim that

“generally speaking, personality research typically considers person characteristics as

dispositional or trait-like if they have some measure of temporal continuity and if

they are capable of influencing subsequent behaviour” (p. 180). In contrast, he

suggests that a state is “most accurately operationalized when narrowly measured ‘at

this moment’ or ‘today’”(p. 180). Simply, a trait can be conceptualised as a constant

feature, whereas a state is circumstantially dependent and changes with time

(Salminen, Saarijarvi, Aairela, & Tamminen, 1994).

Luthans and colleagues argue that states and traits should be conceptualised

as existing on a continuum of different degrees of stability rather than as

dichotomous stable or unstable constructs (Avey, Luthans, & Mhatre, 2008). They

base this conclusion upon the findings of Conley (1984). Conley investigated the

stability of general intelligence, the personality traits of neuroticism, extraversion

and psychotism, and the construct of self-opinion by reviewing a large number of

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longitudinal studies which had been previously conducted using those constructs. In

this review Conley concluded that intelligence was more stable than personality and

that personality was more stable than self-opinion. This finding was interpreted as

initial evidence that there is a hierarchy of consistency rather than a state versus trait

dichotomy.

The problem with the continuum perspective is that it allows for only a vague

classification of a construct’s stability and this imprecision begs the questions of how

should stability be defined; by how much, how frequently or at what rate can one

expect to see change in a construct near the centre of the continuum? Answers to

these questions are of importance when an organisation makes decisions about

whether the develop a construct in existing employees, whether to select employees

already possessing the desired levels of the construct or whether a combination of the

two methods would be most beneficial to organisational performance.

The co-existence of states and traits.

Importantly, it should be noted that it is perfectly possible that a single

construct can exist in both a state and trait form. Research into anxiety, for example,

has shown this to be the case. When stating that someone is anxious, one may be

referring to the fact that they are anxious at that particular point in time (due to the

presence of a stressor; state anxiety), or that they chronically or frequently have

higher levels of state anxiousness than most people (trait anxiety). Thus, a person

with an elevated trait-anxiety score is “generally more disposed than the average

person to respond with state anxiety, and, unless (he) lives in a very sheltered

environment, he is likely to experience anxiety states more often than other people”

(Spielberger, 1966, p. 16). Reflecting this distinction, Spielberger and colleagues

(1970) developed the State-Trait Anxiety Inventory, which consists of separate self-

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report scales for measuring state and trait anxiety. The state scale requires

respondents to indicate their feelings related to anxiety at a specified point in time,

whereas the trait scale requires them to indicate how they generally feel in terms of

anxiety related symptoms. Similarly, the more positive construct of subjective well-

being has been classified as both a state and a trait (Diener, 1984).

The review of each of the four central POB constructs (hope, optimism,

resilience and self-efficacy) below will show that they have each been linked with

positive organisational outcomes and conceptualised and measured successfully as

both state and trait constructs. However, PsyCap, the construct comprised of the

combination of the four constructs has only been conceptualised and measured as

‘state-like’ (e.g. Luthans, Avolio, et al., 2007), consistent with the continuum

perspective. The lack of research conducted to investigate PsyCap as a trait construct

appears to be a gap in our understanding of PsyCap, which will be addressed in this

thesis. As each of the PsyCap components has been successfully measured as state

and trait, it also appears likely that there may be a state rather than ‘state-like’

construct of PsyCap. Therefore, research in this thesis will attempt to measure state

and trait components of PsyCap rather than adopting the continuum perspective. In

classifying traits and states as separate constructs, Wright’s (2007) distinction that a

construct can be considered a trait if it demonstrates temporal continuity and can

influence subsequent behaviour will be used. If a construct does not meet these

criteria it will be considered a state.

Hope

Leading hope theorist C.R. Snyder and colleagues have defined hope as “a

positive motivational state that is based on an interactively derived sense of

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successful (a) agency (goal-directed energy), and (b) pathways (planning to meet

goals)” (Snyder, Irving, & Anderson, 1991b, p. 287). Put simply, hopeful thinking is

the belief that one can find pathways to reach a desired goal and the belief that one

can muster the motivation to use those pathways (Snyder, 2002).

A fundamental aspect of hope theory is that much of human behaviour is goal

directed, and goals are the foundation upon which Snyder’s (1989) hope theory was

built (Rand & Cheavens, 2009). Lopez, Snyder and Pedrotti (2003) outline a number

of factors related to goals. First, a goal must be of sufficient value before a person

will pursue it. Goals may be approach-oriented (i.e. aim to achieve something) or

preventative in nature (i.e. aim to avoid something). They can also vary temporally

(i.e. long versus short term goals) and in their difficulty to achieve. People high in

hope generate more, and generally more clearly defined goals than those low in hope

(Snyder, 2002).

The pathways component of hope refers to whether an individual perceives

that he or she is able to identify one or more workable routes to achieve a goal.

Research has shown that people high in hope are effective in generating alternative

pathways to goals (Snyder, Harris, et al., 1991). The ability to generate alternative

routes to goals is important when encountering barriers or blockages to goal

achievement (Rand & Cheavens, 2009). When faced with barriers, people high in

hope are able to generate alternative pathways and continue towards goal attainment,

whereas those low in hope may no longer see the goal as attainable.

Agency is the motivational component of goal-theory and involves

affirmative self-talk such as “I can do this” and “I won’t give up” (Snyder, Lapointe,

Crowson, & Early, 1998). Agency differs from pathways thinking and self-efficacy

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in that it reflects the intention and will to act rather than perceiving the ability (self-

efficacy) or the route (pathways) to achieve a goal. Hopeful thinking requires that

both the agency and pathways components are present (Snyder, Irving, et al., 1991b).

The two components are thought to influence each other such that during goal pursuit

pathways thinking augments agency thinking and vice versa (Rand & Cheavens,

2009; Snyder, Harris, et al., 1991). If either component of hopeful thought is lacking,

then the likelihood of goal attainment is impaired (Adams et al., 2003).

Although most theories of hope can be grouped into either emotion-based

models or cognition-based models, the two approaches are beginning to merge and

hope can be seen to have both affective and cognitive qualities (Lopez et al., 2003).

Snyder’s hope theory emphasises the cognitive component of hope, but also contains

an emotive component. According to the theory, people’s perceptions of their goal

pursuits causally affect their emotions; people appraise encountering barriers to goals

as stressful, positive emotions result from perceptions of goal attainment and

negative emotions reflect a perceived lack of success (Lopez et al., 2003). Supporting

research has shown that successfully achieving a goal or overcoming obstacles

causes positive emotions whereas insurmountable goal blockages generate negative

emotions (Snyder et al., 1996).

Emotions during goal pursuit (caused by the perceived degree of success of

goal attainment) also serve as reinforcing feedback for the goal directed behaviour.

For example, if someone perceives their goal progress to be going well, the feedback

of positive emotions reinforce the goal pursuit progress and maintain motivation. If

the person is not doing well, negative emotions and accompanying self-critical

thoughts may undermine the goal attainment progress (Lopez et al., 2003). However,

there is a difference in emotions and motivation between those high and low in hope.

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High hope people are likely to view barriers to goals as a challenge and remain

motivated but pursue a different pathway to a goal, whereas low hope people more

typically experience negative emotions and become stuck, and often abandon their

goal pursuits when faced with barriers.

Hope and positive organisational outcomes. Being high in hope has been

found to have many benefits for individuals. It has been linked with improved

physical health (Snyder, Irving, & Anderson, 1991a), lower levels of depression or

dysphoria (Carifio & Rhodes, 2002; Kwon, 2000), better overall psychological

adjustment (Kwon, 2002), greater social competence (Barnum, Snyder, Rapoff,

Mani, & Thompson, 1998), higher levels of positive affect and lower levels of

negative affect (Snyder et al., 1996). Similarly to anxiety, affect has been shown to

have both state and trait components (Watson, Clark, & Tellegen, 1988). This is one

piece of evidence that opens the possibility to the hope construct similarly having

both state and trait components.

According to hope theory, high hope employees should set more personal

goals for achievement, be motivated to work towards achieving those goals, be

readily able to find alternative pathways when faced with blockages, be less likely to

interpret any blockages faced as stressful, and are more likely to experience positive

emotions. What does the research to date indicate about the impact of being a high

versus low hope employee or leader of employees? The research reviewed below

will show that it has been linked to job performance, perseverance and attitudes

towards work.

Initial research which has suggested that high hope may be related to high

work performance was obtained from academic samples. A number of studies have

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measured participant’s hope, and found it to be a significant predictor of future

academic performance (Ciarrochi, Heaven, & Davies, 2007; Curry, Snyder, Cook,

Ruby, & Rehm, 1997; Snyder, 1994; Snyder, Irving, et al., 1991a). These studies

have found that dispositional hope scores in high school and college populations

have been able to predict academic achievement (i.e. semester grades and overall

grade point averages), even when accounting for previous indicators of academic

achievement or ability (i.e. the students’ grade point averages or measures of verbal

and numerical aptitude). Snyder and colleagues (1996) additionally showed that state

hope scores were predictive of performance on a complex verbal learning task. Aside

from academic achievements, work in college samples has found that hope was able

to predict the sporting achievements of participants beyond predictions made by self-

esteem, mood, confidence, amount of time practiced and natural athletic talent

(Curry et al., 1997).

In studies of a number of different employee samples, Peterson and Byron

(2008) found individual’s hope scores to be predictive of their job performance.

Those with higher hope scored more highly on objective performance ratings taken a

year after the hope ratings. This study additionally found that management

executives higher in hope produced more and better quality solutions to work related

problems. In terms of employee commitment and retention, studies with children

playing sports and academics have found that high-hope people are more likely to

continue with (as opposed to quitting) an activity (Snyder, 2002). Studies have

additionally reported a significant correlation between hope and job satisfaction

(Luthans, Avolio, et al., 2007).

An individual’s level of hope is not only important for their individual

performance, but a leader’s level of hope has been shown to influence the well-being

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of their employees as well. An exploratory study conducted with managers of fast-

food restaurants found that restaurants run by managers high in hope had better

profits, higher retention of employees, and higher levels of employee satisfaction

than those run by managers low in hope (S. J. Peterson & Luthans, 2003). Similarly,

Mansfield (2007) found that the level of hopefulness of the CEO and top leadership

team of an organisation were able to predict the employees’ job satisfaction and

retention likelihood.

The stability of hope. In 1991 Snyder and colleagues (Snyder, Harris, et al.,

1991) proposed the Hope Scale, a measure of dispositional hope. In this paper they

stated that “The present model assumes that hope is consistent across situations and

time. Although specific situations may exert a unique influence on the level of hope,

there is nevertheless a resiliency once this cognitive set is established. Generally,

because of their underlying sense of agency and pathways in achieving goals, higher

as compared with lower hope people should have more goals across the various

arenas of their life, and they should select and attain more difficult goals” (Snyder,

Harris, et al., 1991, p. 571). The dispositional aspect of hope described here was

supported by four reported samples in which the test-retest reliability of the scale had

been examined. The reported test-retest correlations were .85 over a three week

interval, .73 over an 8 week interval and .76 and .82 over 10 week intervals in two

separate samples. These test-retest correlations are of a similar magnitude to the test-

retest correlations of other scales designed to measure traits. For example, a review

Schuerger and colleagues (1982) reviewed the test-retest reliabilities of a number of

different scales designed to measure personality traits. They found that the average

test-retest reliabilities of the personality variables ranged from .67-.91 over a period

of 1-2 weeks, and from .61-.80 after one to two years.

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In 1996, Snyder and colleagues (Snyder et al., 1996) developed the State

Hope scale. They developed this scale on the premise that “in contrast to the more

enduring type of motivational set, there should be a temporal state that is related to

the ongoing events in people’s lives... People probably have dispositional hope that

applies across situations and times, but they also have state hope that reflects

particular times and more proximal events. State hope, as measured in a given

moment, provides a snapshot of a person’s current goal-directed thinking” (Snyder et

al., 1996, p. 321). When validating the state hope scale, participants completed the

scale on a daily basis for a period of four weeks. The test-retest correlations of the

scores across any two days ranged from .48 to .93. These test-retest correlations

show the potential for state-hope to vary significantly over small periods of time.

The proposed relationship between state and trait hope is that an individual’s

trait hope scores should set a range within which state hope may vary. Thus, an

individual with high trait hope should have a higher range of state hope scores than

an individual with low trait hope (Snyder et al., 1996).

Optimism

Perhaps the most popular view of an optimist is that of a person who sees the

glass as being half-full rather than half empty. This expression reflects the view of

optimists as people who believe that the world is the ‘best of all possible worlds’

(Reivich & Gillham, 2003). Within psychological research, optimism is more

strongly linked to expectations about either specific or general outcomes. Optimists

can be readily summarised as people who expect good things to happen, whereas

pessimists are people who expect bad things to happen (Carver, Scheier, Miller, &

Fulford, 2009). Although the terms optimist and pessimist are used to conveniently

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differentiate between those high and low in optimism, some argue that the two

groups are not qualitatively distinct from each other. Rather, all people sit

somewhere on a continuum which ranges from very optimistic to very pessimistic,

with most people situated near the centre of the continuum (Carver et al., 2009).

However, contrasting views have also been presented, with some evidence

suggesting that optimism and pessimism are distinct dimensions (Bryant &

Cvengros, 2004). According to this view people can be both optimistic and

pessimistic at the same time. The majority of research conducted, and all research

reviewed below, is based on scales that give a single score along the optimism-

pessimism continuum.

The key theories in optimism research are based upon either expectations or

explanatory style. While some researchers argue that the two are conceptually linked

(Carver & Scheier, 2003), others argue that they can be unrelated (Reivich &

Gillham, 2003). This review will first explain optimism from the view point of

expectations, before turning to the theory of optimism as based on explanatory style.

A key theory of optimism based upon expectancy is Scheier and Carver’s

(1985) theory of dispositional optimism. This theory stems from expectancy-value

models of motivation, which, similarly to theories for hope, conceptualise behaviour

as being directed by the pursuit of goals. Goals are the value component of

expectancy-value theories. They can be classified as desirable or undesirable, may

differ in their specificity and the extent to which they are valued by an individual (the

conceptualisation of goals in expectancy-value theories is very similar to the

description of goals in hope theory above). The greater the value of a goal, the

greater a person’s motivation to act with regards to that goal will be (Carver &

Scheier, 2001).

! 23!

The expectancy component relates to a person’s level of confidence (or

doubt) about whether the goal can be obtained. Expectancy-value theories posit that a

person will only embark on goal pursuit activities if they have sufficient confidence

that a goal can be obtained. Throughout the pursuit of the goal they will only

continue to exert effort if they retain sufficient confidence that the goal can be

achieved (Carver & Scheier, 2001). Expectancies that people hold with regards to

goal attainment can be influenced by many sources of information, with one

important source being information from memory of the outcomes of previous goal

pursuits.

According to theories based upon expectancy, when confronting a challenge

or threat, optimists would be expected to adopt a confident view, whereas pessimists

are likely to be more doubtful and hesitant. Therefore, optimists are likely to exert

energy towards goal attainment in situations where pessimists are likely to withdraw

effort and potentially abandon goal pursuit. In support of this, research has shown

that optimists are inclined to take a goal-engaged approach to coping with stress,

while the coping methods of pessimists are more related to disengagement (Carver et

al., 1993; Scheier et al., 1989).

A second class of theories of optimism is that based upon explanatory style.

The predominant theory is Seligman’s theory of learned optimism which is an

extension of his previous research on learned helplessness (Seligman, 2006).

Explanatory style refers to an individual’s habitual way of explaining the causes of

events. According to Seligman’s theory (Seligman, 2006), someone’s explanatory

style lies at the core of whether they believe themselves to be deserving and valuable

(the view of an optimist) or hopeless and worthless (the view of a pessimist). There

! 24!

are three key elements to explanatory style; permanence, pervasiveness and

personalisation.

The permanence aspect of explanatory style refers to whether people assume

events to have permanent or temporary causes. Optimists will tend to view positive

events as having permanent causes and negative events as having temporary causes,

whereas the opposite is true for pessimists. They tend to view negative events as

having a permanent cause and positive events as having a more temporary cause. For

example, after winning a raffle or lottery, an optimist may explain the event in terms

of them being a lucky person, whereas a pessimist may take the view that it was their

lucky day. Although the view of having a lucky day is not necessarily a negative one,

it does imply to the person that they are not likely to often win competitions, while

the optimist who sees themselves as being generally lucky, may assume that they

will win games of luck more often. The permanence aspect is thought to influence

whether people view that they will chronically succeed or fail in certain aspects of

their life.

Pervasiveness refers to whether individuals think that events have a specific

or a more general or universal cause. An optimist will interpret positive events as

having a universal cause, while negative events are seen to be caused by specific

factors. Pessimists take the opposite view and see positive events as having a specific

cause while negative events are caused by more general factors. For example, an

optimist may explain their good results in a maths test in terms of them being smart,

while the pessimist will take the view that they are smart at maths. This has the effect

of the optimist expecting good results in all goals or activities that require intellect,

while the pessimist is more likely to think that they may be able to achieve goals

related to maths ability but not other areas of intellect. The pervasiveness of an

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explanation is thought to influence the scope of someone’s life that is affected by

positive and negative events. For example, if someone is fired from their job but

makes a specific attribution about it, they may find that their work-life is affected but

they are able to remain positive and succeed in other aspects of their lives. By

contrast, if a global attribution is made after being fired, someone may feel

despondent and negative about all aspects of their life.

Personalisation refers to whether a person sees an event as being personally

caused by them or caused by external factors. Optimists tend to explain positive

events in terms of their actions causing the events, while seeing negative events as

being caused external factors. Pessimists by contrast are likely to see positive events

as being externally caused and negative events as being their own fault. For example,

after scoring a goal in a soccer game, an optimist is likely to credit their goal

shooting ability, while the pessimist may acknowledge their team’s skill in setting up

the goal as having caused the event. The extent to which people credit events with

having internal or external causes is thought to influence their self-esteem.

In summary, those who explain negative events in terms of temporary,

specific and external factors can be classified as optimists, while those who explain

them in terms of permanent, universal and internal factors are pessimists. The

opposite explanatory styles are true for the explanation of positive events

(permanent, universal and internal for optimists and temporary, specific and external

for pessimists). Someone who sees positive events as having permanent causes,

universal to all aspects of their lives and caused by their own personal actions or

talents have reason to act with confidence and expect positive events to occur in the

future. Additionally, explaining a negative event as being a temporary event, related

to a specific aspect of life or event and caused by external factors limits the person’s

! 26!

perception of failure to a specific situation and therefore means that they will only

feel helpless or like they are unable to succeed with regards to a specific event and

moment in time.

Seligman and colleagues have devised two techniques to measure explanatory

style; the Attributional Style Questionnaire (ASQ; C. Peterson et al., 1982), and the

content analysis of verbatim explanations (CAVE; C. Peterson, Luborsky, &

Seligman, 1983). Evidence for the impact of the explanatory styles can be drawn

from a number of studies discussed below, in which optimism as measured in terms

of explanatory style using the ASQ or CAVE, have been shown to impact upon work

performance and outcomes discussed below.

Optimism and positive organisational outcomes. Optimism has been

linked to many positive individual outcomes such as enhanced psychological well-

being, positive mood, good morale, perseverance, effective problem solving,

popularity, good physical health and positive health habits (C. Peterson, 2000;

Scheier & Carver, 1992; Seligman, 2006). Pessimism by contrast, has been linked to

depression, passivity, failure, social estrangement, morbidity and mortality (C.

Peterson, 2000; Seligman, 2006). Studies examining the effects of optimism on well-

being have consistently found that optimists routinely maintain higher levels of well-

being during times of stress than those who are less optimistic (Scheier & Carver,

1993). With regards to job-related well-being, studies have reported that optimism is

predictive of lower levels of perceived job stress, reduced work/non-work conflict

and reduced risk for job burnout (Chang, Rand, & Strunk, 2000; Tuten &

Neidermeyer, 2004). Some studies which link optimism to positive organisational

outcomes are described below.

! 27!

Initial evidence linking optimism to job performance can be drawn from

studies of academic performance. Studies of university students have shown that

students who are rated to be optimists based on an assessment of their explanatory

style had better grade point averages than pessimists, even when controlling for

ability. These studies have used measures of Scholastic Aptitude Test (SAT) scores

(Seligman, 2006), or high school grade-point average (Chemers, Hu, & Garcia,

2001). The study of Chemers et al. additionally found that optimists as compared to

pessimists were better ‘adjusted’ to university. Their adjustment measure was a

composite measure which combined the participants’ self-rated satisfaction with their

academic progress and intention to continue at university.

Studies have also linked optimism to improved athletic performance in both

team (Gordon, 2008) and individual (Seligman, Nolen-Hoeksema, Thornton, &

Thornton, 1990) sports. In Gordon’s study of soccer players, the overall performance

score for soccer plays was significantly related to their optimism score (r= .77). The

study of individual sports used varsity swimmers as a sample and found that

swimmers with a more pessimistic explanatory style (measured at the start of a swim

season) showed more unexpected poor performances during competition than

optimistic swimmers. In an extension of the study the swimmers were induced to

experience failure through falsely reporting to them that their swim times were slow.

Regression analysis showed that the ratio of the swimmers’ times before and after the

negative feedback was predicted by their optimism score (t= 3.06, p < .005). This

and other analyses clearly showed that after the negative feedback swim times

deteriorated for the pessimistic swimmers while the optimistic swimmers

subsequently swam at the same or faster speeds. This suggests that the optimists had

! 28!

less disappointing swims than the pessimists because they were better able to

persevere after setbacks.

In more concrete work domains, a number of studies have found that

measures of optimism are able to predict performance. Many of these studies have

been conducted in employees working within the sales domain. For example,

Seligman and Schulman (1986) conducted a cross-sectional study using life

insurance sales agents and found that individuals scoring in the top (more optimistic)

half of their Attributional Style Questionnaire (ASQ) sold 37% more insurance in

their first two years on the job than those that scored in the bottom (more pessimistic)

half. They additionally conducted a one-year longitudinal study of newly hired

agents and found that the agents who fell in the top half of the ASQ sold more

insurance and remained in the role at twice the rate of those in the bottom half. Thus,

this study shows not only higher performance levels, but also higher retention rates

for optimistic employees. These findings were reflected in a similar study which also

used insurance sales-people as study participants (Corr & Gray, 1995) and the ASQ

as a measure of optimism. In this study optimism was positively related to

performance both in terms of sales effort and total number of sales.

Other studies in contrast have not found support for the relationship between

optimism and performance and there is evidence to suggest that an optimistic

disposition may only be predictive of performance in particular domains. For

example, a study of law students found that pessimists outperformed optimists on

measures of grade point averages and law journal success (Satterfield, Monahan, &

Seligman, 1997). This suggests that optimism may be more important for job

performance in some professions than others. It has been argued that sales is a

profession in which failure is common and that success as a salesperson is dependant

! 29!

in part on the ability to cope well with failure (Dixon & Schertzer, 2005). The study

of swimmers has similarly shown that optimists respond better to failure than

pessimists. Thus, in jobs in which failure is frequently experienced optimism may be

an important factor in predicting job success. It has also been suggested that in

domains that require more caution and reality appreciation than initiative or

creativity, pessimism as opposed to optimism may be advantageous to job

performance (Satterfield et al., 1997).

The difference between hope and optimism. The constructs of hope and

optimism share many similarities. Indeed, scores on hope and optimism scales are

positively correlated. For example Bryant & Cvenngros (2004) report that hope and

optimism share approximately 64 percent of their variance. Other studies have

similarly reported a positive correlation between hope and optimism (see for example

Luthans, Avolio, et al., 2007 (r= .61 and .42 in two seperate samples); Snyder,

Harris, et al., 1991 (who cite two unpublished manuscripts in which r= .60 and r=

.50)). Additionally, the review above clearly shows that the constructs are predictive

of many similar outcome variables. Some studies have even combined the two

constructs into one research variable including Rand (2009) who conducted a study

in that combined measures of hope and optimism to form a trait he labelled ‘goal

attitude’. The similarities and differences between the two constructs are explored

below.

The central idea that behaviour is directed towards or away from goals or

avoidance goals is shared by both theories. Additionally, the theories both presume

that sufficient motivation or confidence is necessary to elicit a behavioural response

to a goal. Snyder (2002) argued that optimism may be conceptualised as being

similar to the agency component of hope, but that it does not incorporate the

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pathways component. That is, optimists may be confident that they can achieve a

goal but if they are low in hope they may not be able to conceptualise the way in

which the goal may be accomplished. How can an optimist be confident that a goal

can be achieved if they cannot see a way to achieve it? One possibility is that hope

focuses upon an individual’s own actions resulting in goal achievement whereas

optimism allows for the possibility of confidence that a goal will be achieved through

external means.

Bryant and Cvengros (2004) conducted a study to investigate the relationship

between hope and optimism. They found that although hope and optimism share 64%

of their variance, factor analysis of data from participants who had completed the

Adult Hope Scale and a measure of optimism (Life Orientation Test, see below)

showed that a model with Hope and Optimism as separate factors provided superior

fit to the data than a model with a single combined factor for hope and optimism.

They also reported that the two constructs showed different patterns of association

with coping and self-efficacy. Specifically, optimism had a stronger influence on the

use of positive reappraisal as a coping mechanism than hope, whereas hope had a

greater influence on the overall level of general self-efficacy than optimism. Based

on this research the authors suggested that “hope focuses more directly on the

personal attainment of specific goals, whereas optimism focuses more broadly on the

expected quality of future outcomes in general” (p. 273).

The stability of optimism. Scheier and Carver (Scheier & Carver, 1985)

designed a measure of dispositional optimism called the Life Orientation Test (LOT).

The reported test-retest correlation for this scale was .79 over a four-week period.

This shows a high level of consistency between testing periods and supports the idea

that there is a relatively stable, trait component to optimism.

! 31!

The authors of this dispositional measure of optimism also state however that

“there is little doubt that even a serious pessimist varies somewhat in his or her

pessimism over changing circumstances, as does the optimist” (Carver & Scheier,

2003, p. 85) and point towards the need to develop a state measure of optimism. The

suggestion that there is a state component of optimism can also be drawn from

Seligman’s idea of “learned optimism” in which people may develop and change

their level of optimism by changing their cognitive thought processes (Seligman,

2006). This does not point towards a momentary or fluctuating state, but does show

that there is a developable component of optimism.

Self-efficacy

Self-efficacy is a key element of Bandura’s (1977b) social learning theory. It

refers to “beliefs in one’s capabilities to mobilize the motivation, cognitive resources,

and courses of action needed to meet given situational demands” (Wood & Bandura,

1989, p. 408) and is concerned with people’s judgements or perceptions about their

capability to perform particular tasks, rather than their actual abilities to perform the

tasks. The basic premise behind the theory is that people’s efficacy beliefs determine

the behaviours that they choose to engage in and how much they persevere when

faced with challenges and setbacks (Maddux, 2009).

There are three dimensions upon which someone’s self-efficacy expectations

may vary; magnitude, generality and strength. Magnitude refers to the level of

difficulty of a task that a person feels they can complete. Some people may be

limited to feeling able to complete only simple tasks, while others feel that moderate

or even very difficult tasks are within their capabilities. Generality refers to whether

people expect success in specific or more general situations. Finally, strength refers

! 32!

to how susceptible one’s efficacy beliefs are to change. Weak expectations are easily

extinguished by disconfirming experiences whereas strong ones will not be easily

altered. Therefore, someone with strong expectations about their ability to perform a

task is likely to persevere despite facing difficulties or disconfirming experiences

(Bandura, 1977a).

Bandura (1997) posited that self-efficacy beliefs are constructed from four

principle sources of information. The first, and strongest, is enactive mastery

experiences. This refers to instances in which someone successfully completes a task

and the successful completion serves as an indicator of their capability. These are

thought to be the most influential source of information because they provide the

most authentic feedback of whether one can complete a task. Repeated successes

build a robust sense of self-efficacy, whereas failures undermine it. Failures are

thought to be particularly harmful to efficacy beliefs if they occur before a sense of

self-efficacy has been established. When people persevere through difficulties to

attain success, they develop confidence in their ability to overcome obstacles and are

subsequently more likely to recover quickly from setbacks and persevere through

obstacles. The second source of information which influences self-efficacy is

information drawn from vicarious experience. People may develop efficacy through

seeing others effectively perform behaviours. The effects of such modelling is

enhanced when there is similarity between the subject and model in terms of personal

characteristics such as age and gender (Bandura, 1977a). Thirdly, verbal or social

persuasion can influence someone’s efficacy. When someone is completing a new

task, and particularly challenging a one, self-efficacy can be enhanced through a

significant other expressing confidence in their abilities to complete the task. Finally,

an individual’s perceptions of their physiological state may influence their self-

! 33!

efficacy. For example, if an individual feels highly aroused or anxious while

completing a task, this may signal an inability to complete the task and therefore

reduce self-efficacy. According to the theory, an individual will weight and integrate

these four information sources together with other information about the task,

personal factors and situational factors to give rise to a final perception of self-

efficacy.

Self-efficacy as conceptualised by Bandura refers to an ability to perform a

specific task. Therefore, it has been emphasised that measures of self-efficacy should

be tailored to the domain being studied (Bandura & Adams, 1977; Gist, 1987).

Parker (1998) however argued that in today’s work environment where effective

performance requires employees who are sufficiently confident to take on broader

duties, a form of self-efficacy which she termed role breadth self-efficacy (RBSE) is

particularly relevant. Rather than focusing upon a specific task, RBSE “concerns the

extent to which people feel confident that they are able to carry out a broader and

more proactive role, beyond traditional prescribed technical requirements” (Parker,

1998, p. 835). She argued that while the exact components that make up an expanded

role will vary between job roles and organisations, they are also likely to share some

generic competencies. She identified the ability to be proactive and use initiative,

strong interpersonal skills and the ability to integrate activities laterally across

departments and manage the interface between boundaries as generic components of

RBSE. RBSE is similar to Bandura’s conception of self-efficacy in that it is

concerned with the employee’s belief that they are able to perform these proactive,

interpersonal and integrative behaviours rather than whether they actually can, or are

allowed to perform these behaviours. Parker further argued that it differs from other

! 34!

measures of contextual performance such as organisational citizenship behaviour in

that it is concerned with what people feel they can do, not their actual behaviours.

Self-efficacy and positive organisational outcomes. High self-efficacy in a

task is argued to increase the effort and persistence towards challenging tasks,

therefore increasing the likelihood that they will be completed (Axtell & Parker,

2003). Research has consistently found a link between self-efficacy and work related

performance. This research has covered a broad spectrum of work domains including

insurance sales (Barling & Beattie, 1983), managerial performance (Wood, Bandura,

& Bailey, 1990), managerial idea generation (Gist, 1989), faculty research

productivity (Taylor, Locke, Lee, & Gist, 1984), coping with career related events

(Stumpf, Brief, & Hartman, 1987), adaptability to new technology (Hill, Smith, &

Mann, 1987) and learning and skill acquisition (Campbell & Hackett, 1986; Mitchell,

Hopper, Daniels, George-Falvy, & James, 1994; Wood & Locke, 1987). A meta-

analysis of 114 studies conducted by Stajkovic and Luthans (1998a) showed a

significant correlation (the weighted correlation, corrected for attenuation = .38)

between self-efficacy and work-related performance.

Aside from performance, a number of other positive organisational outcomes

have been found to be related to self-efficacy. For example, new employee’s self-

efficacy measured upon entering an organisation was found to predict the employee’s

self-rated ability to cope with the job and their turnover one year later (Saks, 1995).

Another study based upon employees required to work away from their office base

found that those with higher self-efficacy to work in this context reported more

positive levels of adjustment to the role (Raghuram, Wiesenfeld, & Garud, 2003).

Similarly, Lee and Ashforth (1990) reported a negative relationship between self-

efficacy and burnout. On the positive side of adjustment, studies have also shown

! 35!

that individuals with higher self-efficacy are more likely to derive a sense of

satisfaction from their jobs. One recent study reporting this finding involved the

longitudinal study of teachers (Caprara, Barbaranelli, Steca, & Malone, 2006).

Studies focused upon RBSE have generally not included the same outcome

variables as those focused upon domain specific self-efficacy, making direct

comparison of the two types of self-efficacy difficult. However, the studies which

have focused upon RBSE rather than self-efficacy in a particular domain have found

this particular form of self-efficacy to be related to proactive behaviours in the

workplace. The types of behaviours that RBSE has been linked to include employee

innovation, proactive performance, personal initiative and taking charge (Axtell &

Parker, 2003).

The difference between self-efficacy and hope. Self-efficacy concerns an

individual’s belief about their ability to execute a particular behaviour. In contrast,

hope is concerned with two key components; being able to find pathways to a goal,

and mustering the motivation to follow them. Although these concepts all relate to

goal directed behaviour there are key differences. The pathways component of hope

is about being able to find a pathway to achieve a goal, whereas self-efficacy is more

concerned with belief in one’s ability to execute the behaviours required to follow

that pathway.

The difference between self-efficacy and optimism. Self-efficacy and

optimism are related in that they are both concerned with the expectation that desired

outcomes will be achieved. However, where they are crucially different is the extent

to which the positive outcomes rely on personal actions. Self-efficacy concerns an

individual’s belief in their own ability to execute the behaviours to create a response,

! 36!

whereas optimism is more concerned with whether a positive outcome can be

expected. People may be optimistic for a variety of reasons that do not relate to their

own abilities to execute skills, they be optimistic because they “believe they are

immensely talented, because they are hard working, because they are blessed,

because they are lucky, because they have friends in the right places, or any

combination of these or other factors that produce good outcomes” (Carver &

Scheier, 2003, p. 77).

The stability of self-efficacy. Self-efficacy is a dynamic construct and the

review above clearly shows that people’s efficacy judgements change over time as

new information and experience are acquired. In fact, an individual’s judgement

about their efficacy to perform a task can be altered even while they are performing

the actual task (Gist & Mitchell, 1992). In this respect there is clearly a malleable,

state component to self-efficacy. In support of this, Bandura stated that “efficacy

beliefs should be measured in terms of particularized judgements of capability that

may vary across realms of activity, and under different situational circumstances”

(Bandura, 1997, p. 42).

However, Parker’s RBSE construct refers to a broader conceptualisation of

efficacy, and removes the requirement for efficacy judgements to be made about

particular realms of activity and particular situational circumstances. While she does

show that RBSE can be increased through organisational changes such as increased

job enrichment and increased quality of communication (Parker, 1998), the same

study also showed that RBSE had a test-retest correlation of .79 over a period of 18

months, indicating that there is a stable component of RBSE. Gist (1987), states that

“because the selection of high-performing individuals is important to organizations,

self-efficacy, as a predictor of performance may be helpful” (p. 479). This argument

! 37!

acknowledges the possible selection use of the stable component of self-efficacy,

although Gist also acknowledges that more research is needed to determine the

extent to which self-efficacy can be generalised across job situations.

Resilience

Although resilience has attracted attention in research for many years, its

definition remains elusive and the processes that comprise resilience are not yet well

understood (Jackson, Firtko, & Edenborough, 2007). It is broadly conceptualised as

an ability to withstand adversity or stress and, in accordance with this definition, it is

necessary for someone to encounter adversity in order for them to be able to

demonstrate resilience. Another conceptualisation of resilience is that it is

demonstrated by “flexible adaptation to the changing demands of stressful

experiences” (Tugade & Fredrickson, 2004, p. 320). These definitions suggest that

resilience is demonstrated in the ‘act’ of bouncing back from or adapting to adverse

circumstances rather than in carrying the belief that one is likely to be able to do so.

Early studies of resilience have attempted to identify factors that assist

individuals to thrive during adversity. These studies have identified cognitive factors,

individual competencies, intrapersonal and environmental factors that contribute to

resilience. Cognitive factors identified include self-efficacy (Millear, Liossis,

Shochet, Biggs, & Donald, 2007), optimism, intelligence, creativity, humour and a

“belief system that provides existential meaning, a cohesive life narrative, and an

appreciation of the uniqueness of oneself” (Tusaie & Dyer, 2004, p. 4). Other

competencies linked to resilience include a range of coping strategies, social skills

and educational abilities (Tusaie & Dyer, 2004). A key environmental factor

influencing resilience is social support (Rutter, 1985).

! 38!

Resilience focused upon children growing up in conditions of adversity has

been extensively studied in literature. Ann Masten, a leading scholar of resilience

within this context has defined resilience as “a class of phenomena characterised by

good outcomes in spite of serious threats to adaptation or development” (Masten,

2001, p. 228). It is clear from this definition that it is necessary for an individual to

have experienced significant threat to their development in order to be classified as

resilient. These threats may be due to genetic, environmental or experiential factors.

Some examples include being the child of a parent with schizophrenia, coming from

a low socio-economic background or exposure to violence.

Threats to normal development are commonly referred to as risk factors, with

their polar opposite being assets. Assets are factors which can protect an individual

against threats to development or adaptation. For example, poor parenting may be a

risk factor, but strong parenting an asset. Similarly a low level of education may be a

risk factor and a high level an asset. According to the concept of compensatory

effects, enough positive assets could offset the risk factors in a child’s life, thus

resulting in their resilience (Garmezy, Masten, & Tellegen, 1984). In accordance

with this theory of resilience, an individual can increase their resilience through

removing risk factors or increasing assets. In comparison to the other constructs

previously discussed (hope, optimism and self-efficacy), resilience is based more

upon negative than positive psychology; resilience is indicated more by the absence

of negative symptoms rather than the presence of positive ones.

Resilience as a more positive and organisationally relevant construct has been

studied in the POB literature. Within this literature, resilience has been defined as

“the developable capacity to rebound or bounce back from adversity, conflict, failure

or even positive events, progress and increased responsibility” (Luthans, 2002a, p.

! 39!

702). Luthans and colleagues have extended Masten’s view of resilience to the

workplace and adopted her view of risk factors and assets as central to resilience.

They note some examples of work place risk factors to be “macro-level external

threats such as economic instability, or micro-level internal threats such as

harassment or missing a career threatening deadline on a project” (Luthans,

Vogelgesang, & Lester, 2006, p. 29). Examples of workplace assets include

promotions, bonuses, recognition, or mentorship programs (Luthans, Vogelgesang, et

al., 2006, p. 29). They further argue that assets can be increased and risks decreased

by increasing an employee’s access to knowledge, skills and/or abilities or by

strengthening their social network.

Resilience and positive organisational outcomes. Before resilience

research was applied to organisational settings, many studies linked resilience to

better adaptive behaviour, particularly in the areas of social functioning, morale and

somatic health (Wagnild & Young, 1993). In today’s workplace in which there are

increasing levels of stress and decreasing amounts of recovery time, resilience is

becoming increasingly important (Luthans, Vogelgesang, et al., 2006). In spite of

this, studies of resilience in an organisational context are fragmented, limited and

non-systematic (Sutcliffe & Vogus, 2003). The majority of work covering resilience

in the workplace has been conducted by Luthans and colleagues when researching

the construct of PsyCap and this is discussed below.

The difference between resilience and hope, optimism and self-efficacy:

The key difference between resilience and three POB constructs reviewed above is

that whereas hope, optimism and self-efficacy can be classed as proactive constructs,

resilience is more reactive (Luthans, Vogelgesang, et al., 2006). That is, it is

necessary for a stressor or threat to be present in order for resilience to be displayed,

! 40!

whereas people can be hopeful, optimistic and self-confident without the presence of

a stressor or threat. Luthans and colleagues additionally proposed that the other

constructs may act as pathways to resilience, such that those who are hopeful,

optimistic and self-efficacious are more likely to exhibit resilience in times of

difficulty than those who are not. However, they also suggested that the inverse

relationship may occur, and that resiliency could serve to restore hope, optimism and

self-efficacy after a challenging experience.

The stability of resilience. Resilience has been measured using the resilience

scale published by Wagnild and Young (1993). This scale was constructed to

measure resilience as a personality trait and the authors of the scale cite data from an

unpublished study conducted by Killien and Jarret as evidence of the stability of the

construct. The study cited measured the resilience of pregnant and postpartum

women during pregnancy and at one, four, eight and 12 months post-partum. They

reported test-retest correlations ranging from .67 to .84 over this 18 month period (p

< .01), as evidence of the stability of resilience.

However, resilience has also been portrayed as a developable construct,

influenced by the balance of assets and risk factors present in an individual and their

environment. Schoon (2006), states that it is generally accepted that resilience is not

a static state, and this is supported by programs that enhance resilience in individuals

(Luthans, Avey, Avolio, Norman, & Combs, 2006; Luthans, Avey, & Patera, 2008;

Luthans, Vogelgesang, et al., 2006).

Psychological Capital

Although the constructs of hope, optimism, resilience and self-efficacy

arguably have conceptual independence, Luthans and colleagues also propose that

! 41!

there may be a common, underlying link which ties them together (Luthans, Avolio,

et al., 2007). They propose the concept of Psychological Capital, or PsyCap, which is

a second-order construct, formed by a combination of an individual’s hope,

optimism, self-efficacy and resilience. PsyCap has been defined as “an individual’s

positive psychological state of development and as characterised by: (1) having

confidence (self-efficacy) to take on and put in the necessary effort to succeed at

challenging tasks; (2) making a positive attribution (optimism) about succeeding now

and in the future; (3) persevering towards goals and, when necessary, redirecting

paths to goals (hope) in order to succeed; and (4) when beset by problems and

adversity, sustaining and bouncing back and even beyond (resiliency) to attain

success” (Luthans, Youssef, et al., 2007, p. 3). It can be thought of as a measure of

‘who you are’, in contrast to two other forms of capital known to be of importance in

predicting workplace performance. These are human capital or ‘what you know’, and

social capital, or ‘who you know’ (Luthans, Avolio, Walumbwa, & Li, 2005).

According to the definition of PsyCap and the discussion of each of the

PsyCap constructs above, it has been proposed that “those with high PsyCap tend to

be more determined, expend more effort, expect success, manoeuvre obstacles more

effectively, and bounce back from setbacks more readily” (Avey et al., 2008, p. 706).

Although these claims have not been fully investigated, recent research has shown

PsyCap to be a significant predictor of organisational commitment, workplace

performance and job satisfaction in a range of work settings and cultures (Larson &

Luthans, 2006; Luthans et al., 2005; Luthans, Youssef, et al., 2007). For example, in

a recent study (Luthans, Avolio, et al., 2007) PsyCap scores correlated .39 with job

satisfaction and .36 with affective organisational commitment. Regression analyses

additionally showed that PsyCap was the strongest contributor to predicting job

! 42!

satisfaction and affective organisational commitment when the personality constructs

of conscientiousness, extraversion and core-self evaluations were taken into account.

In these analyses PsyCap was able to account for 4% of the unique variance in job

satisfaction and 13% of the variance in affective organisational commitment. Using

samples from two different workplaces (a high-tech manufacturing firm and an

insurance firm), this study found correlations of .33 and .22 between PsyCap and

organisational performance measures. In the manufacturing firm performance was

assessed using “a sum of ratings based on quality and objective quantity of their

work on electrical subsystem designs including error and rejection rates, meeting the

schedule, complexity of assignment, and ability to work with peers” (Luthans,

Avolio, et al., 2007, p. 555). Performance ratings in the insurance firm were based on

the last month of performance of the participants and included objective data such as

the number of claims processed as well as the participants’ managers’ evaluation of

their overall performance. In light of findings such as these, PsyCap appears to be an

important construct to investigate within an organisational context.

There are two important claims that are made about the PsyCap construct.

The first is that the four base constructs combine synergistically rather than

additively. That is, “the whole (PsyCap) may be greater than the sum of its parts

(self-efficacy, optimism, hope and resiliency)”(Luthans, Youssef, et al., 2007, p. 19).

This is claimed to occur because the constructs may interact with each other in a self-

increasing manner. For example, hope and optimism are related to experiencing

positive emotions (Seligman, 2006; Snyder, 2002) and research has shown that

resilient people use positive emotions to rebound from stressful encounters (Tugade

& Fredrickson, 2004). Therefore, people higher in hope and optimism are more

likely to be resilient and are therefore more likely to experience success in stressful

! 43!

situations. Experiencing success in stressful situations may in turn build their self-

efficacy and resilience. High self-efficacy is linked with striving for higher goals,

which may be related back to higher hope levels. This is just one example of the

ways in which the constructs may be interrelated.

The second aspect of PsyCap, and a focal point of this research, is that

PsyCap is conceived to be a state-like rather than trait-like construct. That is, they are

argued to be “not as stable and are more open to change and development compared

with “trait-like” constructs such as the Big Five personality dimensions or core self-

evaluations, but importantly they also are not momentary states” (Luthans, Avolio, et

al., 2007, p. 544). This rather vague definition of the stability of PsyCap is the result

of continuum perspective of traits and states previously discussed.

The stability and measurement of PsyCap. In following the continuum

perspective, Luthans and colleagues define the PsyCap constructs as being ‘state-

like’, that is “relatively malleable and open to development” (Luthans, Avolio, et al.,

2007, p. 544), which they claim is more stable than positive states which they

describe as “momentary and very changeable; represents our feelings” (Luthans,

Avolio, et al., 2007, p. 544) but less stable that ‘trait-like’ constructs which they

describe as being “relatively stable and difficult to change; represents personality

factors and strengths” (Luthans, Avolio, et al., 2007, p. 544).

The PsyCap questionnaire (PCQ-24; Luthans, Youssef, et al., 2007) is

structured such that the first six items are adopted from Parker’s (1998) measure of

self-efficacy, items seven to twelve are adopted from Snyder et al.’s (1996) state

hope scale, items thirteen to eighteen from Wagnild and Young’s (1993) measure of

resilience and the final six items are from Sheier and Carver’s (1985) optimism scale.

Each of these scales have been validated and used extensively in research upon the

! 44!

constructs. The items used in the PCQ-24 have been adapted from the original scales

such that each item reflects the respondent’s thoughts or feelings about their work,

rather than their general approach to life. For example, the item from the optimism

scale (LOT; Scheier & Carver, 1985), “If something can go wrong for me, it will”,

was adapted to read “If something can go wrong for me work-wise, it will.” This was

similar to a methodology previously successfully used to adapt the hope scale to

measure hope in specific areas such as family life, romantic relationships and leisure

(Sympson, 1993), and there does not appear to be any problems with these minor

wording changes to the items.

However, the items were also adapted in order to ensure that the PCQ-24

targets PsyCap as a ‘state-like’ construct. The primary adaptation taken to achieve

this was to include initial instructions asking the respondent to describe themselves

“right now”. In addition, the authors make claim to having adapted the wording of

some items to make them more state relevant (Luthans, Avolio, et al., 2007; Luthans,

Youssef, et al., 2007). However, this adaptation of the items appears to be

questionable. There are two forms of items within the PCQ-24 that are considered

problematic for the measurement of a state; constant tendency items and future

conditional items.

The term constant tendency is used to describe those items that contain terms

such as ‘usually’, ‘always’ and ‘never’. These terms refer to a constant tendency to

feel, think or act in a particular way and should therefore not vary with time. In a

number of previous scales which have been developed to include both a trait and

state scale of a construct, a key differentiation between the two versions of the scales

is that the trait scale includes constant tendency terms such as ‘in general’ whereas

the state scales refers to shorter term or moment specific tendencies such as ‘right

! 45!

now’ (see for example Cepeda-Benito, Gleaves, Williams, & Erath, 2000; Hong,

1998; Spielberger et al., 1970; Spielberger, Jacobs, Russell, & Crane, 1983).

Although the PCQ-24 contains the initial instruction to rate how you are feeling

‘right now’, this does not make an item state appropriate if the item itself contains a

constant tendency term. Thus, the inclusion of constant tendency items in the PCQ-

24 is considered inappropriate for the measurement of a state.

The second form of items considered problematic for state measures are the

future conditional items. These are items that ask participants how they would feel if

they were confronted with a certain situation. For example, the item “when things are

uncertain for me at work, I usually expect the best” does not concern how the

respondent feels “right now”, but rather how they would feel at another point in time.

Through asking respondents to project their mind to themselves at another point in

time, this necessarily asks them to consider their more generalised, rather than

moment-specific feelings. Hong (1998), constructed a state-trait test anxiety scale

using this same reasoning. In this scale, Hong used past tense to assess the state

component of test-anxiety (how participants had felt at the specific moment they

were sitting a test), and used future tense in the trait items. The future conditional

items in the PCQ-24 are therefore thought to be more appropriate for the

measurement of a trait, rather than a state.

Research conducted with the PCQ-24, has shown that the scale has a test-

retest reliability of .52 over a period of two weeks (Luthans, Avolio, et al., 2007).

There is no strong evidence available to differentiate between whether this test-retest

reliability indicates that an individual’s level of PsyCap changes over time or if the

scale is simply unreliable. While research showing that PsyCap can be increased with

training (Luthans, Avey, et al., 2006; Luthans, Avey, et al., 2008) shows that it is

! 46!

possible for an individual’s level of PsyCap to change over time, this does not clarify

whether an individual’s level of PsyCap remains constant or changes over time

without intervention. It is quite possible that the test-retest reliability of the PCQ-24

falls into the ‘state-like’ range of .52 because it includes both state and trait

appropriate items, rather than because it is best conceptualised as a ‘state-like’

construct.

Given that each of the PsyCap constructs have been successfully measured as

both state and trait constructs, there may also be both state and trait components to

PsyCap. However, the PCQ-24 does not adequately measure a state due to it being a

mix of both state and trait appropriate items. In addition, as far as it is possible to tell

from the published literature, there has been no attempt to construct a measure of

PsyCap reflecting its trait like qualities and no systematic examination of its stability

over time. This is in spite of the wealth of evidence reviewed above which

demonstrates that each of the individual PsyCap components have both stable or trait

components and variable or state components. The current research aims to fill this

gap in our understanding of PsyCap. Filling this gap is important to enable

organisations to make optimal decisions as to whether to select individuals already

possessing the desired level of PsyCap into an organisation, if resources are better

directed at developing PsyCap in existing employees, or whether a combination of

the two can lead to optimal outcomes.

The research was conducted in three phases. The first phase involved an

examination of the assertion that many items in the PCQ-24 were unsuitable for

measuring a state and the construction of separate state and trait based scales to

measure PsyCap. Phase two tested the stability of these two scales over time. Finally,

in phase three the ability of the stable PsyCap component to predict the future

! 47!

performance of new employees to an organisation was assessed. Chapter two will

discuss phase one of the research.

! 48!

CHAPTER TWO: ASSESSMENT OF THE PCQ-24 AND DEVELOPMENT

OF THE GENERALISED AND MOMENT SPECIFIC PSYCAP SCALES

As discussed in the literature review, the PCQ-24 contains two forms of items

that appear to be problematic for the measurement of a state; the constant tendency

items that contain phrases such as ‘always’ and ‘usually’, and the future conditional

items that ask participants how they think they would feel if they were in a particular

situation. This chapter will describe the process undertaken to examine the above

assertion, and outline the development process used to develop two new scales

designed to separately measure both the state and trait components of PsyCap. The

scale designed to determine if there is a trait component to PsyCap has been termed

the Generalised PsyCap scale as its focus is upon general tendencies with respect to

PsyCap. The state based PsyCap scale, which assesses the way respondents feel at a

specific moment in time has been termed the Moment Specific PsyCap scale. States

have been measured using the reference period of how you are feeling ‘right now’ as

well as the reference period of ‘how you have been feeling over the last few weeks’.

In this research, the decision was made to use the Moment Specific reference of

‘right now’ in order to allow a clearer comparison to the PCQ-24 which uses the

‘right now’ reference period.

This chapter is divided into three phases. Phase one describes the item

generation phase for the Moment Specific and Generalised PsyCap scales. In phase

two, a rating process was conducted to assess whether the PCQ-24 items and the

items developed in phase one were more appropriate for measuring a state or a trait.

In phase three, the items, rating scale and instructions for inclusion in the Moment

Specific and Generalised PsyCap scale were selected.

! 49!

Phase One: Item development for the Moment Specific and Generalised PsyCap

scales

Phase one aimed to generate items for inclusion in the Moment Specific and

Generalised PsyCap scales which were worded in an appropriate manner to measure

states and traits. As the items in the PCQ-24 were drawn from well-validated studies,

and the measure itself has undergone a thorough validation process (Luthans, Avolio,

et al., 2007), the content of the items was not changed from the PCQ-24 items.

Rather, for the Moment Specific PsyCap scale the PCQ-24 items were adapted to

ensure that they were maximally suited for the measurement of a state and for the

Generalised PsyCap scale the PCQ-24 items were written so that they were

maximally suited for the measurement of a trait.

Method.

Item development for the Moment Specific PsyCap scale. There were three

key adaptations made to the PCQ-24 items to form the items for the Moment

Specific PsyCap scale. The constant tendency items were re-worded, the future

conditional items were reworded and the items not appropriate for the ‘right now’

reference period were changed as required. Additionally, in order to ensure that

respondents remained aware of the “right now” reference period as they completed

the survey, the words “Right now” were added to the beginning of each item.

The constant tendency items are those that refer to a constant tendency to

feel, think or act in a particular way and should therefore not vary with time. In order

to adapt these items the state-inappropriate terms usually, always and never were

removed from each item of the PCQ-24 that contained them. For example, the item

“I usually take stressful things at work in stride” was re-worded to read “Right now, I

am taking stressful things at work in stride”.

! 50!

Next the future conditional items, which ask participants how they would feel

if they were confronted with a certain situation, were re-written to reflect

respondents’ present feelings towards a past event. For example, the item “When I

have a setback at work, I have trouble recovering from it, moving on”, was re-

worded to read “Right now, I am having trouble recovering and moving on from a

previous setback at work.”

Finally, items that did not practically fit with the “right now or at this

moment” reference period for the scale were modified. This reference period refers

to what participants are doing at the exact moment that they are answering the

questionnaire. That is, they are answering the questionnaire. For many of the items

this reference period did not make sense. For example, in responding to the item “At

this time, I am meeting the work goals that I have set for myself” with an ‘at this

moment’ reference period, respondents must answer that they disagree unless their

work goal was to complete the survey. These items were re-phrased so that they

would fit with the reference period. For example the item “I am meeting the work

goals that I have set for myself” was re-phrased to read “Right now, I feel able to

meet the work goals that I have set for myself.”

Item development for the Generalised PsyCap scale. The PCQ-24 items

were also modified to form the Generalised PsyCap scale. These modifications

involved ensuring that each item reflected a constant feeling or behaviour. Thus,

items were modified such that each one contained a phrase such as “in general”,

“generally” or “usually” rather than “right now”, “at the present time”, or “at this

time”.

Results and Discussion.

! 51!

Following the methods described above two alternative items were written

for each item of the PCQ-24. One version of the item was written in a format

considered to be a better state item, and one a better trait item. Thus, there were three

alternative items for each of the original PCQ-24 items. These were the original item

and a re-worded Generalised and Moment Specific version of each item. These items

were rated in phase two to establish whether they were viewed as being appropriate

state or trait items.

Phase Two: Expert rating of the PCQ-24, Moment Specific and Generalised

PsyCap scale items.

In phase two a rating process was used to test the assertion that a number of

items in the PCQ-24 are more appropriate for measuring a trait rather than a state,

and to determine if the items written in phase one of this study were state or trait

appropriate items. It is common practice in survey development to use a rating

process to assess the content validity of items included in a scale. There are a number

of variations to the methods used in this process, but they generally involve

providing participants in a rating group with a definition of the construct to be

measured, and asking them to rate the extent to which each item is relevant to that

construct. This assessment aims to eliminate items that are not conceptually

consistent with the construct they are measuring (Hinkin, 1998).

In the current study the aim was not to assess the content validity of the

items (as they were drawn from previously validated scales), but to assess the

appropriateness of the wording of the items for measuring a state or a trait. However,

a similar methodology was applied, as it was considered to be an appropriate,

objective method through which the wording of the PCQ-24 items and the Moment

Specific and Generalised PsyCap items could be assessed.

! 52!

Method.

Participants. Hinkin (1998) suggests that in this early phase of scale

development “it may be appropriate to use a small sample of students as this is a

cognitive task not requiring an understanding of the phenomena under examination”

(p. 109). Recommendations for the number of raters required when selecting items

for a scale have varied, with estimates ranging from two to 20 (Grant & Davis,

1997). Therefore, being cautious, twenty post-graduate psychology students from the

University of Western Australia participated voluntarily in this rating process.

Procedure. The raters were given a definition of a state and a trait and then

asked to rate each item of the PCQ-24 and the two modified versions of each item

(from phase one) as to whether they should be considered a state or trait item. The

items were presented to the raters in a random order. The rating was a forced choice

decision, such that if the rater felt that the item could be appropriate for the

measurement of both a state and a trait, or, was appropriate for neither a state nor a

trait, they were required to indicate for which purpose they felt the item was more

suitable.

Results and Discussion.

Rating of the PCQ-24 items. The table below shows the percentage of raters

that classified each item of the PCQ-24 as a state or a trait appropriate item. It can be

seen that despite the PCQ-24 being constructed with the purpose of being a ‘state-

like’ measure, the majority of the items were classified as being more appropriate

trait than state items by the raters. For 21 of the 24 PCQ-24 items, over 50% of the

raters (60-100% of raters depending on item, see table 1 below) indicated that the

item was more suitable for measuring a trait than a state.

! 53!

Table 1.

The percentage of raters that rated each item of the PCQ-24 as being a better state or

trait item.

Item % State % Trait

I feel confident analysing a long term problem to find a solution. 15 85

I feel confident in representing my work area in meetings with management. 25 75

I feel confident contributing to discussions about the company’s strategy. 40 60

I feel confident helping to set targets/goals in my work area. 10 90

I feel confident contacting people outside the company (e.g., suppliers, customers) to discuss problems.

30 70

I feel confident presenting information to a group of colleagues. 30 70

If I should find myself in a jam at work, I could think of many ways to get out of it.

5 95

At the present time, I am energetically pursuing my work goals. 100 0

There are lots of ways around any problem. 15 85

Right now I see myself as being pretty successful at work. 100 0

I can think of many ways to reach my current work goals. 40 60

At this time, I am meeting the work goals that I have set for myself. 95 5

When I have a setback at work, I have trouble recovering from it, moving on. 10 90

I usually manage difficulties one way or another at work. 0 100

I can be “on my own,” so to speak, at work if I have to. 15 85

I usually take stressful things at work in stride. 0 100

I can get through difficult times at work because I’ve experienced difficulty before.

0 100

I feel I can handle many things at a time at this job. 40 60

When things are uncertain for me at work, I usually expect the best. 0 100

If something can go wrong for me work-wise, it will. 10 90

I always look on the bright side of things regarding my job. 0 100

I’m optimistic about what will happen to me in the future as it pertains to work. 40 60

In this job, things never work out the way I want them to. 30 70

I approach this job as if, “every cloud has a silver lining.” 20 80

! 54!

Rating of the modified Moment Specific PsyCap items. For the modified

Moment Specific items, there was 100% agreement on 22 out of 24 items that the

item reflected a state rather than trait. The item with the lowest percentage of ‘state’

ratings had 90% of respondents indicate that the item was appropriate to measure a

state. This was the item “Right now, I am able to think of many ways to reach my

current work goals.”

Rating of the modified Generalised PsyCap items. For the modified

Generalised PsyCap items, there was 100% agreement for 18 items that they

reflected trait like qualities. The item with the lowest agreement of ‘trait’ ratings had

80% of respondents indicate that the item reflected a trait. This was the item “In this

job I generally feel that I can handle many things at a time.”

Summary. The results from phase two support the assertion that many items

in the PCQ-24 are more appropriate for measuring a state than a trait. For three of the

four constant tendency items, all raters indicated that they thought the item was more

appropriate for measuring a trait than a state. For the final item, 70% of raters

indicated that they thought the item was more trait appropriate. There were two

future conditional items, for which 90% and 95% of raters indicated that they

thought the item was more trait appropriate. Interestingly, beyond these two

problematic forms of items, there were only three items for which over 50% of the

raters thought the item was more state than trait appropriate. Thus, overall the ratings

suggest that the items from the PCQ-24 may be more appropriate for measuring a

trait than a state. The high level of agreement amongst raters that the modified

Generalised PsyCap items were trait appropriate and that Moment Specific PsyCap

! 55!

items were state appropriate provides an initial indication that the modifications were

effective.

Phase Three: Selection of items, instructions and rating scales for the Moment

Specific and Generalised PsyCap scales.

The rating process carried out in phase two provided evidence that many of

the PCQ-24 items were more appropriate items for measuring a trait than a state, that

the Moment Specific PsyCap items could be considered appropriate state items and

that the Generalised PsyCap items could be considered appropriate trait items.

However, it did not provide comparative evidence about which version of each item

was considered to be the ‘best’ item for inclusion in the Moment Specific and

Generalised PsyCap scales. Thus a second rating process was used to select items to

be included in the Moment Specific and Generalised PsyCap scales which were to be

used for further validation research.

When selecting the items for inclusion in the scales it was also necessary to

consider the way that the items would fit with the scales’ instructions and rating

scales. Thus, the instructions and rating scale were selected before this rating process

was conducted. The instructions and response scales for the Generalised and Moment

Specific PsyCap scales were taken from the State-Trait Anxiety Inventory (STAI;

Spielberger et al., 1970) because this is a well established measure which is able to

differentiate between the state and trait components of the construct it measures,

anxiety. A number of other scales which target the state and trait components of a

construct have also used the instructions and rating scale of the STAI. Following the

format used for the STAI, the Moment Specific PsyCap measure instructs

participants to indicate how much each item describes themselves ‘right now’ or ‘at

this moment’. The response format is a four point Likert type scale (not at all to very

! 56!

much so). The Generalised PsyCap scale instructs participants to respond to each

item with respect to how often they generally feel the way described in the statement.

The response scale for the Generalised scale was similarly a four point scale ranging

from almost never to almost always.

Method.

Participants. Twenty post-graduate psychology students from the University

of Western Australia who had not participated in the previous rating exercise

participated in this item selection phase.

Procedure. A rating system was employed to decide which items were to be

included in the Generalised and Moment Specific PsyCap scales. In this phase, the

term state and trait were explained to each rater to ensure a common understanding.

They were additionally shown the instructions and response scale that would be used

for the Generalised and Moment Specific PsyCap scales. The raters were then shown

the three alternative forms of item one of the PCQ-24 (the original PCQ-24 item, and

the two alternative versions for each item created in phase one of this study). Each

alternative version was written on a separate card, and raters were asked to order

them along a continuum from which item would be best to include in the state based

scale, to the item that would be the best item for inclusion in the trait scale. The

experimenter made a note of which alternative version of each item was rated as best

state item, the mid-item and the best trait item. This process was repeated for each of

the PCQ-24 items in turn.

Results and Discussion

Table two shows the version of each item rated most frequently as being most

appropriate for measuring a state, and the percentage of raters that rated it as being

! 57!

the best state item. Table three shows the corresponding information for the trait

scale. These were the final items included in the Moment Specific and Generalised

PsyCap scales.

! 58!

Table 2.

Items selected as the most appropriate item for measuring a state and the

percentage of raters that selected that item as being the most appropriate for a state.

Item % selected item as best state item

Right now, I am feeling confident about my ability to analyse a long-term problem to find a solution.

95

Right now, I am feeling confident about my ability to represent my work area in meetings with management.

100

Right now, I am feeling confident about my ability to contribute to discussions about the company’s strategy.

95

Right now, I am feeling confident about my ability to help set targets/goals in my work area. 95

Right now, I am feeling confident about my ability to contact people outside the company (eg. suppliers, customers) to discuss problems.

95

Right now, I am feeling confident about my ability to present information to a group of colleagues.

100

Right now, I can think of many ways to solve a problem I am experiencing at work. 95

Right now, I feel that I am able to energetically pursue my work goals. 85

Right now I am feeling like there are lots of ways around any problem. 100

Right now, I am seeing myself as being pretty successful at work. 100

Right now, I am able to think of many ways to reach my current work goals. 95

Right now, I am feeling willing and able to meet the work goals that I have set for myself. 95

Right now, I am having trouble recovering and moving on from a previous set back at work. 95

Right now, I am feeling able to manage difficulties at work. 95

Right now, I am comfortable being “on my own” so to speak at work. 100

Right now, I am taking stressful things at work in stride. 90

Right now, I am feeling able to get through difficulties at work because I’ve experienced difficulty before.

95

Right now, I feel like I can handle many things at a time in this job. 100

Right now, I am expecting the best about uncertain situations at work. 100

Right now, I feel like if something can go wrong for me work-wise, it will. 95

Right now, I am looking on the bright side of things regarding my job. 100

Right now, I’m optimistic about what will happen to me in the future as it pertains to work. 100

Right now, I feel that things in this job work out the way I want them to. 90

Right now, I am approaching this job as if “every cloud has a silver lining.” 90

! 59!

Table 3.

Items selected as the most appropriate item for measuring a trait and the

percentage of raters that selected that item as being the most appropriate for a trait.

Item % selected item as best trait item

I generally feel confident analysing long-term problems to find solutions. 90

I generally feel confident representing my work area in meetings with management. 95

I generally feel confident contributing to discussions about the company’s strategy. 90

I generally feel confident helping to set targets/goals in my work area. 95

In general, I feel confident contacting people outside the company (eg. suppliers, customers) to discuss problems.

95

Generally, I feel confident presenting information to a group of colleagues. 95

I can generally think of many ways to solve problems that I experience at work. 90

I generally feel that I am energetically pursuing my work goals. 95

I generally feel that there are lots of ways around any problem. 85

I generally see myself as being pretty successful at work. 95

I can usually think of many ways to reach my work goals. 90

I usually feel that I am meeting the work goals I have set for myself. 95

I generally have trouble recovering from setbacks at work and moving on. 90

I generally manage difficulties one way or another at work. 85

I am generally comfortable being “on my own,” so to speak, at work if I have to be. 85

I generally take stressful things at work in stride. 90

I generally feel that I can get through difficult times at work because I’ve experienced difficulty before.

90

In this job I generally feel like I can handle many things at a time. 90

I usually expect the best when things are uncertain for me at work. 90

I generally feel that if something can go wrong for me work-wise it will. 95

I generally look on the bright side of things regarding my job. 90

I am generally optimistic about what will happen to me in the future as it pertains to work. 90

I generally feel as if things in this job work out the way I want them to. 90

I generally approach this job as if “every cloud has a silver lining.” 90

! 60!

There was a high level of inter-rater consistency with respect to which of the

three alternative forms of each item were the best state and trait items (the minimum

agreement level was 85%), thus no further analysis was required to select the items

for inclusion in the final Generalised and Moment Specific PsyCap scales.

General Discussion

The ratings for the PCQ-24 items supported the initial argument that many of

the items are more appropriate for measuring a trait rather than a state. This is a

problematic finding for the PCQ-24 which is posited to be a ‘state-like’ measure and

warrants the development of modified versions of this scale. In order to examine the

stability of the full spectrum of the PsyCap construct, separate state and trait scales

are required. The items for a state based scale should refer only to moment specific

feelings whereas the items for a trait based scale should refer to generalised

tendencies.

In the rating process for selecting items for inclusion in the Generalised and

Moment Specific PsyCap scales there was a high level of inter-rater consistency.

Although this provides an initial indication that the scales may be successful state

and trait scales, the stability and validity of these scales needs to be investigated

empirically. Chapter three discusses the first study conducted to validate the

Generalised and Moment Specific PsyCap scales.

! 61!

CHAPTER THREE: PSYCHOMETRIC ASSESSMENT OF THE

GENERALISED AND MOMENT SPECIFIC PSYCAP SCALES

Having completed the item generation and selection phases of scale

development as discussed in chapter two, the second phase according to Hinkin’s

(1998) guide to the development of survey measures, is questionnaire administration.

Within this phase he recommends that “the items should now be presented to a

sample representative of the actual population of interest, such as managers or

employees, with the objective of examining how well those items confirmed

expectations regarding the psychometric properties of the new measure... The new

items should be administered along with other established measures to examine the

“nomological network”- the relationship between existing measures and the newly

developed scales.” (p. 110). This chapter will describe the study that aimed to

achieve this step in scale development.

In this study a sample of employees from a single organisation completed the

Generalised and Moment Specific PsyCap scales along with other relevant scales on

two testing occasions which were separated by approximately two months. The data

collected were used to assess the (i) stability, (ii) validity and (iii) factor structure of

the newly created Generalised and Moment Specific PsyCap scales. The introduction

below explains the hypotheses regarding these three aspects of the scales.

Stability of Generalised and Moment Specific PsyCap

Previous research conducted using the PCQ-24 has reported a test-retest

correlation of .52 over a two week period (Luthans, Avolio, et al., 2007). In the

current study it was hypothesised that the Moment Specific PsyCap measure would

! 62!

be more variable (ie. have a lower test-retest correlation) than the PCQ-24, whereas

the Generalised PsyCap scale would have a higher test-retest correlation than the

PCQ-24 over time. McRae and Costa (1994) report that most personality scales have

short-term test-retest correlations ranging from .70 to .90. Thus, in the current study

this was the expected range within which the test-retest correlation of the

Generalised PsyCap scale would fall. As the Moment Specific scale was expected to

vary over time more than the PCQ-24, it was anticipated that the test-retest

correlation of the Moment Specific PsyCap scale would fall below .50.

Wright (2007), argues that test-retest correlations are insufficient evidence to

classify a construct as being a state or a trait. He argues that because correlation-

based evidence of stability can confirm only that the relative positions of individuals

on a construct have remained unchanged and not whether there have been absolute

changes, using only test-retest correlations can result in a construct which has

undergone considerable change (although equally in all participants of a study),

being declared as stable. Wright suggests two supplementary analyses to assess the

stability of a construct. These are to test for changes in the mean scores of a variable

across a whole sample as well as changes in the variance of the variable in the

sample.

While no single test can prove the stability of a construct, convergence of

evidence that there is a high test-retest correlation, invariance of means and

invariance of variance overtime can be seen as strong evidence of a trait. Conversely,

while a state would be expected to have a lower test-retest correlation, it is still

possible for a construct to vary while the sample means and variances remain

unchanged. Thus, although these may be examined, the test-retest correlation is

considered most important for distinguishing a state measure from a trait measure.

! 63!

Given these considerations, the Generalised PsyCap scale was required to

meet the following criteria in order to conclude that it reflected trait-like qualities; (i)

the test-retest correlation was required to be higher than .70, (ii) the mean scale score

across the whole sample would not change between the two testing periods and (iii)

the variance of the scale score for the whole sample would not change between the

two testing periods. The only criteria for the Moment Specific PsyCap scale to be

considered a state scale was that the test-retest was required to be less than .50.

Additionally, it was hypothesised that if the Moment Specific and Generalised

PsyCap scales measured a state and trait respectively, then the test-retest correlation

of the Generalised scale would be significantly higher than that of the Moment

Specific scale.

Validity

Concurrent and Predictive Validity. Previous research has shown that the

PCQ-24 is related to work performance and attitudes (Larson & Luthans, 2006;

Luthans, Avolio, et al., 2007). In the current study it was hypothesised that

Generalised PsyCap during the first measurement period would be significantly

related to performance, satisfaction and commitment at both the first and second

testing periods. This hypothesis was formed because the Generalised PsyCap scale

was expected to be concurrently correlated with the same constructs as the PCQ-24,

and to have the ability to predict these attitudinal outcomes into the future as it was

expected to have a consistent influence on these attitudes over time. In contrast, it

was hypothesised that Moment Specific PsyCap would be significantly related to

each of the outcome measures collected at the same sampling point but, to be less

predictive of future measures than the tait measure due to its instability in individuals

over time.

! 64!

Previous research using hierarchical regression analyses has found that

PsyCap is able to account for unique variance in the dependent variables of job

satisfaction and affective organisational commitment when Conscientiousness,

Extraversion and Core self-evaluations are taken into account (Luthans, Avolio, et

al., 2007). In this study the data collected were used to replicate this finding. The

ability of Generalised PsyCap to predict unique variance in job satisfaction, affective

commitment and performance when Conscientiousness, Extraversion and Core self-

evaluations were included in the regression model was examined. It was

hypothesised that Generalised PsyCap at time one would account for significant

variance in the prediction of each of the dependent variables.

Convergent and discriminant validity. In order to determine the convergent

and discriminant validity of the PCQ-24 Luthans, Avolio et al. (2007) compared

scores on the PCQ-24 to age, education, Agreeableness, Openness to Experience,

Neuroticism, Extraversion, Conscientiousness and Core Self-Evaluations. In the

current study the relationships between the above listed variables and the Moment

Specific and Generalised PsyCap scales were examined. It was hypothesised that the

results for the current study would reflect those obtained by Luthans et al. as the

changes made to the wording and rating scales of the Generalised and Moment

Specific PsyCap scales was predicted to alter the variability over time but not the

fundamental nature of the constructs being measured. Luthans, Avolio et al. found no

significant correlation between PsyCap and age (r= .01), Agreeableness (r= .06) or

Openness to experience (r= -.10), a small but significant negative correlation

between PsyCap and Neuroticism (r= -.12), a moderate positive relationship between

PsyCap and Extraversion (r= .36) and Conscientiousness (r= .39) and a strong

positive relationship between PsyCap and Core Self-Evaluations (r= .60). Finding

! 65!

correlation coefficients of a similar magnitude in the current study will indicate that

the modifications made to the PCQ-24 to form the Generalised and Moment Specific

PsyCap scales has not affected the construct being measured by the scales. If the

correlation coefficients are significantly different, this provides an indication that the

construct being measured was affected by the modifications made to the scales. This

would mean that full validity studies should be conducted for the new scales, rather

than relying upon the studies conducted upon the PCQ-24.

Factor Structure of Generalised and Moment Specific PsyCap. Previous

research, which has been conducted using a variety of samples, has found that of the

models tested, the best fitting factor structure for the PCQ-24 is one that consists of

one factor for each component of PsyCap (hope, optimism, resilience and self-

efficacy), with those four dimensions linked to form a higher-order factor, PsyCap

(Luthans, Avolio, et al., 2007). This same factor structure was expected to be found

to be the best fitting to both the Generalised and Moment Specific PsyCap scales in

the current study, indicating that the changes made to the PCQ-24 did not change the

factor structure of the scales.

Method

Participants

Data were collected from employees of a not-for-profit organisation in

Western Australia. Questionnaire items were sent to all employees of the

organisation (N= 479). One hundred and sixty three employees completed the

survey, representing a 33% response rate. This sample included 42 (25.76%) males

and 121 (74.23%) females. There was a much higher response rate from the female

(49.59%) than the male (17.87%) employees of this organisation. Participants in this

! 66!

sample ranged from 19 to 76 years of age (mean age= 41.96, S.D.= 12.34). Other

than the gender statistics, there were insufficient data available from the organisation

to determine if the respondents systematically differed from the non-respondents.

To collect the second time sample of data, the questionnaires were sent to all

employees of the organisation a second time. Eighty seven employees responded to

the second questionnaire, representing an 18.16% response rate from the entire

organisation. In this sample there were 19 (21.84%) males and 67 (77.01%) females;

one participant did not indicate their sex. This sample ranged from 19 to 64 years of

age (mean age = 42.14, S.D.= 11.86). In total, only 44 employees from the

organisation (or 26.99% of respondents from the first time sample) completed the

questionnaires on both occasions. This sub-set of the sample ranged from 19 to 64

years of age (mean= 39.84, S.D.= 11.94). There were 8 (18.2%) males and 36

(81.8%) females. Those that responded on two occasions were significantly less

extraverted than those that responded on only one occasion, but did not differ from

those that responded on one occasion only in terms of age, gender, Core Self-

Evaluations, Agreeableness, Conscientiousness, Neuroticism, Openness to

Experience, Generalised or Moment Specific PsyCap (as measured at time one).

Measures

PsyCap measures. The Generalised and Moment Specific PsyCap scales

were as described in chapter two. As discussed in more detail below, Cronbach’s

alpha was above .90 at each testing period.

Concurrent and predictive validity measures.

Job satisfaction. Satisfaction was measured using a five-item job satisfaction

scale which Judge, Bono and Locke (2000) adapted from a measure developed by

! 67!

Brayfield and Rothe (1951). Responses to the scale were indicated on a 1 (strongly

disagree) to 7 (strongly agree) scale. The five item measure was selected rather than

Brayfield and Rothe’s 14 item measure as it has been shown to have a high level of

reliability (α= .89; Judge, Bono, & Locke, 2000), with the added advantage of shorter

length. Participants in the study were asked to complete a number of different

questionnaires on two separate occasions, so shorter scales were considered

advantageous. Cronbach’s alpha for this scale was above .80 at each collection

period.

Affective Organisational Commitment. Affective organisational commitment

was measured using Allen and Meyer’s (1990) eight item scale. Example items from

this scale include “I would be very happy to spend the rest of my career with this

organisation” and “I feel as if this organisation’s problems are my own.” The scale

uses a 1 (strongly disagree) to 5 (strongly agree) response scale. For this scale

Cronbach’s alpha was greater than .75 at both collection periods.

Performance. Performance was assessed using the individual task behaviour

items from Griffin, Neal and Parker’s (2007) measure of work performance. This

nine-item scale asked respondents to indicate how often they had carried out

behaviours which can be considered as key performance indicators during the last

week. The response scale was a five-point scale ranging from very little to a great

deal. This scale demonstrated a high level of internal consistency in the current study

(α≥ .85 for both collection periods).

Convergent and discriminant validity measures.

Core self-evaluations (CSES). Judge, Locke & Durham (1997) introduced the

concept of CSES, a higher order latent construct comprised of self esteem,

! 68!

generalised self-efficacy, neuroticism and locus of control. CSES was measured

using the 12-item scale constructed by Judge, Erez, Bono and Thoresen (2003). In

the current study α = .82.

Big Five Personality Domains. The ‘big five’ personality dimensions of

Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness to

Experience were measured using the brief, Ten-Item Personality Inventory (TIPI;

Gosling, Rentfrow, & Swann Jr., 2003). Each item of the scale consists of two

adjectives which describe one of the Big 5 personality domains. Participants indicate

on a 1 (disagree strongly) to 7 (agree strongly) scale the extent to which they think

the pair of adjectives describe how they see themselves. There are two items for each

of the Big 5 constructs. The scale was selected in order to allow the comparison of

the Generalised and Moment Specific PsyCap scores to the Big 5 personality

dimensions using the same scale that Luthans, Avolio et al., (2007) used to examine

the convergent and discriminant validity of the PCQ-24. However, as the scale uses

only two items to measure each personality dimension, the internal consistency for

each dimension was not always acceptable in the current study. Cronbach’s alpha

was equal to .65, .05, .62, .58 and .24 for the Extraversion, Agreeableness,

Conscientiousness, Neuroticism and Openness to Experience scales respectively.

These low reliabilities indicate that any results based on this scale must be

interpreted with caution. In the current study the decision was made only to include

the variables with an internal reliability greater than .60; thus the Agreeableness,

Neuroticsm and Openness to Experience scales were excluded from analyses.

Procedure

! 69!

Participants were invited to participate in this study at two time periods.

During the first period, the questionnaires of interest to this study (listed above) were

included in a larger scale organisational survey being conducted by the researcher.

The surveys were sent to all employees’ home addresses. Participants were invited to

complete the survey within a three week period and return it by mail to the

researcher. During this three week period two reminder emails were sent to

employees and posters raising awareness of the survey were placed around the

workplace. In this survey participants provided some demographic information and a

unique codename of their choice to allow their data to be linked with future surveys.

The questionnaires of interest to this study were interspersed amongst other scales in

the large organisational survey, but the order in which they were presented was as

follows; satisfaction, performance, affective organisational commitment, CSES,

Generalised PsyCap, Moment Specific PsyCap, TIPI.

A second survey was sent to all employees’ home addresses eight weeks after

the first survey. This survey contained the following scales (listed in order of

inclusion); satisfaction, affective commitment, performance, Generalised PsyCap,

Moment Specific PsyCap and then some demographic information including the

codename provided in the first survey. Employees were again invited to return their

completed surveys by post within a three week period. A reminder email was sent to

all employees to remind them to complete the survey approximately half way

through this three week period.

Analytical Strategy

Analyses were conducted to evaluate the (i) factor structure, (ii) reliability

and (iii) validity of the Generalised and Moment Specific PsyCap scales.

! 70!

Factor Structure. Confirmatory factor analysis, conducted using SPSS

AMOS version 17, was used to compare four competing models of the underlying

structure of the Generalised and Moment Specific PsyCap scales. Similarly to

Luthans et al., (Luthans, Avolio, et al., 2007), this study used the procedures

recommended by Hinkin (Hinkin, 1995). The models tested were (i) a single factor

model, (ii) a four factor (self-efficacy, hope, resilience and optimism) model with the

six relevant items from the scale loading onto each of the uncorrelated factors, (iii) a

four factor (self-efficacy, hope, resilience and optimism) model with the six relevant

items loading onto each of the correlated factors and (iv) a model with the same four

factors linked by a higher order PsyCap factor. The error variances of the items were

not correlated with each other in any of the models. It was predicted that the four

factors of model three would be highly correlated, and that this high degree of inter-

correlation could be explained by a single higher order PsyCap factor. Thus, the

hypothesis was that the higher order model (model four) would be the best fit for the

data.

The maximum likelihood method of estimation was used for the analyses as

there was no evidence of significant skewness or kurtosis within the questionnaire

items. In order to allow comparison of the models the following statistics were

calculated (with acceptable levels for each statistic in parenthesis; chi-square (χ2, not

significant, Byrne, 2001) with corresponding degrees of freedom, standardised root

mean residual (SRMR; <.08, Hu & Bentler, 1999), Root-Mean-Square Error of

Approximation (RMSEA; <.06, Hu & Bentler, 1999), Comparitive Fit Index

(CFI>.95, Hu & Bentler, 1999) and Parsimonious Normed Fit Index (PNFI; >.5;

Mulaik et al. (1989) reported that parsimony-based measures typically have lower

acceptable values than relative and absolute measures, with .50 or greater deemed

! 71!

acceptable). There is no common agreement about which measures of fit should be

reported (Meyers, Gamst, & Guarino, 2006), but the statistics reported were selected

to cover absolute fit, relative fit and parsimony of fit and to allow a direct

comparison with the fit statistics for the PCQ-24 reported by Luthans, Avolio et al.

(2007).

Reliability. The two important assessments for reliability were the internal

consistency and the stability of the scales over time. Internal reliability was assessed

by calculating Cronbach’s alphas for each PsyCap component separately, as well as

for the whole scale combined. This was calculated separately for the Generalised

and Moment Specific PsyCap scales, for each of the two testing occasions that they

were completed.

The stability of the PsyCap scales was tested using three methods as

recommended by Wright and Quick (2009). Firstly, the test-retest reliabilities of the

measures were determined. Next, a paired samples t-test was used to test for

invariance of the means over time. The variance of variance was tested using

Levene’s test.

Finally, the Pearson-Filon test with the Steiger (1980) modification was used

to test the hypothesis that the Generalised PsyCap scale would have a higher test-

retest correlation than the Moment Specific PsyCap scale.

Analyses focused only upon the higher order PsyCap factor, rather than the

four subscales (hope, optimism, self-efficacy and resilience), as the primary aim of

this research was to determine whether this scale could be classified as a state or

trait.

! 72!

Validity. Analyses were conducted to assess the (i) concurrent, (ii) predictive

and (iii) convergent and discriminant validity of the Moment Specific and

Generalised PsyCap scales.

Concurrent validity was assessed in terms of the correlation between the

PsyCap scale and the proposed job related outcomes of PsyCap (job satisfaction,

affective commitment and performance) measured during the same testing session

(e.g. Moment Specific or Generalised PsyCap at time one and job satisfaction at time

one, or Moment Specific or Generalised PsyCap at time two and job satisfaction at

time two). Using the measures taken at the same point in time may cause an inflated

correlation coefficient due to common method variance, but was considered to be the

most appropriate measure to use as the Moment Specific PsyCap scale was expected

to fluctuate too much over time for the measures of PsyCap and the attitudinal

outcomes to be separated in time. This is because if a scale fluctuates frequently over

time, it should not have predictive validity (due to its changing nature) and would

therefore only be expected to demonstrate a relationship to constructs measured at

the same time period. It was hypothesised that the Moment Specific PsyCap scale

would be more highly correlated to concurrent measures of the outcomes

(satisfaction, commitment and performance) than the Generalised PsyCap scale. This

hypothesis was tested using the method proposed by Dunn and Clark (1969, 1971),

as outlined by Meng, Rosenthal and Rubin (1992) to compare the correlation

coefficients the Moment Specific and Generalised PsyCap scales had with the job

related outcomes.

The predictive validity of the PsyCap scales was assessed in two ways.

Firstly, the correlation coefficients between Generalised and Moment Specific

PsyCap at time one and each of the job related outcomes (job satisfaction, affective

! 73!

commitment and performance) at time two were calculated. Next, the unique

variance that PsyCap could account for in predicting job satisfaction, commitment

and self-rated performance when Core Self-Evaluations, Extraversion and

Conscientiousness were also included in the model was examined using hierarchical

regression analyses. In these analyses, Conscientiousness, Extraversion and Core

Self-Evaluations were entered into block one of the analysis, and Generalised

PsyCap at time one was entered into block two. Moment Specific PsyCap was not

included in this analysis as when it was entered into the regression the Variance

Inflation Factor (VIF) was well above the recommended level of five, which is an

indicator of serious multicollinearity (Menard, 1995). A separate analysis was not

run for Moment Specific PsyCap as it was not hypothesised to exhibit predictive

validity.

This study aimed to assess convergent and discriminant validity by

correlating the Generalised and Moment Specific PsyCap scores with the same set of

variables used to determine convergent and discriminant validity by Luthans et al.

(2007). These were age, Agreeableness, Openness to Experience, Neuroticism,

Extraversion, Conscientiousness and Core Self-Evaluations. However, in the current

study, Agreeableness, Neuroticism and Openness to Experience were omitted from

these analyses due to the low internal consistency of these TIPI subscales. The

convergent and discriminant validity analyses were conducted using the PsyCap data

collected during the first collection period. Although the TIPI components had low

internal reliability, the correlations between these scales and the PsyCap scales have

been reported without correcting for attenuation to allow a comparison of data from

the current study to the data of Luthans, Avolio et al. (2007). The authors of this

study similarly reported that the low reliability of the TIPI was a problem but did not

! 74!

correct for it. However, given the low internal reliability, it is appropriate to correct

for attenuation. Therefore, the correlations between the TIPI scales and the PsyCap

scales were also corrected for attenuation using the formula published by Spearman

(Spearman, 1904).

Results

Factor Structure.

Table 4 shows the fit statistics for the four alternative models of the Generalised and

Moment Specific PsyCap scales separately.

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Table 4.

Fit indexes for the Generalised and Moment Specific PsyCap models.

Model #

Model Description χ2 df p SRMR RMSEA CFI PNFI CAIC ECVI

Generalised PsyCap

1. 1 factor 736.64 252 <.001 .079 .101 .776 .638 1036.75 4.38

2. 4 uncorrelated factors 748.31 252 <.001 .296 .102 .771 .633 1048.42 4.44

3. 4 correlated factors 409.15 246 <.001 .059 .059 .925 .742 746.77 2.72

4. 4 first order factors, 1 second order factor

418.32 248 <.001 .062 .060 .921 .745 743.44 2.75

Moment Specific PsyCap

1. 1 factor 872.39 252 <.001 .081 .116 .758 .633 1170.71 5.29

2. 4 uncorrelated factors 964.36 252 <.001 .340 .124 .722 .603 1262.68 5.79

3 4 correlated factors 552.02 246 <.001 .063 .082 .881 .718 887.62 3.61

4. 4 first order factors, 1 second order factor

555.35 248 <.001 .064 .082 .880 .723 878.52 3.60

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The fit statistics reported above show that models three and four were the best

fitting to the data. These are the models comprised of the four correlated first order

factors of self-efficacy, hope, resilience and optimism (model three), or the

uncorrelated first-order factors linked by the second order factor, PsyCap (model

four). There was very little difference in the fit between these two models. In both of

these models, the SRMR fit statistics reached the targets of being below .08 for both

the Moment Specific and Generalised PsyCap scales and the PNFI also fell into the

acceptable range. The CFI fell marginally below .95 in both models of the

Generalised PsyCap scale, and further below the .95 criterion for the Moment

Specific scale. Similarly, the RMSEA statistic met the criteria of being below .06 for

the Generalised but not the Moment Specific PsyCap scale. For both scales, the

significant χ2 statistic indicates some problems with the fit of the models.

As the fit statistics reported above were insufficient to distinguish between

models three and four, Bozdogan’s (1987) consistent version of Akaike’s (1987)

Information Criterion (CAIC) and the Expected Cross-Validation Index (ECVI) were

also calculated to attempt to distinguish between the two models. The CAIC

addresses the parsimony of fit (taking sample size into account) and the ECVI

addresses the likelihood that the model fit would be cross-validated across similar

sized samples from the same population (Byrne, 2001). When comparing models,

lower values for the CAIC and the ECVI indicate superior fit. The CAIC was lower

for model four than model three in both cases, which indicates that model four is the

superior fitting. The ECVI supported this suggestion for the Moment Specific Model

(as ECVI was lower for model four than three), but not for the Generalised PsyCap

model (in which the ECVI was lower for model three). However, the χ2 statistics

were marginally lower for model three than model four for both Generalised and

77##

Moment Specific scales. In balance, there is insufficient evidence to differentiate

between the model with four correlated factors and the model with the four factors

linked by a higher order PsyCap factor, but it can be concluded that neither model

provides a strong fit to the data. The modification indices were examined to

determine if there was a model that would better fit the data. However, the suggested

modifications did form a meaningful model so are not presented here.

Of note, the fit statistics for the higher order model reported in the current

study are quite similar to those reported for the same model fit to the PCQ-24 by

Luthans, Avolio et al. (2007). For their employee sample Luthans, Avolio et al.

reported a RMSEA = .048 (= .060 in the Generalised model and .082 in the Moment

Specific model in the current study), CFI = .924 (= .921 in the Generalised model

and .880 in the Moment Specific model in the current study) and SRMR = .056 (=

.062 in the Generalised model and .064 in the Moment Specific model in the current

study). The lack of difference in the fit of the Generalised and Moment Specific

PsyCap scales and the PCQ-24 to the higher order PsyCap mode indicate that it can

be concluded that the current adaptations to the PCQ-24 to make it a pure state and

pure trait scale have not affected its factor structure. Further analyses in this study

have been conducted with the higher order factor, to allow comparison with previous

studies using the PCQ-24 which have reported the relationships between the higher

order PsyCap factor and other variables.

Reliability

Internal consistency of Generalised and Moment Specific PsyCap.

Cronbach’s alphas for each PsyCap component separately, as well as for the measure

combined for each testing occasion are reported in Table 5. All scales demonstrated

78##

acceptable reliability (α > .70) on both test occasions. This allows for the conclusion

that the changes to the wording of the items in the PCQ-24 to create the two scales

did not detrimentally affect their reliability.

Table 5.

Cronbach’s alpha for each PsyCap component and the full Generalised and

Moment Specific PsyCap scales at each testing time.

Self-efficacy

Hope Resilience Optimism PsyCap

Generalised PsyCap

Time 1 .835 .894 .801 .811 .933

Time 2 .868 .884 .781 .760 .925

Moment Specific PsyCap

Time 1 .872 .882 .819 .832 .940

Time 2 .846 .907 .808 .835 .942

Stability of Generalised and Moment Specific PsyCap.

The test-retest reliability of the Generalised PsyCap scale was .711. The

paired samples t-test (two tailed) showed a trend towards difference but no

significant differences in the means of the Generalised PsyCap scale over the two

assessment periods, t(41)= 1.755, p= .087. Additionally, Levene’s test indicated that

there was homogeneity of variance. Thus, the Generalised PsyCap scale met all three

criteria set to conclude that the scale measured a trait; (i) the test-retest correlation

was above .70, (ii) the mean scale scores across the whole sample would not change

between testing periods and (iii) the variance of the scale scores for the whole sample

79##

would not change between the two testing periods, providing strong evidence that the

Generalised PsyCap scale measures a trait component of PsyCap.

For the Moment Specific scale the test-retest reliability across the two time

periods was .571. The paired samples t-test showed no significant differences in the

means of the scale at the two time points, t(40)= .761, p= .451. Levene’s test

indicated that there was homogeneity of variance. The criterion for the Moment

Specific PsyCap scale to be classified as a state was that the test-retest reliability was

below .50. Therefore the current results suggest that the Moment Specific PsyCap

scale does not measure a state component of PsyCap.

Relative Stability of Generalised vs Moment Specific PsyCap. The Pearson-

Filon test with the Steiger (1980) modification showed that there was no significant

difference between the test-retest correlation of the Generalised PsyCap scale

compared to that of the Moment Specific PsyCap scale (Z= 1.10, p= .272). This

finding provides additional evidence that the Moment Specific PsyCap scale

exhibited trait like qualities.

Validity

Concurrent Validity. The correlations between the two PsyCap scales and job

satisfaction, affective commitment and self-rated performance at the time matched

periods (e.g. PsyCap at time one and satisfaction at time one or PsyCap at time two

and satisfaction at time two) are shown in table six below. The PsyCap scores for

each scale were calculated by averaging the scores of all items included in that scale.

The hypothesis that the PsyCap scales would be related to concurrent measures of

job satisfaction, affective organisational commitment and performance were only

partially supported. Interpreting the effect sizes according to Cohen (1992), the

80##

correlations between the PsyCap scales and job satisfaction and self-rated

performance at the corresponding time periods generally suggest that PsyCap (both

Generalised and Moment Specific) is moderately to strongly related to immediate job

satisfaction and self-rated performance. However, there were only weak (and non-

significant) relationships between the PsyCap scales and affective organisational

commitment.

Comparison of the correlations between the two PsyCap scales and the

outcome measure at the time matched period (i.e. comparing the .279 correlation

between Generalised PsyCap at time one and job satisfaction at time one with the

.224 correlation between Moment Specific PsyCap at time one and job satisfaction at

time one) showed that there were no significant differences between the correlations

of the Moment Specific and Generalised scales with any of the outcome measures.

Table 6.

Correlation coefficients for Generalised and Moment Specific PsyCap with

Job Satisfaction, Affective Organisational Commitment and Self-rated Performance

at time matched periods.

Job Satisfaction (at time matched period)

Affective Commitment (at time matched period)

Self-rated Performance (at time matched period)

Generalised PsyCap

Time 1 .279** .046 .389**

Time 2 .586** .219* .565**

Moment Specific PsyCap

Time 1 .224** .104 .339**

81##Time 2 .507** .242* .577**

* p < .05

** p < .01

Predictive validity of Generalised and Moment Specific PsyCap. The

correlations between Generalised and Moment Specific PsyCap measured at time

one, with the outcome measures of job satisfaction, affective commitment and self-

rated performance are shown in tables seven, eight and nine. These tables show that

both the Generalised and Moment Specific PsyCap scales were significantly

correlated with future reported levels of job satisfaction (r= .452 for Generalised

PsyCap and r= .307 for Moment Specific PsyCap), and self-rated performance (r=

.332 for Generalised PsyCap and r= .364 for Moment Specific PsyCap). The PsyCap

scales were not significantly correlated with affective organisational commitment (r=

.260 for Generalised PsyCap and r= .182 for Moment Specific PsyCap).

The results for the regression analyses conducted to determine the unique

variance that Generalised PsyCap could account for in predicting job satisfaction,

affective commitment and self-rated performance when Conscientiousness,

Extraversion and Core Self-Evaluations were also included in the model are shown

in tables 10 and 11. It was found that Generalised PsyCap did not significantly

predict the dependent variables when the personality variables were already taken

into account. However, as can be seen in Table 11, it was also generally found that

the other personality dimensions did not significantly predict the dependent variables

either. Of note however, Generalised PsyCap was the strongest predictor of self-rated

performance and the second strongest predictor of job satisfaction in the regression

models including extraversion, conscientiousness, core self-evaluations and PsyCap.

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Table 7.

Correlations between Generalised and Moment Specific PsyCap (at time one) and

job satisfaction.

1 2 3 4

1. Generalised PsyCap –Time 1 1.0

2. Moment Specific PsyCap-Time 1

.850** 1.0

3. Satisfaction- Time 1 .279** .224** 1.0

4. Satisfaction- Time 2 .452** .307 .706** 1.0

* p < .05

** p < .01

Table 8.

Correlations between Generalised and Moment Specific PsyCap (at time one) and

affective organisational commitment.

1 2 3 4

1. Generalised PsyCap –Time 1 1.0

2. Moment Specific PsyCap-Time 1

.850** 1.0

3. Commitment- Time 1 .046 .104 1.0

4. Commitment- Time 2 .260 .182 .260 1.0

* p < .05

** p < .01

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Table 9.

Correlations between Generalised and Moment Specific PsyCap (at time one) and

performance.

1 2 3 4

1. Generalised PsyCap –Time 1 1.0

2. Moment Specific PsyCap-Time 1

.850** 1.0

3. Self-rated Performance- Time 1

.389** .339** 1.0

4. Self-rated Performance- Time 2

.332* .364* .433** 1.0

* p < .05

** p < .01

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Table 10.

Coefficient of determination and change in coefficient of determination for Block One (includes Conscientiousness, Extraversion and Core self-evaluations), and Block Two (addition of Generalised PsyCap at time 1) regressed onto job satisfaction, affective organisational commitment and self-rated performance (at time two).

Job Satisfaction Affective Commitment Self-rated Performance

R² ∆R² R² ∆R² R² ∆R²

Block 1 .288 .151 .100

Block 2 .310 .022 .152 .058 .168 .068

* p < .05

** p < .01

Table 11.

Beta weights for Conscientiousness, Extraversion, Core Self-evaluations and Generalised PsyCap at time 1 regressed onto job satisfaction, affective organisational commitment and self-rated performance (at time two).

Job Satisfaction

Β

Affective Commitment

β

Self-rated Performance

β

Conscientiousness .115 -.176 .204

Extraversion .402* .090 -.187

Core self-evaluations -.093 .374 -.104

Generalised PsyCap- Time 1 .251 -.047 .447

* p < .05

** p < .0

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Convergent and discriminant validity. The convergent and discriminant

validity assessments are shown in Table 12. There was a small but significant

relationship between PsyCap and age, medium correlations between PsyCap and

extraversion and conscientiousness and large correlations between PsyCap and core

self-evaluations. When corrected for attenuation, the correlations between PsyCap

and extraversion were .461 for the Generalised PsyCap scale and .324 for the

Moment Specific Scale. The correlations between conscientiousness and PsyCap

when corrected for attenuation were .496 for the Generalised and .494 for the

Moment Specific PsyCap scales.

These results are mixed in their support for the hypotheses concerning

PsyCap’s relationships with other variables. The correlations between PsyCap and

extraversion, conscientiousness and core self-evaluations were as hypothesised,

while the correlations between PsyCap and age, although not large in magnitude (r=

.268 and .289) appear to be higher than those reported by Luthans, Avolio et al

(2007). Taken together these findings suggest that varying the phrasing and reference

period of the PCQ-24 has not significantly changed the construct that it assesses.

However, replication of these findings is required due to the unexpected correlation

with age and the unreliability of the TIPI scales in the current study.

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Table 12.

Correlations of Generalised and Moment Specific PsyCap (at time one) with other

variables. The internal reliability of each scale is shown on the diagonal.

1 2 3 6 7 8 Mean (SD)

1. PsyCap Generalised (.93) 3.151 (.458)

2. Moment Specific PsyCap

.850** (.94) 2.095 (.320)

3. Age .289** .268** 41.985 (12.339)

6. Extraversion .359** .253** .019 (.65) 4.945 (1.424)

7. Conscientiousness .377** .377** .330** .112 (.62) 6.298 (.759)

8. Core Self-Evaluations .651** .548** .196* .275** .280** (.82) 3.803 (.507)

* p < .05

** p < .01

Discussion.

Luthans, Avolio et al. (2007) have argued that PsyCap is a ‘state-like’

construct, but to date, they have not attempted to construct a scale to measure

PsyCap as either a pure state or pure trait. Thus, this study has provided evidence that

if the items of a PsyCap scale are phrased to measure a stable construct, it is possible

to obtain a stable measurement. This, coupled with the scales’ relationships with job

satisfaction and self-rated performance, indicate that it may be an appropriate

employee selection tool. The study has also extended upon initial research of the

PCQ-24, as in initial research the stability of the scale has been assessed using only

test-retest reliabilities (Luthans, Avolio, et al., 2007). Wright and Quick (2009)

discuss that as a test-retest reliability only assesses the relative rather than absolute

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positions of individuals in a group, test-retest analyses should always be

supplemented by an assessment of changes in mean and variance over time. These

aspects were assessed in the current study, and all converged upon the conclusion

that the Generalised PsyCap scale measures a trait construct.

In terms of predictive validity, it must be noted that although Generalised PsyCap

was the strongest and second strongest predictor of self-rated performance and job

satisfaction respectively, the beta weights did not reach significance in the regression

analyses. The non-significance in these analyses may reflect a power issue. Power

analysis conducted using G*Power, indicated that in order to detect a medium effect

size with a power of .80, a sample size of 85 was required. In order to detect a large

effect size a sample of 40 was required. As only 44 participants completed the

surveys at both times one and two, the study was only sufficiently powered to detect

large effect sizes. Thus, it is necessary to conduct further research with a larger

number of participants before more conclusive findings concerning the predictive

validity of the Generalised PsyCap scale may be drawn.

An additional finding of the current study was that affective commitment was not

related to Generalised PsyCap as hypothesised. A possible explanation for this

finding involves the nature of the workplace from which the sample was drawn. The

employees were drawn from a not-for-profit organisation concerned with helping

people living with a permanent disability. Therefore, affective commitment may

have been heavily influenced by the nature of the work they performed, and the

employees’ relationships with their clients. This may have overshadowed some of

the individual psychological factors that influence affective commitment. Further

research is required to determine if PsyCap is related to affective commitment in

other groups of employees.

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The unexpected finding of the study was the stability of the Moment Specific

PsyCap scale. This scale demonstrated a test-retest reliability higher than .50,

invariance of means and homogeneity of variance in the current research and was

more stable than the PCQ-24 has been reported to be by Luthans, Avolio et al (2007).

Given that the items of the PCQ-24 were adapted to be more state-like for the

Moment Specific PsyCap scale, it was expected that the Moment Specific PsyCap

scale would demonstrate less stability than the PCQ-24. Moreover, the study was

conducted over a longer period of time than the two week period for which the .52

test-retest correlation for the PCQ-24 has been reported previously (Luthans, Avolio,

et al., 2007). In addition, the Pearson-Filon tests which revealed no significant

differences in the test-retest correlations for the Generalised and Moment Specific

PsyCap scales.

Likely coupled with the unexpected stability of the Moment Specific PsyCap

scale, was the finding that Moment Specific PsyCap at time one was moderately

correlated with future job satisfaction and self-rated performance. Moment Specific

PsyCap was not expected to demonstrate predictive validity as it was expected to

demonstrate a high degree of variability in individuals over time. Since the degree of

variability was not as large as anticipated, it is unsurprising that the scale

demonstrated some predictive validity.

Two possible explanations for the stability of the Moment Specific PsyCap

scale are investigated further in the next chapter. The first is that priming effects

occurred. In the current studies, participants always completed the Generalised

PsyCap scale directly before the Moment Specific PsyCap scale and this may have

affected their responding. Two possible mechanisms, through which this may have

occurred, the consistency motif and context-induced mood, are discussed in chapter

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four. Additionally, in following the State-Trait anxiety scale, the Generalised and

Moment Specific PsyCap scales had four-point response scales whereas the PCQ-24

had a six-point response scale. This modification may have also increased the

stability of the measures and is discussed further and investigated in chapter four.

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CHAPTER FOUR: ASSESSMENT OF ORDER AND RATING SCALE

EFFECTS OF GENERALISED AND MOMENT SPECIFIC PSYCAP

SCALES

Due to the unexpected stability of the Moment Specific PsyCap scale

reported in the previous experiment, this experiment aimed to examine the possibility

that the stability was the result of the particular methodology used. There were two

key aspects of the method which were considered relevant. These were the order in

which the Generalised and Moment Specific PsyCap scales were presented and the

rating scales used. This chapter will describe how each of these factors may have

influenced the stability of the Moment Specific PsyCap scale, before describing the

experiment designed to investigate these factors.

Order Effects

Cognitive psychologists studying survey response have found extensive

evidence that the context in which survey items are answered can dramatically

change participants’ responses to those items (see for example Rogelberg, Church,

Waclawski, & Stanton, 2004; Schwarz & Sudman, 1992). The context of an item

could refer to a wide variety of factors including the purposes of the survey, who is

present at the time the survey is completed, the characteristics of the interviewer for

aurally administered surveys and even remote factors such as the weather at the time

the survey is completed (Tourangeau, Rips, & Rasinski, 2000). As an example of a

context effect, one study asked respondents to report either three positive or three

negative life events that had recently occurred for them. When participants were

subsequently asked to rate their overall happiness and life satisfaction, participants

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who had been asked to recall positive events reported higher happiness and

satisfaction than those asked to recall negative events (Strack, Schwarz, &

Gschneidinger, 1985). This study provides an example of how a previous task can

affect subsequent survey responses, and highlights the need to consider the impact

that completing the Generalised PsyCap scale may have had upon responses to the

Moment Specific PsyCap scale in the study presented in chapter three.

The most common context effect examined in survey research is the effect

that earlier items can have upon responding to later items. This is often referred to as

an order effect. In the previous scale validation experiment described in chapter three

participants always completed the Generalised PsyCap scale immediately prior to

completing the Moment Specific PsyCap scale. Thus, it is possible that responding to

the Generalised PsyCap scale affected responses to the Moment Specific scale. There

are a number of possible mechanisms by which item order effects can arise, but two

are considered particularly relevant for the current study. These are the consistency

motif and context-induced mood.

Consistency motif. A substantial amount of theory and research generated by

both social psychologists and cognitive psychologists studying survey response has

shown that people generally want to be, or want to appear to be, both consistent and

rational in their beliefs and survey responses (N. P. Podsakoff & Organ, 1986; P. M.

Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Tourangeau & Rasinski, 1988).

Thus, the ‘consistency motif’ has been termed to refer to “the propensity for

respondents to try to maintain consistency in their responses to questions” (P. M.

Podsakoff et al., 2003, p. 882). One of the simplest forms of this effect stems from

questionnaire items that share common wording (Johns, 1994).

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In accordance with the consistency motif, participants in the previous

experiment may have been reluctant to indicate that their current feelings (indicated

on the Moment Specific PsyCap scale) were significantly different from their general

feelings (indicated by the Generalised PsyCap scale). Thus, completing the stable

Generalised PsyCap scale first may have artificially increased the stability of the

Moment Specific PsyCap scale, simply because participants gave similar responses

to both scales. This effect was likely to be exacerbated in the current study due to the

similarity of the wording of the Generalised and Moment Specific PsyCap scales. A

second mechanism through which context effects are likely to have occurred in the

previous study is through context-induced mood.

Context-induced mood. Schwarz and Clore (1983) showed that items can

alter a respondents mood by bringing positive or negative material to mind and that

this altered mood may affect responding to subsequent items. This effect was

demonstrated in the previously discussed study by Strack, Schwarz, & Gschneidinger

(1985), in which recalling positive or negative events affected later ratings of

happiness and life satisfaction. Podsakoff and colleagues have used the term

‘context-induced mood’ to refer to instances in which “the first question (or set of

questions) encountered on the questionnaire induces a mood for responding to the

remainder of the questionnaire” (P. M. Podsakoff et al., 2003, p. 882). It has

additionally been argued that the less respondents are aware that their current

feelings were caused by the previous questions asked, the more likely the mood is to

have an influence upon later judgements (Sudman, Bradburn, & Schwarz, 1996).

In the previous study, asking participants to indicate their Generalised

PsyCap before their Moment Specific PsyCap, asked them to consider how they

usually feel or act before indicating their feelings at that specific moment in time. It

93##

is possible that this consideration of general feelings may have altered their current

mood. For example, if a respondent was feeling uncharacteristically up or down (ie.

optimistic or pessimistic, hopeful or hopeless, confident or non-confident or resilient

or not resilient) in their mood state before completing any questionnaire items,

recalling how they usually feel (when completing the Generalised PsyCap Scale), is

likely to have shifted their mood back towards their mean level of feelings. This may

have shifted the Moment Specific PsyCap responses towards the Generalised PsyCap

responses and may have therefore created more stability in the Moment Specific

PsyCap measure than if it had been completed before the respondents completed the

Generalised PsyCap scale.

Rating Scale

Another possible methodological reason for the Moment Specific PsyCap

scale being more stable than expected in the previous experiment is the difference in

response scales used for the scale compared to the PCQ-24. Whereas the PCQ-24

uses a six point response scale, the Moment Specific PsyCap scale, in taking the

response scale from the State-Trait Anxiety Inventory used only a four-point

response scale. Studies have shown that as the number of response catgories

increase, both the test-retest and internal reliability of the scale improves (Lissitz &

Green, 1975; Lozano, Garcia-Cueto, & Muniz, 2008). This finding may point

towards the hypothesis that the shorter response scale in the previous scale validation

study may have resulted in increased rather than decreased variability of the Moment

Specific PsyCap scale in comparison to the PCQ-24. However, these studies have

assessed the reliability of scales measuring a stable construct, rather than scales

designed to measure a variable construct. When measuring a scale designed to be

variable, it is possible that creating more response options may increase the

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sensitivity of a scale to detect small changes. Therefore, increasing the number of

response options may decrease the test-retest reliability of a state scale.

The Present Study

This study aimed to assess the effect of the order of presentation of the

Generalised and Moment Specific PsyCap scales (relative to each other) and the

effect of the number of response options of the rating scales upon the consistency of

the Generalised and Moment Specific PsyCap scales. There were two key hypotheses

regarding the Moment Specific PsyCap scale. Firstly it was hypothesised that the

Moment Specific PsyCap scale would have a lower test-retest reliability when it was

presented before, rather than after the Generalised PsyCap scale. Secondly, it was

hypothesised that the Moment Specific PsyCap scale would have a lower test-retest

reliability when measured using a six rather than a four point response scale.

For the Generalised PsyCap scale it was hypothesised that if responses to the

scales were affected by order effects, then the test-retest reliability of the Generalised

PsyCap scale would be lower when it was presented after (rather than before) the

Moment Specific PsyCap scale. It was also hypothesised that the Generalised

PsyCap scale would have a higher test-retest reliability when presented with a six,

rather than a four point response scale.

In addition to the hypotheses concerning order effects and rating scale, the

internal consistency, validity (concurrent, predictive, convergent and discriminant)

and factor structure of the Generalised and Moment Specific PsyCap scales were

assessed. The hypotheses concerning these aspects of the scales remained unchanged

from the previous study described in chapter three.

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Method

Design

Participants in this study were invited to complete a series of questionnaires

at three points in time. In the first testing session participants completed the PsyCap

scales, in addition to the scales required to determine concurrent, predictive,

convergent and discriminant validity. In the second and third testing sessions the

PsyCap scales, as well as the scales required to determine concurrent and predictive

validity were completed.

Participants were divided into four conditions. The conditions differed with

respect to the order in which the two PsyCap scales were completed, and the

response format for the PsyCap scales. The conditions have been labelled in

accordance with the order of the PsyCap scales and the number of response options.

In the MS-G-6 condition participants completed the Moment Specific PsyCap scale

with a six point response scale, followed by the Generalised PsyCap scale with a six

point response scale. In the G-MS-6 condition participants completed the

Generalised and then the Moment Specific PsyCap scales, both with a six point

response scale. Participants in the MS-G-4 condition completed the Moment

Specific, followed by the Generalised PsyCap scales, both with four point response

scales. Finally, in the MS-G-4 condition participants completed the Moment Specific

PsyCap scale with a four point response scale followed by the Generalised PsyCap

scale with a four point response scale.

Participants

Participants for this study were drawn from two separate samples. The first

sample consisted of one hundred and eighty three third year undergraduate

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psychology students from the University of Western Australia (UWA). They were

invited to participate as part of a statistics unit in which they were enrolled.

Participants in this sample ranged from 18 to 62 years of age (mean age = 21.33,

S.D.= 4.01). The sample consisted of 50 (27.32%) males and 128 (69.94%) females;

five participants did not indicate their sex. Participants were informed of the purpose

of the study and although required to participate as a part of their unit, were given the

option of withdrawing his or her data. All participants consented for their data to be

used. All participants from this sample were assigned to the G-MS-4 condition.

The second sample consisted of four hundred and forty three third year

undergraduate psychology students from the University of Western Australia (UWA)

who were enrolled in an Industrial and Organisational Psychology unit. Participants

in this sample ranged from 17 to 50 years of age (mean age = 20.89, S.D.= 3.71).

The sample consisted of 110 (24.80%) males and 313 (70.70%) females; twenty

participants did not indicate their gender. Participants were assigned to small tutorial

classes for the unit in which they were enrolled. The tutorial classes were then

randomly selected to be assigned to one of the remaining three conditions (MS-G-4,

G-MS-6 and MS-G-6).

There were 178 participants in the MS-G-4 condition, 129 participants in the

MS-G-6 condition and 136 in the G-MS-6 condition. There were a greater number of

participants allocated to the MS-G-4 condition in the second sample in order to allow

comparisons to be made between the MS-G-4 data collected in the first sample

(which had 183 participants).

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Measures

PsyCap measures. The PsyCap scales for those in the four point response

scale conditions were as described in chapter two. For the six point rating scale

conditions the survey items remained unchanged from those reported in chapter two,

but the participants indicated their responses on a six, rather than four point response

scale. For the Moment Specific PsyCap scale response options ranged from ‘not at

all’ to ‘extremely so’. For the Generalised PsyCap scale the six response options

ranged from ‘almost never’ to ‘almost always’. Reliability statistics for the

Generalised and Moment Specific PsyCap scales are reported in the results section

below. When completing the scales (and all scales described below), students were

asked to interpret the words ‘job’ or ‘work’ to refer to their role as a university

student.

Concurrent and Predictive Validity measures.

Job satisfaction. Job satisfaction was measured using the five-item job

satisfaction scale described in chapter three. To adapt the scale to suit university

students, participants were instructed to respond to the items with respect to their

university studies and to interpret the words “job” or “work” as relating to their role

as a university student. Cronbach’s alpha for this scale was above .78 at each

collection period.

Affective Educational Commitment. Similar to the affective organisational

commitment used in chapter three, Hellman (2002) developed a measure of

educational commitment containing three sub-scales; continuance, affective and

normative commitment. The affective commitment sub-scale was used to measure

affective educational commitment in the current study. The scale contains five items

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which assess affective educational commitment using a 1 (strongly disagree) to 5

(strongly agree) response scale. An example item from the scale is “Being a college

student has a great deal of personal meaning for me.” To translate the survey to the

current study’s Australian context the word college was replaced with the word

university in all items. The scale demonstrated high reliability at all testing periods (α

> .82).

Performance. University performance was measured using self-report

measures. The self-report measure was an adaptation of the individual task behaviour

items from Griffin, Neal and Parker’s (2007) measure of work performance

described in chapter three. This scale was changed such that it asked participants to

rate their performance on their university studies rather than their “core tasks” as in

the original scale. The scale asked respondents to indicate how often they had carried

out behaviours which can be considered as key performance indicators during the

last week. Example items included (How often have you... ) “Completed your

university work well” and “Initiated better ways of completing your university

work.” This scale demonstrated a high level of internal consistency in the current

study (α≥ .90 for all three collection times).

Convergent and discriminant validity measures.

Core self-evaluations (CSES). Participants completed the 12 item CSES scale

(Judge et al., 2003) as described in chapter three. In the current study Cronbach’s

alpha for this scale was equal to .86.

Big Five Personality Domains. The ‘big five’ personality dimensions were

measured using the Ten-Item Personality Inventory (TIPI; Gosling et al., 2003), also

described in chapter three. Similarly to the previous study however, Cronbach’s

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alpha did not reach acceptable reliability for the subscales. In the current study α was

equal to .72, .37, .61, .66 and .43 for the Extraversion, Agreeableness,

Conscientiousness, Neuroticism and Openness to Experience scales respectively.

These low internal reliabilities are likely the result of only two items measuring each

construct. Similarly, to the previous study, any scales for which Cronbach’s alpha

was below .60 (in this case Agreeableness and Openness to Experience) were

excluded from further analyses.

Procedure

Participants were asked to complete the surveys on three occasions

throughout the university semester. For both samples there was a three week gap

between the first and second testing session. The two samples differed in the time

difference between their second and third testing session. For the sample which

completed the G-MS-4 condition, there was a two week gap between the second and

third testing sessions. This gap was five weeks for the second sample. It was not

possible to distribute the testing periods identically in both samples due to the timing

of the classes in which testing took place.

Participants completed the questionnaires at the beginning of class on each

testing occasion. Participants in the G-MS-4 condition completed the surveys online,

whereas the second sample completed them in a paper and pencil format. The online

surveys were structured such that each survey in the series only became visible when

the previous one had been answered. This ensured that they were completed in the

order intended. Participants in the paper and pencil sample were instructed to work

through the booklet of surveys in order (from beginning to end). Each survey was

printed onto a separate page so that once participants turned the booklet over to

100##

complete the next survey their responses from the previous one were not visible. This

mimicked the online procedure in which only one scale was visible at a time.

Participants completed the scales in the same order on each testing occasion.

The PsyCap scales were completed first, with the order and response scale for these

scales varying according to condition as described above. The remaining scales were

completed in the following order for all conditions: job satisfaction, self-rated

performance and then affective educational commitment. In the first testing session

participants additionally completed the CSES and TIPI questionnaires after

completing the above listed scales.

Analytical Strategy

Factor Structure

As in the study reported in chapter three, confirmatory factor analysis,

conducted using SPSS AMOS version 17, was used to compare the four competing

models of the underlying structure of the Generalised and Moment Specific PsyCap

scales. The models tested were (i) a single factor model, (ii) a four factor (self-

efficacy, hope, resilience and optimism) model with the six relevant items loading

onto each of the uncorrelated factors, (iii) a four factor (self-efficacy, hope, resilience

and optimism) model with the six relevant items loading onto each of the correlated

factors and (iv) a model with the same four factors linked by a higher order PsyCap

factor.

The maximum likelihood method of estimation was used for the analyses as

there was no evidence of significant skewness or kurtosis in the items. In all of the

analyses the error variances were constrained to be uncorrelated.

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The following fit statistics were calculated (with acceptable levels for each

statistic in parenthesis; chi-square (χ2) with corresponding degrees of freedom,

standardised root mean residual (SRMR; <.08), Root-Mean-Square Error of

Approximation (RMSEA; <.08), Goodness of Fit Index adjusted for degrees of

freedom (GFI; > .95), Comparitive Fit Index (CFI>.90) and Parsimonious Normed

Fit Index (PNFI; >.5), Consistent Information Criterion (CAIC) and the Expected

Cross Validation Index (ECVI). No criteria were set for the CAIC and ECVI, but as

discussed in the previous chapter, lower values for these fit statistics were taken to

indicate superior fit.

Internal consistency of Generalised and Moment Specific PsyCap. Research

has shown that increasing the number of response options for a scale also tends to

increase the reliability of that scale, with the optimal number of response options

thought to be between four and seven (Lozano et al., 2008). The PsyCap scales with

a four point response format had demonstrated acceptable reliability the previous

study, so it was thought that all versions of the PsyCap scales in the current study (in

which the response scale had remained at four or increased to six) would also

demonstrate acceptable reliability. This was tested by calculating Cronbach’s alphas

for each PsyCap component separately, as well as for the total scale for each

occasion that the Generalised and Moment Specific PsyCap scales were completed

for each condition separately. A criterion of α > .70 was set as adequate reliability.

Stability of Generalised and Moment Specific PsyCap. As in the previous

experiments, the stability of the PsyCap scales was tested using the three methods

recommended by Wright and Quick (2009); these are the test-retest reliability, a test

for invariance of the means over time, and a test of invariance of variance. In this

study a repeated measures ANOVA was used to test for invariance of the means over

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time and the variance of variance was tested using Mauchly’s test of sphericity.

Following the same criteria set in chapter three, the criteria for a scale to be

considered a trait were (i) the test-retest correlations higher than .70, (ii) no changes

in the mean sample scores across assessment periods and (iii) homogeneity of

variance across assessment periods. For a scale to be considered a state scale, the

criterion was that the test-retest correlation fall below .50.

Effect of rating scale and order of presentation upon the test-retest

reliabilities of the scales. To determine whether the test-retest reliabilities of the

PsyCap scales were affected by their order of presentation the mean test-retest

reliabilities for each scale were calculated for each condition separately. The mean

test-retest reliability was calculated by using the r to Fisher’s z transformation for

each test-retest coefficient, taking the average of these z scores and then transforming

this average back to an r score. The mean test-retest reliability for each scale when it

was presented first was compared to its mean test-retest reliability when it was

presented second.

Next, to assess whether having a four or six point rating scale affected the

test-retest reliability of the scales, the test-retest reliability for each scale with a four

versus six point rating scale was compared. Given that the test-retest reliability of the

PsyCap scales was affected by their order of presentation, this comparison was made

only for when the scales were presented first (ie. the test re-test reliability of the

Generalised PsyCap scale in the G-MS-6 condition was compared to that of the

Generalised PsyCap scale in the G-MS-4 condition and the test-retest reliability of

the Moment Specific PsyCap scale in the MS-G-6 condition was compared to the

test-retest reliability of the Moment Specific PsyCap scale in the MS-G-4 condition).

103##

Relative stability of Generalised and Moment Specific PsyCap.

Within conditions. The Pearson-Filon test with the Steiger (1980)

modification was used to test assess the hypothesis that within each condition the

Generalised PsyCap scale would be more stable than the Moment Specific PsyCap

scale. This was achieved by assessing whether the test-retest correlations of the

Generalised PsyCap scale were significantly higher than those of the Moment

Specific PsyCap scale (for an example of this analysis see (Kashy & Snyder, 1995).

This analysis was conducted for each of the possible test-retest couples (time 1 and 2,

time 1 and 3 and time 2 and 3). There were therefore three comparisons of the

relative stability of the Generalised and Moment Specific PsyCap scales for each

condition.

Between conditions. Next, to control for order effects, the stability of the

Generalised PsyCap scale when presented first (and therefore unaffected by order

effects) was then compared with the stability of the Moment Specific scale when

presented first (and therefore unaffected by order effects) in order to give an estimate

of the relative stability of the two scales when they were not influenced by order

effects. To assess this, the three test-retest reliabilities for the Moment Specific scale

in the MS-G-6 condition (between times one and two, between times one and three

and between times two and three) were compared with the corresponding test-retest

correlations of the Generalised PsyCap scale in the G-MS-6 condition using the

Fisher’s z test. The same analyses were not conducted for the conditions using a four

point response scale as the time intervals between testing phases for the two

conditions were not the same.

Validity

104##

Concurrent validity. As discussed in chapter three, it was hypothesised that

the Moment-Specific PsyCap scale would be strongly correlated to the outcome

variables of job satisfaction, affective educational commitment and self-rated

performance collected in the same assessment period.

It was additionally hypothesised that the correlation between Moment

Specific PsyCap and the outcome variable at each time matched period would be

higher than the correlation between Generalised PsyCap and the time matched

outcomes. For example, the correlation between Moment Specific PsyCap at time

three and self-rated performance and time three was hypothesised to be higher than

the correlation between Generalised PsyCap at time three and self-rated performance

at time three. This hypothesis was tested by comparing the correlation coefficients

using the equation outlined by Meng and colleagues (Meng et al., 1992).

Predictive validity of Generalised and Moment Specific PsyCap. As

described in chapter three, it was hypothesised in the current study that Generalised

PsyCap at time one would be a strong predictor of outcome measures of job

satisfaction, affective educational commitment and performance at subsequent

testing periods. This hypothesis was based upon the findings of Luthans et al. (2007)

who found that the PCQ-24 was able to predict these outcomes in spite of it only

having a test-retest reliability of .52 in their study. It was predicted that a scale which

was more stable over time would be a stronger predictor than a scale which changes

over time. For this reason it was hypothesised that the correlations between the

Generalised PsyCap scale at time one and the outcome measures in subsequent

testing sessions would be larger than the correlations between the Moment Specific

PsyCap scale at time one and the same outcome variables. This hypothesis was tested

105##

by comparing the correlations using the method proposed by Dunn and Clark (1969,

1971), as outlined by Meng, Rosenthal and Rubin (1992).

Luthans and colleagues (2007) also found that scores on the PCQ-24 were

able to predict the unique variance in the outcome measures when

Conscientiousness, Extraversion and Core Self-Evaluations were also included in the

prediction model. Regression analyses were used to test whether the Generalised

PsyCap score was similarly able to predict unique variance in the outcome variables.

In these analyses Conscientiousness, Extraversion and Core Self-Evaluations were

entered into block one of the analysis, and Generalised PsyCap at time one was

entered into block two. As described in chapter three, Moment Specific PsyCap was

not included in these models as the two PsyCap scales were too highly correlated.

Convergent and discriminant validity. Analyses were conducted to assess

whether the order and rating scale changes in the current study affected the scales’

relationships with variables shown to relate to the PCQ-24 or to the Moment Specific

and Generalised PsyCap scales in chapter three. Luthans et al. found no significant

correlation between PsyCap and age (r= .01), whereas this correlation was small but

significant in the study described in chapter three. Luthans et al., and the study in

chapter three both reported moderate positive relationships between PsyCap and

Extraversion (r= .36) and Conscientiousness (r= .39) and a strong positive

relationship between PsyCap and Core Self-Evaluations (r= .60). These correlations

were observed for the PsyCap scales in each condition separately to determine if any

of the correlations systematically differed in the present study. As the Neuroticism

scale demonstrated acceptable internal consistency in the current study, this provided

to first opportunity to compare the relationship between this scale and Generalised

and Moment Specific PsyCap. Luthans et al. reported a small significant relationship

106##

between PsyCap and Neuroticism (r= -.12), and a similar correlation between the

Generalised and Moment Specific PsyCap scales and Neuroticism was expected in

the current study. Similarly to in chapter three, these correlations were observed

using the PsyCap data collected at time one only. This was considered appropriate as

the Moment Specific PsyCap scale was expected to change over time.

The low internal reliabilities of the TIPI scales in the current study denoted

that it would be appropriate to correct the correlations involving these scales for

attenuation. However, the purpose of the convergent and discriminant analyses

reported here was to compare the correlations between the Generalised and Moment

Specific PsyCap scales and the personality traits with the correlations between the

PCQ-24 and the same personality traits reported by Luthans, Avolio et al. (2007).

These authors also used the TIPI and did not correct the correlations for attenuation,

despite similarly reported low reliability of the TIPI subscales. Therefore, the

correlations between the Generalised and Moment Specific scales in the current

study have been reported without correction to allow for a direct comparison with the

Luthans, Avolio et al. study. However, the correlations were also corrected for

attenuation using the formula published by Spearman (Spearman, 1904), and these

are reported separately.

Results

Participation numbers

As participants did not attend all classes the number of participants included

in the study at each testing period is different. The number of participants from each

condition that responded at each time period of the study is shown in table 13 below.

The table below shows the number of participants that participated in the study at

107##

each testing time and the number that participated at each time pair. The table can be

read in a similar manner to a correlation table.

Table 13. The number of participants from each condition that completed the

surveys at each time period and at each time pair.

G-MS-4 MS-G-4 G-MS-6 MS-G-6

1 2 3 1 2 3 1 2 3 1 2 3

1. Time 1 177 173 130 118

2. Time 2 136 141 141 146 108 114 88 97

3. Time 3 104 70 107 104 97 105 76 74 80 66 60 71

Factor structure. Tables 14 and 15 show the fit statistics for the four alternative

models for Generalised and Moment Specific PsyCap in each condition separately.

108$$

Table 14.

Generalised Specific PsyCap Scale: Fit indexes for each model and comparisons between models.

Model χ2 Df p SRMR RMSEA GFI CFI PNFI CAIC ECVI G-MS-4 1 factor 556.03 252 <.001 .073 .093 .777 .813 .646 854.094 3.583 4 uncorrelated factors 727.44 252 <.001 .269 .119 .740 .707 .563 1025.496 4.524 4 correlated factors 399.71 246 <.001 .062 .077 .841 .905 .704 735.022 2.790 4 first order factors, 1 second order factor 402.23 248 <.001 .063 .077 .840 .905 .708 725.120 2.781 MS-G-4 1 factor 635.20 252 <.001 .071 .093 .754 .800 .648 931.109 4.202 4 uncorrelated factors 868.58 252 <.001 .308 .119 .698 .678 .551 1164.494 5.544 4 correlated factors 502.15 246 <.001 .069 .077 .803 .866 .687 835.049 3.507 4 first order factors, 1 second order factor 506.03 248 <.001 .070 .077 .803 .865 .691 826.600 3.506 G-MS-6 1 factor 575.50 252 <.001 .077 .100 .709 .784 .617 856.773 5.246 4 uncorrelated factors 643.75 252 <.001 .313 .110 .696 .738 .581 925.020 5.779 4 correlated factors 388.43 246 <.001 .068 .067 .801 .905 .696 704.865 3.878 4 first order factors, 1 second order factor 392.08 248 <.001 .068 .067 .799 .904 .700 696.789 3.876 MS-G-6 1 factor 601.49 252 <.001 .073 .105 .696 .812 .656 882.016 5.536 4 uncorrelated factors 782.32 252 <.001 .367 .129 .651 .715 .578 1062.845 6.971 4 correlated factors 458.29 246 <.001 .067 .083 .751 .886 .700 773.879 4.494 4 first order factors, 1 second order factor 458.90 248 <.001 .067 .082 .752 .887 .705 762.803 4.468

109$$

Table 15.

Moment Specific PsyCap Scale: Fit indexes for each model and comparisons between models.

Model χ2 Df p SRMR RMSEA GFI CFI PNFI CAIC ECVI G-MS-4 1 factor 680.125 252 <.001 .067 .097 .732 .828 .689 977.917 4.288 4 uncorrelated factors 940.242 252 <.001 .361 .123 .696 .724 .603 1238.034 5.725 4 correlated factors 459.135 246 <.001 .058 .069 .826 .915 .744 794.151 3.133 4 first order factors, 1 second order factor 460.714 248 <.001 .059 .069 .825 .915 .749 783.322 3.120 MS-G-4 1 factor 558.937 252 <.001 .067 .084 .776 .839 .680 854.847 3.764 4 uncorrelated factors 908.209 252 <.001 .322 .122 .695 .657 .534 1204.119 5.771 4 correlated factors 458.864 246 <.001 .063 .071 .821 .889 .704 791.762 3.258 4 first order factors, 1 second order factor 467.100 248 <.001 .064 .071 .818 .885 .707 787.678 3.282 G-MS-6 1 factor 529.370 252 <.001 .071 .101 .691 .824 .666 864.161 5.304 4 uncorrelated factors 696.674 252 <.001 .338 .120 .684 .753 .610 996.510 6.338 4 correlated factors 398.336 246 <.001 .066 .066 .799 .927 .732 699.978 3.840 4 first order factors, 1 second order factor 398.845 248 <.001 .066 .066 .798 .927 .738 690.069 3.823 MS-G-6 1 factor 529.370 246 <.001 .071 .093 .734 .824 .652 809.891 4.963 4 uncorrelated factors 696.674 248 <.001 .338 .118 .682 .718 .570 977.195 6.291 4 correlated factors 398.336 252 <.001 .066 .070 .797 .903 .700 713.922 4.019 4 first order factors, 1 second order factor 398.845 252 <.001 .066 .069 .797 .904 .705 702.742 3.991

110##

The fit statistics reported above show little difference in the fit of the Generalised

and Moment Specific scales. For both scales the fit statistics do not clearly

differentiate between models three and four. In both models the SRMR and RMSEA

fall below the .08 criteria (except for the Generalised PsyCap scale in the MS-G-6

condition). The CFI and PNFI also generally fall within the acceptable range.

However, the GFI failed to reach the .95 criteria in all assessments of the models,

and the significant χ2 in all cases indicates some problems with the fit.

In terms of differentiating between the model with four correlated factors and

the model in which these factors are linked by a higher order factor, the χ2 statistic is

consistently lower for the correlated factors model in all cases, suggesting this is a

better fit. However, this is contradicted by the CAIC and ECVI with are generally

lower for the higher order factor model, indicating that this is a better fit. In sum, it is

not possible to conclude that one model is a significantly better fit than the other, and

neither model can be considered a strong fit to the data. Consistent with the previous

study, subsequent analyses have been conducted using the higher order factor model.

Reliability

Internal consistency of Generalised and Moment Specific PsyCap. Table 16

shows that the Generalised PsyCap resilience scale failed to reach the criterion (α >

.70) in the MS-G-4 condition on two occasions. Given the number of analyses this is

not considered problematic as you would expect some to fail to reach appropriate

levels by chance. All other scales reached acceptable levels of reliability on all

occasions.

111##

Table 16.

Cronbach’s alpha for each PsyCap component and the full Generalised and

Moment Specific PsyCap scales at each testing point, for each condition separately.

Self-efficacy Hope Resilience Optimism PsyCap G-MS-4 Generalised Time 1 .814 .825 .732 .798 .918 Time 2 .872 .907 .771 .875 .948 Time 3 .883 .880 .663 .809 .939 Moment Specific Time 1 .870 .872 .796 .869 .919 Time 2 .922 .922 .799 .868 .957 Time 3 .923 .929 .818 .850 .961 MS-G-4 Generalised Time 1 .865 .847 .710 .779 .931 Time 2 .853 .870 .675 .780 .928 Time 3 .856 .881 .696 .810 .936 Moment Specific Time 1 .839 .869 .739 .775 .934 Time 2 .808 .877 .779 .808 .936 Time 3 .846 .891 .716 .837 .944 G-MS-6 Generalised Time 1 .841 .861 .738 .881 .934 Time 2 .880 .921 .821 .875 .952 Time 3 .909 .927 .789 .877 .958 Moment Specific Time 1 .892 .918 .788 .864 .950 Time 2 .916 .934 .775 .896 .958 Time 3 .935 .944 .834 .874 .966 MS-G-6 Generalised Time 1 .870 .905 .785 .855 .947 Time 2 .863 .916 .743 .829 .934 Time 3 .882 .912 .811 .805 .939 Moment Specific Time 1 .861 .897 .776 .818 .942 Time 2 .851 .912 .807 .832 .949 Time 3 .886 .926 .723 .828 .947

Stability of Generalised PsyCap. The test-retest reliabilities of the

Generalised PsyCap scale for each condition are shown in table 17. The table shows

112##

that the Generalised PsyCap scores all reached the criteria of being above .70 in the

conditions where it was presented before the Moment Specific PsyCap scale. In the

conditions where it was presented after the Moment Specific Scale, the test-retest

reliabilities fell below .70 in half of the test-retest reliabilities reported.

The repeated measures ANOVA showed no significant differences in the

means of the Generalised PsyCap scales over the three assessment periods in all four

conditions. For the G-MS-4 condition, F(2, 134) = 2.45, p= .090, for the MS-G-4

condition, F(2, 184) = .864, p= .423, for the G-MS-6 condition, F(2, 142) = .333, p=

.717 and for the MS-G-6 condition, F(2, 110) = .890, p= .414. Mauchly’s test

showed that there was also homogeneity of variance in all conditions.

Stability of Moment Specific PsyCap. The test-retest reliabilities for the

Moment Specific PsyCap scale are reported in Table 18. This table shows that all

test-retest reliabilities were above .50 and therefore did not meet the criteria for being

considered a state scale. However by visual inspection, the Moment Specific PsyCap

scale fell closer to the criteria when it was administered before, rather than after the

Generalised PsyCap scale. When it was presented before the Generalised scale, the

range of test-retest reliabilities was from .557- .816. The test-retest reliabilities

ranged from .672- .756 when it was presented after the Generalised scale.

The repeated measures ANOVAs were then conducted to examine whether

changes in the mean levels of the Moment Specific PsyCap scales had occurred over

time. Mauchly’s test revealed that the sphericity assumption had been violated in the

MS-G-4 and MS-G-6 condition, so the Greenhouse-Geisser correction was used for

these conditions. Results showed that there were no significant changes in mean

Moment Specific PsyCap scores for the G-MS-4 (F(2, 118)= .587, p = .558),#MS-G-

113##

4 (F(1.59, 151.08)= 1.319, p= .270), or MS-G-6 (F(1.531, 85.76)= 1.005, p = .352)

conditions. There was however a significant mean change in the G-MS-6 condition,

F(2, 138)= 4.937, p= .008. Post-hoc comparisons with LSD correction revealed that

there were significant differences between Moment Specific PsyCap measured at

time one compared to at times two and three. As previously indicated, Mauchly’s test

indicated that there was differences in the variances of the Moment Specific PsyCap

scales in the MS-G-4 and MS-G-6 conditions. There was homogeneity of variance

for this scale in the G-MS-4 and G-MS-6 conditions.

114##

Table 17.

Test-retest reliabilities of the Generalised PsyCap scale for each condition.

G-MS-4 MS-G-4 G-MS-6 MS-G-6

1 2 3 1 2 3 1 2 3 1 2 3

1. Generalised PsyCap Time 1 1.0 1.0 1.0 1.0

2. Generalised PsyCap Time 2 .772** 1.0 .741** 1.0 .812** 1.0 .753** 1.0

3. Generalised PsyCap Time 3 .723** .844** 1.0 .691** .762** 1.0 .823** .857** 1.0 .654** .653** 1.0

* p < .05, ** p < .01

Table 18.

Test-retest reliabilities of the Moment Specific PsyCap scale for each condition.

G-MS-4 MS-G-4 G-MS-6 MS-G-6

1 2 3 1 2 3 1 2 3 1 2 3

1. Moment Specific PsyCap Time 1 1.0 1.0 1.0 1.0

2. Moment Specific PsyCap Time 2 .703** 1.0 .812** 1.0 .756** 1.0 .816** 1.0

3. Moment Specific PsyCap Time 3 .677** .714** 1.0 .574** .568** 1.0 .729** .672** 1.0 .557** .591** 1.0

* p < .05, ** p < .01

115##

Effect of rating scale and order of presentation upon the test-retest

reliabilities of the scales. The mean test re-test reliabilities for each condition are

shown in table 19.

Table 19.

Mean test-retest reliabilities of the Moment Specific PsyCap scale for each

condition.

Generalised PsyCap Moment Specific PsyCap

G-MS-4 .785 .864

MS-G-4 .733 .669

G-MS-6 .832 .721

MS-G-6 .690 .674

Comparison of the test-retest reliability estimates revealed a consistent

pattern of presentation order for each of the two scales, irrespective of the number of

response options. Specifically, when the Generalised PsyCap scale was presented

first, its test-retest reliability was higher than when it was presented after the Moment

Specific scale (mean r= .785 vs .733 for the four point response scale and mean r=

.832 vs .690 for the six point scale). Conversely, when the Moment Specific scale

was presented first, its test-retest reliability was lower than when it was presented

after the Generalised scale (mean r= .669 vs .864 for the four point response scale

and mean r= .674 vs .721 for the six point scale).

As the above comparison indicated that the order of the presentation of the

scales effects their test-retest reliability, the test-retest reliability of the four versus

six point response scale was compared using only the times when the scale was

116##

presented first and therefore unaffected by the previous scale. The test-retest

reliability of the Generalised PsyCap scale was higher with a six rather than four

point response scale (r= .832 vs .785). The test-retest reliability of the Moment

Specific scale was marginally higher with a six rather than four point response scale

(r= .674 vs .669).

Relative stability of Generalised and Moment Specific PsyCap.

Within conditions. To assess whether the test-retest reliabilities of the

Generalised PsyCap scale were significantly higher than those of the Moment

Specific PsyCap scale the test-retest reliabilities for each scale within the same

condition were compared. There were three comparisons of the test-retest reliabilities

of the two scales for each condition, resulting in a total of twelve comparisons being

made. The Pearson-Filon test with the Steiger (1980) modification showed that there

were no significant differences in the test-retest reliabilities of the scales on nine of

the 12 comparisons. There were no differences in the test-retest reliabilities of the

scales between times one and two or one and three. However, the test-retest

reliability of the Generalised PsyCap scale between times two and three was

significantly higher than the test-retest reliability of the Moment Specific PsyCap

scale between times two and three in the MS-G-4 (z= 3.47, p < .01), G-MS-6 (z=

4.64, p < .01) and G-MS-4 conditions (z= 2.97, p < .01).

Between conditions. As previous analyses showed that the order of

presentation of the Generalised versus Moment Specific PsyCap scales affected their

test-retest reliability, a between conditions analysis was conducted to allow

comparison of the test-retest reliabilities of the two scales when they were the first

presented scale and therefore not affected by the previous scale. This was conducted

117##

for the conditions with a six point rating scale only as the time intervals between

testing periods were not the same in the two conditions with a four point rating scale.

Therefore the test-retest reliabilities of the Generalised PsyCap scale in the G-MS-6

condition were compared to the test-retest reliabilities of the Moment Specific

PsyCap scale in the MS-G-6 condition. The results showed that the test-retest

reliabilities between times one and two did not significantly differ but the test-retest

reliabilities of the Generalised PsyCap scale between times one and three and times

two and three were significantly higher than those of the Moment Specific scale.

Summary of reliability results. An important finding of the stability analyses

is that the stability of the scales is affected by its order of presentation. The test-retest

reliability of the Generalised scale is higher when it is presented before the Moment

Specific scale and the test-retest reliability of the Moment Specific scale is lower

when it is presented after the Generalised scale. This finding indicates that there are

strong order effects which cause responses to the second scale completed to be

consistent with those of the first. As such, it is most appropriate to examine the

reliability of each scale when it is the first presented and therefore not influenced by

responses to the previous scale.

Taking the two conditions in which the Generalised scale was presented first,

results show that the scale meets the criteria for being classified as a trait scale. The

test-retest reliabilities were all above .70, there was no significant differences in the

means across testing periods and there was homogeneity of variance. In addition, the

test-retest reliability of the Generalised scale was higher than that of the Moment

Specific scale when the interval between testing was greater than two weeks.

118##

When the Moment Specific scale was presented first, the scale still exhibited

more stability over time than expected. The scales did not meet the criteria of its test-

retest reliability falling below .50. In addition, there was no significant difference in

the means across testing periods. However, the test-retest reliabilities were

significantly lower than the comparable test-retest reliabilities of the Generalised

scale when the testing interval was greater than two weeks and there was

heterogeneity of variance in the scale. These factors point towards responses to the

Moment Specific scale varying somewhat over time, but varying less than they were

hypothesised to.

Validity

Concurrent validity. As hypothesised, both the Generalised and Moment

Specific PsyCap scales showed strong relationships with the outcome variables of

job satisfaction, affective educational commitment and self-rated performance when

measured at time matched periods. Table 20 shows the correlations between the two

PsyCap scales and the outcomes variables at time matched periods.

119##

Table 20.

Correlation coefficients for Generalised and Moment Specific PsyCap with Job

Satisfaction, Affective Educational Commitment and Performance measured at

corresponding time points (ie. across the top row the first cell shows the correlation

between satisfaction and Generalised PsyCap both measured at time one, the second

cell shows the correlation between satisfaction and Generalised PsyCap both

measured at time two etc).

Time 1 Time 2 Time 3 G-MS-4 Generalised PsyCap Satisfaction .485** .608** .573** Commitment .346** .358** .359** Performance .534** .596** .380** Moment Specific PsyCap Satisfaction .571** .606** .566** Commitment .399** .353** .317** Performance .584** .564** .443** MS-G-4 Generalised PsyCap Satisfaction .519** .552** .561** Commitment .317** .387** .333** Performance .428** .451** .452** Moment Specific PsyCap Satisfaction .492** .514** .593** Commitment .328** .312** .358** Performance .519** .601** .577** MS-G-6 Generalised PsyCap Satisfaction .531** .639** .504** Commitment .433** .470** .453** Performance .547** .480** .546** Moment Specific PsyCap Satisfaction .450** .611** .463** Commitment .415** .407** .268* Performance .521** .585** .594** G-MS-6 Generalised PsyCap Satisfaction .464** .606** .659** Commitment .382** .381** .451** Performance .351** .539** .655** Moment Specific PsyCap Satisfaction .465** .595** .600** Commitment .427** .413** .449** Performance .465** .664** .681**

120##

However, the hypothesis that the Moment Specific PsyCap scale would be

more strongly correlated to these outcomes measures was not strongly supported.

This hypothesis was tested by comparing the relevant correlation coefficients (eg.

comparing the correlation between Generalised PsyCap at time two and satisfaction

at time two with the correlation between Moment Specific PsyCap at time two and

satisfaction at time two) using the method outlined by Meng and colleagues (Meng et

al., 1992).

In terms of the two PsyCap scales’ relationships with time matched

satisfaction, results showed one time in which the Moment Specific PsyCap scale

was more highly correlated with satisfaction than the Generalised scale. This was at

time one in the G-MS-4 condition where the correlation between Moment Specific

PsyCap and satisfaction (r= .571) was higher than the correlation between

Generalised PsyCap and satisfaction (r= .485, z= 2.64, p< .01).

Results were similar for the time matched correlations between the PsyCap

scales and commitment. There were no significant differences in the correlations

between Moment Specific or Generalised PsyCap and time matched commitment

with the exception of one instance in the MS-G-6 condition. This was at time three,

where the correlation between Generalised PsyCap at time three and commitment at

time three (r= .453) was higher (rather than lower as hypothesised) than the

correlation between Moment Specific PsyCap at time three and commitment at time

three (r= .268, z= 2.53, p < .05).

The correlations between Moment Specific PsyCap and time matched

performance were generally higher than the correlations between Generalised

PsyCap and time matched performance. This correlation was compared a total of 12

121##

times (three times in each condition), with the finding that Moment Specific PsyCap

was more highly correlated with time matched performance than Generalised PsyCap

was in six of the 12 analyses. These 12 analyses are presented in table 21.

122##

Table 21.

Correlation coefficients for Moment Specific PsyCap with time matched

performance, Generalised PsyCap with time matched performance, and a test of the

differences between the two correlations.

Correlation between Generalised PsyCap and performance at time matched periods.

Correlation between Moment Specific PsyCap and performance at time matched periods.

Difference between the correlation coefficients

G-MS-4

Time 1 .534 .584 z =1.57, p > .05

Time 2 .596 .564 z =0.91, p > .05

Time 3 .380 .443 z =1.51, p > .05

MS-G-4

Time 1 .428 .519 z = 2.34, p < .05

Time 2 .451 .601 z = 3.30, p < .01

Time 3 .452 .577 z = 2.26, p < .05

MS-G-6

Time 1 .547 .521 z =0.593, p > .05

Time 2 .480 .585 z = 2.17, p < .05

Time 3 .546 .594 z =0.759, p > .05

G-MS-6

Time 1 .351 .465 z = 2.69, p < .01

Time 2 .539 .664 z = 2.92, p < .01

Time 3 .655 .681 z = 0.820, p > .05

123##

Predictive validity of Generalised and Moment Specific PsyCap. The

correlations between Generalised and Moment Specific PsyCap measured at time

one, with the outcome measures of job satisfaction, affective educational

commitment and self-rated performance at each testing period are shown in tables

22, 23 and 24. These tables show that both the Generalised and Moment Specific

PsyCap scales were moderately to strongly correlated with each of the outcome

measures.

The hypothesis that Generalised PsyCap at time one would be more strongly

correlated with the future outcome measures than Moment Specific PsyCap at time

one was correlated with the same outcome measures was tested using the method

proposed by Dunn and Clark (1969, 1971), as outlined by Meng, Rosenthal and

Rubin (1992). Six pairs of correlation coefficients were compared for each condition

(Generalised vs Moment Specific PsyCap at time 1 correlated with job satisfaction at

time 2, Generalised vs Moment Specific PsyCap at time 1 correlated with job

satisfaction at time 3, Generalised vs Moment Specific PsyCap at time 1 correlated

with affective commitment at time 2, Generalised vs Moment Specific PsyCap at

time 1 correlated with affective commitment at time 3, Generalised vs Moment

Specific PsyCap at time 1 correlated with performance at time 2 and Generalised vs

Moment Specific PsyCap at time 1 correlated with performance at time 3), resulting

in a total of 24 comparisons being made. Across these analyses there was only one

difference found; in the MS-G-4 condition the correlation between Generalised

PsyCap at time one and performance at time two (r= .386) was significantly lower

than the correlation between Moment Specific PsyCap at time one and performance

at time two (r= .492, z= 2.39, p< .01). Given the large number of correlations, this

effect can perhaps be ignored and attributed to chance and it can be concluded that

124##

Generalised PsyCap at time one was not more highly correlated with the outcome

measures at times two and three than Moment Specific PsyCap at time one was

correlated with the same outcome measures.

The unique variance that Generalised PsyCap could account for in predicting

job satisfaction, affective commitment and self-rated performance when

Conscientiousness, Extraversion and Core Self-Evaluations were also included in the

model was examined using hierarchical regression analyses. A total of six analyses

were run for each of the study conditions. These were with job satisfaction at times

two and three, affective educational commitment at times two and three and self-

rated university performance at times two and three as the dependent variables. The

results for these analyses are shown in tables 25 and 26. Results show that

Generalised PsyCap was able to add significantly to the prediction of Job

Satisfaction in three of the four comparisons in which it was completed before the

Moment Specific scale (in the G-MS-4 and G-MS-6 conditions), but only in one of

the four comparisons in which it was completed after the Moment Specific scale (in

the MS-G-4 and MS-G-6 conditions). The regression analyses with affective

educational commitment as the dependent variable show that Generalised PsyCap

was generally not a strong predictor of commitment, only adding significant variance

to the prediction once across all conditions. This was at time three, in the G-MS-4

condition. Finally, the Generalised PsyCap scale added to the prediction of

performance in three of the four comparisons in which it was completed first, but

none of the comparisons in which it was completed second.

As a summary, these results tend to support the hypothesis that Generalised

PsyCap would be able to account for unique variance in job satisfaction and

performance when the Big Five personality dimensions and core-self evaluations

125##

were taken into account, but, with the caveat that the hypothesis was only supported

in instances in which the Generalised scale was completed before the Moment

Specific scale and therefore unaffected by order effects. However, the hypothesis that

Generalised PsyCap would account for unique variance in affective commitment was

not supported. In addition, it must be noted that the strength of these findings in

limited by low internal reliability of the scales used to measure the Big Five

personality dimensions.

126##

Table 22.

Correlations between Generalised and Moment Specific PsyCap (at time one) and

job satisfaction. For each cell of the table correlation coefficients for the G-MS-4

condition (bold), MS-G-4 condition (plain text) G-MS-6 condition (bold), and then

the MS-G-6 condition (plain text) are shown when read from top to bottom.

1 2 3 4 5

1. Generalised PsyCap –Time 1 1.0

2. Moment Specific PsyCap-Time 1 .870** .837** .853** .841**

1.0

3. Satisfaction- Time 1 .485** .519** .464** .531**

.571**

.492**

.465**

.450**

1.0

4. Satisfaction- Time 2 .519** .445** .441** .532**

.496**

.447**

.405**

.494**

.662**

.769**

.746**

.773**

1.0

5. Satisfaction- Time 3 .554** .465** .508** .499**

.548**

.451**

.477**

.500**

.633**

.672**

.680**

.632**

.741**

.619**

.690**

.658**

1.0

* p < .05

** p < .01

127##

Table 23.

Correlations between Generalised and Moment Specific PsyCap (at time one) and

affective educational commitment. For each cell of the table correlation coefficients

for the G-MS-4 condition (bold), MS-G-4 condition (plain text) G-MS-6 condition

(bold), and then the MS-G-6 condition (plain text) are shown when read from top to

bottom.

1 2 3 4 5

1. Generalised PsyCap –Time 1 1.0

2. Moment Specific PsyCap-Time 1 .870** .837** .853** .841**

1.0

3. Commitment- Time 1 .346** .317** .382** .433**

.399**

.328**

.427**

.415**

1.0

4. Commitment- Time 2 .294** .280** .317** .352**

.359**

.315**

.384**

.384**

.793**

.828**

.837**

.866**

1.0

5. Commitment- Time 3 .247 .218** .345** .357**

.333*

.259**

.429**

.416**

.779**

.820**

.749**

.853**

.832**

.865**

.821**

.808**

1.0

* p < .05

** p < .01

128##

Table 24.

Correlations between Generalised and Moment Specific PsyCap (at time one) and

self-rated performance. For each cell of the table correlation coefficients for the G-

MS-4 condition (bold), MS-G-4 condition (plain text) G-MS-6 condition (bold), and

then the MS-G-6 condition (plain text) are shown when read from top to bottom.

1 2 3 4 5

1. Generalised PsyCap –Time 1 1.0

2. Moment Specific PsyCap-Time 1 .870** .837** .853** .841**

1.0

3. Performance- Time 1 .534** .428** .351** .547**

.584**

.519**

.465**

.521**

1.0

4. Performance- Time 2 .483** .386** .431** .357**

.517**

.492**

.446**

.425**

.572**

.657**

.542**

.488**

1.0

5. Performance- Time 3 .297** .227** .578** .305**

.309**

.262**

.517**

.345**

.409**

.448**

.554**

.496**

.604**

.536**

.627**

.429**

1.0

* p < .05

** p < .01

129##

Table 25.

Coefficient of determination and change in coefficient of determination for Block

One (Conscientiousness, Extraversion and Core Self-Evaluations), and Block Two

(addition of Generalised PsyCap at time 1) regressed onto job satisfaction, affective

commitment and performance.

Job Satisfaction Affective Commitment Performance

Time 2 Time 3 Time 2 Time 3 Time 2 Time 3

R² ∆R² R² ∆R² R² ∆R² R² ∆R² R² ∆R² R² ∆R²

G-MS-4

Block 1 .258 .261 .130 .037 .294 .075

Block 2 .314 .056** .352 .091** .143 .013 .131 .093** .324 .031* .122 .047*

MS-G-4

Block 1 .216 .261 .076 .135 .197 .117

Block 2 .253 .037* .271 .010 .088 .012 .138 .002 .216 .019 .117 .000

G-MS-6

Block 1 .299 .380 .160 .259 .230 .287

Block 2 .304 .005 .421 .041* .177 .017 .289 .030 .251 .021 .387 .009*

MS-G-6

Block 1 .367 .289 .201 .224 .242 .164

Block 2 .387 .020 .310 .020 .213 .013 .226 .002 .243 .002 .165 .001

* p < .05

** p < .01

130##

Table 26.

Beta weights for Big Five Personality Traits, Core Self-evaluations and Generalised

PsyCap at time 1 regressed onto job satisfaction, affective commitment and

performance.

G-MS-4 MS-G-4 G-MS-6 MS-G-6

Time 2 β

Time 3 Β

Time 2 β

Time 3 β

Time 2 Β

Time 3 β

Time 2 β

Time 3 β

Job Satisfaction Conscientiousness .095 .110 .196* .070 .076 .291** .162 .129 Extraversion .093 .101 .130 -.008 .116 .041 -.022 -.117 Core self-evaluations .183 .159 .091 .383** .399** .272* .381** .360* Generalised PsyCap- Time 1

.333** .395** .263* .146 .101 .274* .200 .206

Affective Commitment Conscientiousness .008 -.100 .019 .104 .259* .408** .202 .208 Extraversion .232* .117 .089 .280* .205* .057 .241* .073 Core self-evaluations .093 -.122 .113 .051 -.028 .034 .038 .263 Generalised PsyCap- Time 1

.159 .401** .148 .069 .188 .234 .161 .071

Performance Conscientiousness .202* .086 .252** .258* .180 .110 .277* .144 Extraversion .157 .185 -.012 .086 .081 .020 .075 -.104 Core self-evaluations .172 -.079 .160 .135 .200 .217 .229 .348 Generalised PsyCap- Time 1

.246* .284* .186 -.020 .206 .421** .057 .019

* p < .05, ** p < .01

131##

Convergent and discriminant validity. Having found that order of

presentation affected test reliabilities, it was also necessary to address whether the

validities were affected. The correlations between the PsyCap scales at time one, and

age, Neuroticism, Extraversion, Conscientiousness and Core-Self Evaluations are

shown in Table 27. There were small relationships between PsyCap and age, medium

correlations between PsyCap and Neuroticism, Extraversion and Conscientiousness

and large correlations between PsyCap and Core Self-Evaluations. Correlation

coefficients between variables did not vary greatly between conditions. These

correlation coefficients in Table 27 are generally within the ranges reported by

Luthans et al. and in chapter three, indicating that the convergent and discriminant

validity of the scales was not impacted by their order of presentation or their

response scale.

The correlations corrected for attenuation reported in Table 28 are somewhat

higher. These correlations coefficients must also be interpreted with caution

however, as low reliability of scales (as in the current study) can result in inflated

correlations (Sechrest, 1984).

132##

Table 27.

Correlations of Generalised and Moment Specific PsyCap (at time one) with other

variables (correlations are not corrected for attenuation). For each cell of the table

correlation coefficients for the G-MS-4 condition (bold), MS-G-4 condition (plain

text) G-MS-6 condition (bold), and then the MS-G-6 condition (plain text) are shown

when read from top to bottom.

1 2 3 4 5 6 7 Mean (SD)

1. Generalised PsyCap

1.0 2.80 (0.45) 2.88 (0.46) 4.24 (0.64) 4.09 (0.70)

2. Moment Specific PsyCap

.870**

.837**

.853**

.841**

1.0 2.89 (0.53) 2.87 (0.46) 4.21 (0.72) 3.98 (0.70)

3. Age .223 .209 .043 .304**

.161

.147

.072

.164

1.0 21.33 (4.01) 20.43 (2.43) 21.07 (4.19) 21.36 (4.53)

4. Extraversion .293** .406** .340** .292**

.259**

.346**

.253**

.239**

-.040 .016 .044 -.015

1.0

4.34 (1.37) 4.44 (1.44) 4.59 (1.40) 4.30 (1.40)

5. Conscientious .329** .204** .248** .451**

.375**

.236**

.330**

.455**

.050

.115

.155

.154

-.006 .120 -.064 .157

1.0 4.95 (1.39) 4.99 (1.26) 4.91 (1.33) 5.08 (1.23)

6. Neuroticism -.384** -.412** -.448** -.399**

-.346** -.417** -.339** -.325**

-.088 -.028 -.144 -.116

-.166* -.208** -.246** -.265**

-.255 -.146 -.231** -.222*

1.0 8.78 (2.80) 9.03 (2.79) 9.12 (2.57) 9.57 (2.82)

7..Core self-evaluations

.661**

.654**

.658**

.663**

.682**

.631**

.595**

.629**

.015

.090

.067

.293**

.268**

.405**

.258**

.387**

.417**

.246**

.375**

.397**

.533

.489**

.562**

.530**

1.0 3.46 (0.57) 3.54 (0.53) 3.59 (0.59) 3.51 (0.60)

* p < .05

** p < .01

133##

Table 28.

Correlations of Generalised and Moment Specific PsyCap (at time one) with other

variables. Correlations are corrected for attenuation. For each cell of the table

correlation coefficients for the G-MS-4 condition (bold), MS-G-4 condition (plain

text) G-MS-6 condition (bold), and then the MS-G-6 condition (plain text) are shown

when read from top to bottom.

Generalised PsyCap

Moment Specific PsyCap

Extraversion .363

.502

.410

.354

.316

.429

.302

.289

Conscientiousness .440

.253

.322

.645

.494

.293

.425

.650

Neuroticism -.493

-.526

-.571

-.505

-.444

-.531

-.428

-.412

Core Self-evaluations

.744

.718

.735

.754

.757

.695

.659

.714

Discussion

Order effects. The key finding of this study is the stability assessments of the

Generalised and Moment Specific PsyCap scales which show that the order in which

134##

the scales are presented (relative to each other) influences their stability. A

comparison of the mean test-retest correlations of the scales for each condition

separately reveals that the Generalised PsyCap scale is more stable when presented

before rather than after the Moment Specific PsyCap scale. By contrast, the Moment

Specific PsyCap scale is more variable when presented before, rather than after the

Generalised PsyCap scale. This difference arose with both the four and six point

response scales. This finding suggests that responses to the second of the two scales

to be presented are likely affected by the consistency motif. That is, participants

simply respond to the two scales in a similar manner, thus the stability of the second

scale to be presented is similar to the stability of the first. The result is that the

Generalised PsyCap scales varies more when presented after the Moment Specific

scale and the Moment Specific scale is more stable when presented after the

Generalised Scale.

There were two mechanisms by which order effects were posited to arise.

These were the consistency motif and context induced mood. Context induced mood

effects would occur when answering items presented earlier in a survey changed the

participants’ moods, and therefore changed their responses to later items in a scale.

Context induced mood is likely to have a stronger effect when the Generalised

PsyCap scale was presented first rather than when the Moment Specific scale was

presented first, due to moment specific states being more variable and open to

manipulation than generalised traits. If a respondent was in an unusual PsyCap state

(ie. unlike their usual PsyCap trait level) when starting a survey, recalling how they

usually feel could easily alter how they feel at that specific moment in time. By

contrast, highlighting the participant’s current mood state to them should not change

the way that they ‘generally feel’. The results of the current experiment, in which

135##

presenting the Moment Specific PsyCap scale first resulted in more variability in the

Generalised PsyCap scale therefore points towards the order effects being caused by

the consistency motif rather than context induced mood.

Additional evidence for the consistency motif affecting responses to the scale

presented secondly can be drawn from the high correlations between the Generalised

and Moment Specific PsyCap scales at each testing period. The correlations between

Generalised and Moment Specific PsyCap measured at the same point in time ranged

from .778 to .929, indicating a high level of consistency in responding to the two

scales.

Given these findings it is recommended that if the two scales are used in a

single study, only one of the scales should be completed in a single testing session,

or minimally other scales or filler tasks should be completed between the two scales.

Internal Consistency of Generalised and Moment Specific PsyCap scales.

In the current study the resilience scale did not always meet acceptable levels of

reliability when administered with a four point scale, but all component scales and

the full PsyCap scales reached acceptable levels of reliability when administered

with a six point scale. This finding is in line with previous research which has shown

that the internal reliability of a scale improves with an increased response scale

(Lissitz & Green, 1975; Lozano et al., 2008). It therefore appears advisable to use the

six rather than four point response scale for the Generalised and Moment Specific

PsyCap scales in order to consistently achieve acceptable levels of internal

reliability.

Stability of Generalised and Moment Specific PsyCap. In the conditions in

which it was presented first, the Generalised PsyCap scale met all the criteria for

136##

being classified as a trait. This indicates that contrary to the previous research which

has conceptualised PsyCap as being a ‘state-like’ construct, it is possible to measure

a trait component to PsyCap when the scale items and response scale are constructed

in a manner appropriate for doing so.

Surprisingly, the Moment Specific PsyCap scale had higher test-retest

reliabilities than that reported for the PCQ-24 by Luthans, Avolio et al. (Luthans,

Avolio, et al., 2007), even in the conditions in which it was presented before the

Generalised PsyCap scale. The authors reported that the PCQ-24 had a test-retest

reliability of .52 over a two week period. In the current study, the interval between

testing periods one and two was two weeks. In the two conditions in which the

Moment Specific scale was presented first its test-retest reliabilities were .812 and

.816 for the conditions with a four and six point rating scale respectively. However,

over longer testing intervals the test-retest correlations (which ranged from .557-

.591) were relatively similar to the .52 of the PCQ-24. Thus, although the Moment

Specific PsyCap scale did not meet the current study’s criteria for a state scale,

participant’s responses to the scale did exhibit more change over time than responses

to the Generalised PsyCap scale.

Further support for the responses to the Generalised PsyCap scale showing

more stability over time than the responses to the Moment Specific scale can be

drawn from the comparisons of the test-retest reliability of the scales when they were

the first presented. This comparison was made only for the conditions with a six

point rating scale, as the four point rating scale conditions were not run with identical

test intervals. These results showed that when the testing interval was greater than

two weeks, the test-retest reliability of the Generalised scale was significantly higher

than that of the Moment Specific scale.

137##

Concurrent validity of Moment Specific PsyCap. The PCQ-24 has been

shown to relate to job satisfaction, performance and affective organisational

commitment (Larson & Luthans, 2006; Luthans, Avolio, et al., 2007; Luthans et al.,

2005). Thus, the Generalised and Moment Specific PsyCap scales were also

predicted to be related to these outcomes. Correlation analyses supported this

hypothesis, showing strong to moderate correlations between both PsyCap scales and

satisfaction, affective commitment and self-rated performance measured at the same

time period.

The correlations between PsyCap and performance and satisfaction appear to

be a little higher than the correlations between Moment Specific PsyCap and

affective educational commitment. This finding reflects the higher stability of

affective organisational commitment compared to satisfaction and performance. The

mean test-retest correlation for affective educational commitment in the current study

was .833, compared to the .694 mean test-retest correlation of job satisfaction and

.541 for the self-rated performance scale. The more variable outcome measures are

more likely to be altered by changes in PsyCap states, which is reflected in their

higher correlations with the Moment Specific scale.

Predictive validity of Generalised PsyCap. Given its predicted stability over

time, it was hypothesised that Generalised PsyCap would be able to predict future

levels of job satisfaction, affective commitment and performance. Results supported

this hypothesis, showing moderate to strong relationships between Generalised

PsyCap at time one and future measures of job satisfaction and self-rated

performance. The correlations between Generalised PsyCap at time one and future

measures of affective educational commitment were moderate in magnitude.

138##

Next, to examine the strength of Generalised PsyCap as a predictor of the

outcomes of job satisfaction, affective organisational commitment and self-rated

performance when the Conscientiousness, Extraversion and Core Self-Evaluations

were also taken into account, regression analyses were conducted. Results when the

Generalised PsyCap scale was presented first and responses therefore not effected by

previous responses to the Moment Specific scale are the most relevant to consider.

These analyses with job satisfaction as the dependent variable showed that the

addition of Generalised PsyCap to the model already including Conscientiousness,

Extraversion and Core Self-Evaluations significantly improved the model in three of

the four analyses. Generalised PsyCap was the strongest predictor of job satisfaction

in two of the analyses, but only the second or third strongest predictor in the other

two analyses.

Similarly, three of the four analyses with performance as the dependent variable

showed that the addition of Generalised PsyCap significantly improved the model.

These analyses additionally showed PsyCap to be the strongest predictor of

performance in all four analyses.

The addition of Generalised PsyCap only improved the predictive power of the

model in one of the four analyses run with affective educational commitment as the

dependent variable. Findings regarding the strongest predictors of commitment were

inconsistent, with results showing Conscientiousness to be the strongest predictor in

two of the analyses. Extraversion and Generalised PsyCap were the strongest

predictor in one of the remaining two analyses each.

Together these findings show some positive evidence that Generalised PsyCap

can predict future positive organisational outcomes, although the findings are a little

inconsistent. These inconsistent findings may reflect the unreliability of the TIPI

139##

scales, rather than a true indication of the relative ability of the two scales to predict

future affective commitment. In order to gain a stronger understanding of the ability

of Generalised PsyCap to predict job satisfaction, affective commitment and

performance, the final study reported in this thesis used a more reliable measure of

the Big Five personality domains, the NEO-PI-R (Costa & McCrae, 1992).

In addition, thus far the studies reported have included only self-rated

performance as a performance indicator. This is likely to inflate the correlation

between the PsyCap scales and performance as it stands to reason that someone

higher in PsyCap, and particularly the constructs of self-efficacy and optimism are

more likely to rate their performance as being higher than someone lower in these

personality traits. Therefore, the final study included a supervisor rating of

performance rather than relying on self-ratings.

Convergent and Discriminant Validity of Generalised and Moment

Specific PsyCap. Convergent and discriminant validity of the scales was determined

by examining their relationships with age, the Big-Five personality dimensions

measured by the TIPI and with Core Self-Evaluations. These were the same scales

for which Luthans, Avolio et al. (Luthans, Avolio, et al., 2007) tested the validity of

the PCQ-24. It was hypothesised in the current study that the relationships of the

Generalised and Moment Specific PsyCap scales with these variables would be

unchanged from those reported for the PCQ-24 and the variables. This hypothesis

was supported, indicating that the changes to the PCQ-24 which were made to create

the Generalised and Moment Specific PsyCap scales did not change the construct

being measured by the scales. While the previous study found a small, positive

correlation between age and PsyCap, the results of this study (in which no significant

140##

relationships were found) support the assertion in the previous study that this may

have been by chance.

In both the current study and the study conducted by Luthans, Avolio et al.,

the internal reliability of the Big-Five personality dimensions was low, indicating

that these findings must be interpreted with caution. In the current study the

correlations between the Big-Five personality dimensions and the PsyCap scales

were corrected for attenuation. The corrected correlations were not used in the

comparison of the current study’s reported correlations with Luthans, Avolio et al.’s

study, because Luthans, Avolio et al. also reported low internal reliability but did not

correct for it. However, the corrected correlations revealed that the correlation

between Neuroticism and PsyCap was somewhat higher than that reported by

Luthans, Avolio et al.. They found only a small between PsyCap and Neuroticism (r=

-.12), wheras the results of the current study indicate that a moderate to strong

relationship exists (-.253 ≤ r ≤ -.650). Although in conflict with the research reported

by Luthans et al. using the PCQ-24, this relationship is not surprising, as it makes

theoretical sense that someone high in PsyCap may experience greater emotional

stability (the opposite of Neuroticism). In addition previous research has shown

moderate relationships between the PsyCap components of optimism (r= -.50,

(Scheier, Carver, & Bridges, 1994)), resilience (r= -.71, (Furnham, Crump, &

Whelan, 1997)) and generalised self-efficacy (r= -.54, (Judge, Erez, & Bono, 1998)).

This research suggests that the relationship between PsyCap and Neuroticism may be

higher than that reported by Luthans et al., however, further research using more

reliable personality scales must be conducted before firm conclusions can be drawn.

Factor structure of Generalised and Moment Specific PsyCap scales.

Similarly to the previous study reported in chapter three, the confirmatory factor

141##

analyses in the current study found little differentiation between the model with four

correlated factors model and the higher order factor model, but did find that these

two models provided a superior fit than the single factor model, and four

uncorrelated factors models. The GFI and chi square fit statistics indicate neither the

higher order PsyCap model or the four correlated factors model is not a perfect fit for

the data. However, the analysis did show that the fit of the Moment Specific and

Generalised PsyCap scales to the higher order PsyCap model are similar to that

reported to be the best fitting model to the PCQ-24 by Luthans, Avolio et al. (2007),

and that found in chapter three. Thus, similarly to chapter three, these results show

that the changes that were made to the PCQ-24 to create the Generalised and

Moment Specific PsyCap scales did not significantly alter its factor structure.

Additionally, fit statistics in the current study were similar for all four

conditions in the present study, indicating that altering the order of presentation of

the Generalised relative to the Moment Specific PsyCap scales, or the rating scales

(from a four to six point scale) does not significantly alter its factor structure.

Conclusions. A key finding of this study is that strong order effects arise when

the Generalised and Moment Specific PsyCap scales are presented one after the

other. Respondents in this study have demonstrated a tendency to respond to the two

scales in a consistent manner, resulting in increased stability of the Moment Specific

PsyCap scale when it is presented after the Generalised scale, and increased

variability in the Generalised PsyCap scale when presented after the Moment

Specific scale. When the scales are assessed with the absence of order effects (ie.

when presented first), the Generalised PsyCap scale met the criteria for a trait scale.

These were test-retest reliability > .70, invariance of means across testing periods,

and homogeneity of variance across testing periods. The criteria set for a scale to be

142##

defined as a pure state was a test-retest reliability less than .50. The Moment Specific

PsyCap scale did not meet this criteria, but fell close to in when the testing intervals

were greater than two weeks.

Another consideration in the current study was the use of a four or a six point

rating scale. Results showed little differentiation in the test-retest reliability or other

stability assessments of the two PsyCap scales, but, in line with previous research,

the six-point response format showed increased internal reliability.

The Generalised PsyCap scale demonstrated some abilities to predict the future

positive outcomes of job satisfaction, affective commitment and self-rated

performance. However, it is difficult to make strong conclusions about the ability of

Generalised PsyCap to predict these outcomes due to two limitations already

discussed; the reliability problems in the TIPI, and the use of self-rated performance

measures. The final study in this thesis addresses these key issues, through the use of

a more reliable measure of the personality traits measure and supervisor performance

ratings.

143##

CHAPTER FIVE: VALIDITY OF GENERALISED PSYCAP AS AN

EMPLOYEE SELECTION TOOL

This study aimed to assess the utility of the Generalised PsyCap scale as an

employee selection tool. The previous studies reported in this thesis have examined

the predictive validity of the scale, but have each had different limitations which

mean that clear conclusions about the selection validity of the scale cannot be made.

The limitations of the study described in chapter three which a sample of employees

from a not-for-profit organisation were;

• It was conducted over a relatively short period of time (2 months).

• The participants in the study were existing employees of the organisation

rather than new recruits. This means that the study did not cover the

period in which new employees are required to adjust to their new job

roles and/or organisation. As the PsyCap scale is predicted to predict job

satisfaction and affective organisational commitment, it is plausible that

any study using existing employees in an organisation may be limited as

those with the lower PsyCap scores (and therefore experiencing lower

satisfaction and commitment), may have already left their jobs.

• It used a four point response scale (whereas chapter four has

demonstrated that the 6-point resulted in increased internal reliability and

test-retest correlations).

144##

• The low reliability of the TIPI scales meant that the study was unable to

satisfactorily assess the ability of PsyCap to predict relevant outcomes

above and beyond existing personality measures.

• Performance was only assessed using a self-report measure.

The study in the previous chapter included a six point response scale but was

similarly limited by the use of self-report measures of performance, the low

reliability of the TIPI scales, the use of participants not starting a new job and the

short time period. In addition, the study used students rating their university

performance rather than a sample of employees.

The current study aimed to address these limitations and assess the extent to

which the Generalised PsyCap scale can be considered a valid employee selection

tool. The study was conducted using a sample of Prison Officers who were newly

commencing the role. All participants completed the Generalised PsyCap scale with

a six-point response scale on their first day of employment. The study was conducted

over a period of 9 months, which included the participants’ job training and full

probation period. At the end of training and at the end of probation, the participants’

supervisors rated their performance rather than using self-ratings of performance.

Additionally, the results of the personality and ability tests used by the organisation

to select employees were made available to the experimenter (these were provided in

a numerically coded, de-identified manner, with the permission of the participants).

This included the NEO-PI-R scale, which provided a more reliable measure of the

Big Five personality constructs. With the inclusion of the ability tests, this study was

able to assess whether inclusion of the Generalised PsyCap scale was able to

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improve the prediction of any key outcomes for employees above the selection scales

already used by the organisation.

Stability. In order to be considered a valid selection tool, the Generalised PsyCap

scale was required to exhibit stability over time. This was assessed using the criteria

set out for a trait in previous chapters (test-retest > .70, invariance of means and

invariance of variance). It was hypothesised that similarly to the previous studies

reported, the Generalised PsyCap scale would meet the criteria for classification as a

trait.

Predictive Validity. The study focused on the ability of Generalised PsyCap to

predict the key outcomes included in the studies reported previously in this thesis

and in studies reported by Luthans and collegaues; job satisfaction, affective

organisational commitment and job performance with the hypotheses that

Generalised PsyCap would correlate with concurrent and future levels of these

variables. In addition, it was hypothesised that Generalised PsyCap would be able to

improve prediction of these variables beyond the organisation’s existing selection

assessments. The current study also included some additional measures that were

hypothesised to be predicted by an individual’s PsyCap levels; intention to resign,

engagement and need for recovery.

PsyCap and intention to resign. Intention to resign has been defined as an

“individual’s own estimated probability (subjective) that they are permanently

leaving the organisation at some point in the near future” (Vandenberg & Nelson,

1999, p. 1315). Studies have consistently shown that there are negative relationships

between both job satisfaction and organisational commitment and intention to resign,

and that intention to resign is an important precursor of employee turnover (Tett &

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Meyer, 1993). Thus, the relationship that exists between PsyCap and satisfaction and

commitment suggests that an individual’s PsyCap may also be related to their

intention to turnover. Supporting this, a meta-analysis conducted by Avey, Richard,

Luthans and Mhatre (2011), has shown that a significant negative correlation exists

between PsyCap and turnover intentions (k= 5, corrected r= -.32, sd= .11). The

authors propose that the relationship between the two variables may form for the

following reason, “higher levels of optimism regarding the future and confidence in

their ability to succeed in the current job will motivate them to take charge of their

own destinies (Seligman, 1998), self-select into challenging endeavours (Bandura,

1997), engage the necessary efforts and resources, and persevere in the face of

obstacles (Stajkovic & Luthans, 1998b), rather than become “quitters.” ” (Avey et

al., 2011, p. 132). This relationship was tested in the current study by assessing

whether Generalised PsyCap scale scores were related to intention to resign when the

two variables were measured at the same time as each other, and by assessing the

ability of Generalised PsyCap measured on the Prison Officers’ first day of training

to predict their intention to resign three and nine months later. It was hypothesised

that Generalised PsyCap would significantly correlate with intention to resign

measured when the two measures were taken at the same time. Additionally it was

hypothesised that Generalised PsyCap taken on the first day of training would be

significantly correlated with future measures of intention to resign, and that it would

improve prediction of intention to resign beyond the organisation’s existing selection

measures.

PsyCap and need for recovery. The resource expenditure required of individuals

to fulfil their daily work requirements can be noticed through both physiological

responses and disturbances in mood. When this occurs individuals experience a

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‘need for recovery’, which can be defined as “a conscious emotional state

characterized by a temporal reluctance to continue with the present demands or to

accept new demands” (Sonnentag & Zijlstra, 2006, p. 331). The higher the demands

of the work day on an individual, the higher the individual’s need for recovery will

be at the end of a working day. When need for recovery is higher, individuals need

more time for recovery to be complete. Continuous depletion of resources in the

absence of sufficient recovery will lead to negative consequences such as fatigue,

exhaustion, losses of function, and physical and mental impairment (Sonnentag &

Zijlstra, 2006).

Individual’s higher in PsyCap, who by definition are more resilient and better

able to ‘bounce back’ from adversity, are more likely to quickly recover from work

stress, therefore experiencing a lower need for recovery at the end of the working

day. This has been supported by studies that have suggested that individuals with

higher personal resources (such as PsyCap) are able to deal more effectively with

demanding conditions which may prevent negative outcomes such as exhaustion

(Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2007). In relation to PsyCap

specifically, a number of studies (Cheung, Tang, & Tang, 2011; Wang, Chang, Fu, &

Wang, 2012; Wang, Liu, Wang, & Wang, 2012; Xanthopoulou et al., 2007) have

shown a relationship between PsyCap (or constructs similar to PsyCap) and the long-

term strain outcome of burnout, which can be defined as a syndrome of emotional

exhaustion, depersonalization and reduced personal accomplishment (inefficacy)

(Maslach, Schaufeli, & Leiter, 2001). In the current study need for recovery was

assessed rather than burnout, to increase the sensitivity of the study to predict early

signs of strain. This was considered appropriate as the study was being conducted

over a relatively short time period. It was hypothesised that Generalised PsyCap

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would significantly correlate with need for recovery when the two constructs were

measured at the same point in time. Additionally it was hypothesised that

Generalised PsyCap taken on the first day of training would be significantly

correlated with future measures of need for recovery, and that it could improve

prediction of need for recovery beyond the selection scales already used by the

organisation.

PsyCap and engagement. Work engagement can be defined as “a positive,

fulfilling, work-related state of mind that is characterised by vigor, dedication, and

absorption” (Bakker, Albrecht, & Leiter, 2011, p. 5). The vigor component refers to

whether an employee is energetic towards and stimulated by their work, dedication

refers to whether the employee finds their work meaningful or significant and

absorption refers to whether an employee is fully concentrated on or involved in their

work. High levels of work engagement have been linked to a number of positive

outcomes including increased job performance, job satisfaction, organisational

commitment, organisational citizenship behaviour and reduced intention to resign

(Demerouti & Cropanzano, 2010; Saks, 2006).

Both cross sectional and longitudinal studies have found a relationship between a

construct similar to PsyCap (i.e. a higher order factor comprised of self-efficacy,

organisational-based self-esteem and optimism), and engagement (Xanthopoulou et

al., 2007; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009). Xanthopoulou and

colleagues have proposed a reciprocal relationship whereby personal resources such

as PsyCap may build engagement, but also by which being engaged in work can also

build personal resources. They propose that higher personal resources mean that

individuals feel capable of controlling their work environment, and as a result are

more confident and proud of the work that they do, find meaning in it and therefore

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stay engaged (Xanthopoulou et al., 2007). Additionally, they propose, and have

found evidence to support the theory, that employees who are engaged, are also best

able to mobilise the support from colleagues, receive feedback and create

opportunities at work, which increases their personal (PsyCap) resources

(Xanthopoulou et al., 2009).

As the current study was focused upon the validity of the Generalised PsyCap

scale as an employee selection tool, the relationship investigated was whether

Generalised PsyCap was able to predict future levels of employee engagement. It

was hypothesised that Generalised PsyCap would be correlated to engagement levels

measured at corresponding time periods, and that Generalised PsyCap would be

significantly positively correlated with future levels of engagement. It was

additionally hypothesised that Generalised PsyCap at time one could improve the

prediction of engagement beyond the personality and ability tests already used for

employee selection by the organisation.

Convergent and discriminant validity. In chapters three and four the analyses

planned to assess the PsyCap scales’ relationships with variables shown to relate to

the PCQ-24 were compromised due to the low internal reliability of the Big Five

personality dimensions as measured using the TIPI. In this study, these relationships

were tested using the NEO-PI-R rather than the TIPI to measure the Big Five

personality dimensions. It was hypothesised that the correlations between

Generalised PsyCap and each of the variables would be of a similar magnitude to the

correlations reported by Luthans et al. with the PCQ-24. They found no significant

correlation between PsyCap and age (r= .01), Agreeableness (r= .06) or Openness to

experience (r= -.10), and a moderate positive relationship between PsyCap and

Extraversion (r= .36) and Conscientiousness (r= .39). The correlation between

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Generalised PsyCap and Neuroticism was also assessed, to determine if this

relationship was small in magnitude as reported by Luthans et al., or moderate in size

as indicated in the previous chapter. Given the research based on the individual

PsyCap construct reported in the previous chapter, and the results of that study, it

was hypothesised that the correlation between Generalised PsyCap and Neuroticism

would be moderate in magnitude.

Factor Structure

The factor structure of the Generalised PsyCap scale was not assessed in the

current study as the sample size was insufficient to conduct meaningful analysis.

Method

Design

The participants in this study were Prison Officers newly commencing the

role. As part of the selection process for the role, applicants complete a number of

aptitude and personality assessments. These were made available to the experimenter

and form a part of the data reported in this study.

When commencing the role, the Prison Officers first completed a three month

training program, followed by six months working as a probationary Prison Officer.

In the current study participants completed self-report questionnaires on their first

day of training, in one session within the final two days of their training and in the

final two weeks of their probation period. In addition, the participants’ supervisors

completed an assessment of their performance at the end of training and at the end of

the probation period. Participants had a different supervisor in training to the

supervisor that rated their end of probation performance.

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Participants

All Prison Officers and Juvenile Custodial Officers commencing their

employment with the Department of Corrective Services in Western Australia within

a six month period were invited to participate in this study. All but one consented to

participate, resulting in a total sample size of 80. In this sample were 31 (38.75%)

females and 49 (61.25%) males. Participants ranged from 21 to 59 years of age

(mean age= 37.77, S.D.= 9.11).

Measures

Scales completed as part of the job application process.

NEO-PI-R. The NEO-PI-R is a 240 item self-report questionnaire which

measures the five personality domains; Neuroticism, Extraversion, Openness to

Experience, Agreeableness and Conscientiousness. Previous research has indicated

that the internal consistency of these sub-scales show acceptable levels of internal

consistency, with Cronbach’s alpha reported to be .92, .89, .87, .86 and .90 for the

Neuroticism, Extraversion, Openness to Experience, Agreeableness and

Conscientiousness scales respectively (Costa & McCrae, 1992). Responses to the

NEO-PI-R are indicated on a five point scale ranging from strongly disagree to

strongly agree.

Ability Tests. Participants additionally completed tests to assess their reading

comprehension, verbal reasoning and abstract reasoning abilities. These tests were

previously validated commercially available tests, but will not be described further to

protect the integrity of the employee selection process.

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Scales completed at the start of training.

Generalised PsyCap. Participants completed the Generalised PsyCap scale

with a six-point response scale on their first day of training.

Scales completed at the end of training and end of probation.

Generalised PsyCap. Participants completed the Generalised PsyCap scale

with six point response format at the end of training.

Job satisfaction. As in the study reported in chapter three, job satisfaction

was measured using the five-item scale which Judge, Bono and Locke (2000)

adapted from a measure developed by Brayfield and Rothe (1951). Cronbach’s alpha

for this scale was .72 at the end of training and .87 at the end of probation.

Affective Organisational Commitment. Affective organisational commitment

was measured using Allen and Meyer’s (1990) eight item scale. Cronbach’s alpha for

this scale was .71 at the end of training and .77 at the end of probation.

Intention to resign. Participants were asked to indicate their intention for

staying with their organisation using a single item measure. In this item they were

asked to indicate which of four options best described their intention to stay with the

organisation. The scale was taken from Angle and Perry (1981).

Engagement. Engagement was measured using the nine item version of the

Utrecht Work Engagement scale developed by Schaufeli, Bakker and Salanova

(2006). For this scale participants respond to items such as ‘At work, I feel bursting

with energy’, ‘I am enthusiastic about my job’, and ‘I am immersed in my work’

using a 7 point scale ranging from ‘never’ to ‘everyday’. Although the scale is

comprised of three subscales, only a total engagement score was used for this study

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as it has been argued that the overall score is sometimes more useful for empirical

research than the scores on the three separate dimensions (Schaufeli & Bakker,

2010). Cronbach’s alpha for this scale was .79 at the end of training and .80 at the

end of probation.

Need for recovery. Need for recovery was measured using the 11 items from

Veldhoven and Broersen’s (2003) Need for Recovery scale, with a five point rating

scale ranging from ‘very inaccurate’ to ‘very accurate’. Cronbach’s alpha for this

scale was .88 at the end of training and .91 at the end of probation.

Performance. The performance of each participant was assessed by their

supervisor using a rating scale which was developed specifically for this study in

conjunction with the organisation in which it was run. The scale asked supervisors to

rate the participants’ performance on 27 items using a five point scale ranging from

‘strongly disagree’ to ‘strongly agree’. The scale was developed based upon

behaviours included in the organisation’s internal performance assessment tool. The

scale was divided into five subscales; learning ability, interpersonal skills and

teamwork, communication skills, professionalism and work ethic and personal

coping/stress resilience. An example item from each subscale respectively was

“Readily learns new skills and knowledge”, “Is cooperative with peers”,

“demonstrates effective listening skills”, “shows a high degree of attention to

security”, and “remains calm and patient when placed under pressure”. As factor

analysis of this scale did not support the existence of the five subscales, a global

performance score has been used in this study. Cronbach’s alpha for this scale was

.96 at the end of training and .98 at the end of probation.

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Procedure

When Prison Officers are accepted to the role in the organisation in which

this study took place, they are grouped into training schools which are comprised of

up to 25 new recruits. This group complete three months of training together, before

being dispersed into prisons across the state where they complete a 6 month

probationary period.

In total four training schools were run during the period of this study, and all

but one member of these schools voluntarily participated in the study. Participants

were addressed by the experimenter in a group meeting on their first day of training.

The purpose of the study was explained to them, and, after giving their informed

consent to participate in the study, they completed the Generalised PsyCap scale in a

paper and pencil format.

The experimenter re-visited the participants during a group meeting on the

last or second last day of their three month training. In this session, participants

completed all of the scales listed in the ‘scales completed at the end of training and

end of probation period’ section above in a paper and pencil format. In addition, the

two primary instructors for each training school were asked to rate the participants’

performance during the training using the performance rating scale described above.

The two instructors met together to complete the scales for each member of the

school. Each school had different primary instructors.

At the end of the participants’ six month probation period they were

contacted via email and invited to complete the scales listed above a final time. In

this instance the surveys were completed online. Participants were sent a reminder

email two weeks after the initial email if they had not responded. If they had still not

155##

completed the scale two weeks later, they were sent a paper and pencil version of the

survey which they could complete.

Analytical Strategy

Internal consistency. Cronbach’s alpha was calculated for each PsyCap

component separately, as well as for the total scale for each occasion that the scale

was completed. Similarly to the previous studies reported, a criterion of α > .70 was

set as adequate reliability.

Stability. The three methods recommended by Wright and Quick (2009); test-

retest reliability, a test for invariance of the means over time (repeated measures

ANOVA), and a test of invariance of variance (Mauchly’s test of sphericity) were

used to assess the stability of PsyCap. Following the same criteria set in chapter

three, the criteria for a scale to be considered a trait were (i) the test-retest

correlations higher than .70, (ii) no changes in the mean sample scores across

assessment periods and (iii) homogeneity of variance across assessment periods.

Validity

Concurrent validity. The correlation between scores on the Generalised

PsyCap scale and the outcome variables of job satisfaction, affective organisational

commitment, intention to resign, engagement and need for recovery were evaluated

within each assessment phase.

Predictive validity. The correlation coefficient between Generalised PsyCap

at the start of training and each of the variables it was hypothesised to predict

variance in (job satisfaction, affective organisational commitment, intention to

resign, engagement and need for recovery) were observed.

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Next, for the variables that demonstrated a significant correlation with

Generalised PsyCap, regression analyses were used to assess whether Generalised

PsyCap was a significant predictor of theses variables when the organisation’s

existing selection measures (extraversion, conscientiousness, and ability tests) were

taken into account. In these analyses conscientiousness, extraversion and the ability

tests were entered into block one of the regressions, and Generalised PsyCap at time

one was entered into block two.

Convergent and discriminant validity. The correlations between Generalised

PsyCap and age, Agreeableness, Openness to Experience, Extraversion,

Conscientiousness and Neuroticism were calculated using Generalised PsyCap taken

at the start of training, and the NEO-PI-R scores which were completed as part of the

job application process.

Results

Participation numbers

Given some drop out of participants through the study, the number of

participants at each collection period varied. Pre-selection data were available for all

80 participants, and all participants completed the Generalised PsyCap scale at the

beginning of training. Three participants did not complete the survey at the end of

training assessment, resulting in a 96.25% response rate. By the end of probation,

three participants had resigned from the role and one had been dismissed, leaving a

total of 76 participants in the study. Of these, 56 participants completed the survey,

representing a 73.68% response rate. The lower response rate for the final assessment

period is likely due to the fact that participants were contacted via email to complete

the survey online, whereas the participants completed the assessments in a class

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room environment during their working hours in the assessment periods during

training.

Internal consistency of Generalised and Moment Specific PsyCap.

Table 29 shows that the Generalised PsyCap scale reached an acceptable

level of reliability on all occasions. The resilience and optimism subscales did not

reach acceptable levels on the first testing occasion (α= .650 and .682 respectively),

but for all other testing periods Cronbach’s alpha was greater than .70.

Table 29.

Cronbach’s alpha for each PsyCap component and the full Generalised

PsyCap scale at each testing period.

Testing period Self-efficacy Hope Resilience Optimism PsyCap

Start of training .826 .853 .650 .682 .902

End of training .881 .904 .780 .797 .946

End of probation .904 .921 .754 .748 .935

Stability of Generalised PsyCap. The test-retest reliabilities of the

Generalised PsyCap scale are shown in table 30 below. The table shows that

although the test-retest reliability of the scale reached the criteria for being

considered a trait set in chapter three (>.70) between the start and end of training, the

scale did not meet the criteria across the full period of the study. The test-retest

reliabilities between the end of training and end of probation and start of training and

end of probation were below .70.

The repeated measures ANOVA showed significant differences in the means

of the Generalised PsyCap scales over the three assessment periods F(2, 100) = 4.29,

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p= .02. Mauchly’s test showed that there was homogeneity of variance across testing

periods.

Table 30.

Test-retest reliabilities of the Generalised PsyCap scale.

1 2 3

1. Start of training 1.0

2. End of training .711** 1.0

3. End of probation .536** .649** 1.0

* p < .05

** p < .01

Validity

Concurrent validity. The correlations between Generalised PsyCap and the

outcome variables taken at the same period of time (e.g. end of training job

satisfaction and end of training PsyCap) are shown in table 31. As hypothesised the

Generalised PsyCap scale showed significant relationships with the outcome

variables of job satisfaction, affective commitment and engagement when measured

at time matched periods for both assessment phases. The correlations between time-

matched PsyCap and intention to resign, and need for recovery were significant in

one of the two testing periods each. There were no significant correlations between

Generalised PsyCap and supervisor rated performance.

Table 31.

Correlation coefficients for Generalised PsyCap with outcome variables measured at

corresponding time points.

159## End of

training

End of

probation

Job satisfaction .414** .464**

Affective commitment .398** .291*

Intention to resign -.308** -.144

Engagement .486** .317*

Need for recovery -.211 -.380**

Supervisor rated performance -.080 -.248

* p < .05

** p < .01

Predictive validity. The correlations between Generalised PsyCap measured

at the start of training, with each of the outcome measures at the end of training and

end of probation are shown in table 32. This table shows that Generalised PsyCap

scores at the beginning of training were significantly correlated with job satisfaction,

affective commitment and engagement at the end of training. No other correlations

were significant.

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Table 32.

Correlation coefficients for Generalised PsyCap measured at the start of training with

outcome variables measured at the end of training and end of probation.

End of training End of probation

Job satisfaction .295* .113

Affective commitment .245* -.057

Intention to resign -.153 -.115

Engagement .346** .136

Need for recovery -.207 -.088

Supervisor rated performance -.147 -.117

* p < .05

** p < .01

The unique variance that Generalised PsyCap could account for in predicting

job satisfaction, affective organisational commitment and engagement at the end of

training (the significant correlations from the table above) when Extraversion,

Conscientiousness and the ability tests were also included in the model was

examined using hierarchical regression analyses. The results of these analyses (with

Generalised PsyCap measured at the start of training added into block two of the

regression) are shown in tables 33 and 34. Table 33 shows that Generalised PsyCap

was not able to significantly improve the prediction of any of the outcome variables.

An inspection of table 34 reveals that other than the relationship between

Conscientiousness and job satisfaction, none of the pre-selection variables were

significant predictors of the outcome variables either.

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Table 33.

Coefficient of determination and change in coefficient of determination for Block One (includes Conscientiousness, Extraversion and ability scores), and Block Two (addition of Start of Training Generalised PsyCap) regressed onto outcome variables.

R² ∆R²

Job Satisfaction Block 1 .183

Block 2 .191 .008

Affective Commitment Block 1 .156

Block 2 .172 .016

Engagement Block 1 .145

Block 2 .179 .035

* p < .05, ** p < .01

Table 34.

Beta weights for Reading Comprehension, Verbal Reasoning, Abstract Reasoning, Extraversion, Conscientiousness and Generalised PsyCap at the Start of Training regressed onto job satisfaction, affective commitment and engagement.

Job Satisfaction Affective

Commitment

Engagement

Reading Comprehension -.119 .008 -.051

Verbal Reasoning .048 -.175 -.106

Abstract Reasoning .043 .000 -.073

Extraversion .184 .232 .068

Conscientiousness .252* .144 .212

Start of Training PsyCap .105 .152 .222

* p < .05, ** p < .01

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Convergent and discriminant validity. The correlations between the PsyCap scales

at time one, and age, Agreeableness, Openness to Experience, Extraversion,

Conscientiousness and Neuroticism are shown in Table 35. The relationships

between Generalised PsyCap and age and Agreeableness were non-significant, while

the correlations between Generalised PsyCap and Openness to Experience,

Neuroticism, Conscientiousness and Extraversion were all significant, and medium

in effect size.

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Table 35.

Correlations of Generalised PsyCap (at time one) with other variables.

1 2 3 4 5 6 7. Mean (SD)

1. Generalised PsyCap 1.0 18.96 (2.02)

2. Age .014 1.0 37.78 (9.11)

3. Agreeableness .139 -.10 1.0 127.44 (14.53)

4. Openness to Experience

.389** -.121 -.144 1.0 112.47 (14.52)

5. Conscientiousness .436** -.079 .266* .166 1.0 130.43 (14.30)

6. Extraversion .368** -.152 -.103 .604** .276* 1.0 121.73 (12.38)

7. Neuroticism -.387** .053 -.382** .008 -.644** -.147 1.0 64.15 (16.81)

* p < .05

** p < .01

164$$

Discussion

Overall, results of this study indicate that the Generalised PsyCap scale can

not be considered to be a reliable and valid employee selection tool. While the scale

showed adequate internal consistency, the analyses of the scales stability over time

and predictive validity indicates that it should not be used as a selection tool.

Although the scale showed homogeneity of variance across all three measurement

periods, the repeated measures ANOVA showing significant differences in the

means of the scale over time and the test-retest correlation below the criteria of .70

indicate that the scale did not meet the criteria to be considered a trait.

A question unanswered by this study, is whether the changes in in the

Generalised PsyCap occurred as a result of changing circumstances, or merely as a

consequence of time. This study was conducted in a sample of participants

undergoing significant life changes. Their first assessment was on the first day of a

new job, which represents a transition period for any individual. The three months of

training undergone by the prison officers incorporates a number of confronting

situations. A few of these include role-playing dealing with difficult prisoners,

practicing physical restraint techniques for out of control prisoners and having

pepper spray sprayed into their eyes before being required to restrain a prisoner. The

experimenter was invited to view some of these training activities, which were

visibly stressful for trainees. After completion of the training, the participants then

began working as Prison Officers for the first time. Interviews conducted with

experienced Prison Officers revealed that this is often a more confronting experience

than the training. Officers reported that when confronted with the realities of the

prison environment, many officers that passed their training find it too difficult and

leave the job. This was the case for 3 of the 80 participants in the current study. Of

165$$

note, was a significant drop in the participant’s PsyCap scores between the end of

training and end of probation periods (t(52)= 3.106, p= .003). This drop may be the

result of the stressful experience of being in the prison for the first time, although

more research is required to determine specific factors that may have contributed to

this drop. Some research which supports this assertion is a study conducted by

Luthans and colleagues (Luthans, Norman, Avolio, & Avey, 2008) who reported a

study which showed that employees who perceived themselves to be in a supportive

organizational climate, defined as “the overall amount of perceived support

employees receive from their immediate peers, other departments, and their

supervisor that they view as helping them to successfully perform their work duties”

(p. 225), were more likely to experience higher levels of PsyCap. Similar findings

concerning organisational resources leading to increased personal resources have

been reported by Xanthopoulou and colleagues (Xanthopoulou et al., 2009). It

appears that in the current study, although it was attempted to measure a possible

trait component of PsyCap, the Generalised PsyCap variable may have been

similarly influenced by environmental factors.

While the results of the current study do indicate that the Generalised PsyCap

scale is not sufficiently stable to be used as an employee selection tool, it remains

unanswered whether an individual’s scores would remain stable when not

undergoing such substantial life changes. In addition, the study brings to light the

importance of considering which factors in the transition to new working

environments may cause a decrease in PsyCap, so that the on-boarding of new

employees can be conducted in a more successful manner.

As a stable trait measure is required for a scale to have predictive validity, it

is unsurprising that scores on the Generalised PsyCap scale taken on the first day of

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training were unable to predict any of the outcome variables measured at the end of

probation. However, in spite of the lack of predictive validity, the current study did

demonstrate consistent significant relationships between Generalised PsyCap scores

and job satisfaction, affective organisational commitment and engagement when the

scores are completed concurrently. This result is consistent with earlier studies in this

thesis and shows support for previous studies that have demonstrated a relationship

between these variables (Avey et al., 2011; Luthans, Avolio, et al., 2007;

Xanthopoulou et al., 2009).

The intention to resign and need for recovery variables each showed a

significant relationship with Generalised PsyCap at one of the two testing periods.

The correlation between intention to resign and Generalised PsyCap was significant

(r=.308) when the measures were completed at the end of training, but not at the end

of probation (r= -.144). As the previously discussed meta-analysis showing that a

relationship exists between PsyCap and intention to resign (Avey et al., 2011) only

included 5 independent samples, this study highlights the need for more research to

be conducted in this area before conclusions about this relationship can be made. The

correlation between need for recovery and Generalised PsyCap was significant when

the measures were completed at the end of probation (r= -.380), but was non-

significant at the end of training (r=-.211). As need for recovery is proposed to build

up progressively when there is insufficient time for recovery each day (Sonnentag &

Zijlstra, 2006), it is plausible that the reason that this relationship only came to light

when the participant’s need for recovery had had sufficient time to develop.

Supporting this supposition is the finding that need for recovery scores were

significantly higher at the end of probation than they were at the end of training

(t(51)= -2.59, p= .013).

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In contrast to the previous studies in this thesis, the analyses in this study

showed no significant relationships between Generalised PsyCap and performance.

In fact, although not significant, it is noteworthy that all correlation coefficients were

negative, indicating that the higher a participant’s self-rated PsyCap, the lower their

supervisors rated their performance. This finding is in contrast to studies reported by

Luthans et al (Luthans, Avolio, et al., 2007) which have shown positive relationships

between self-rated PsyCap and supervisor rated performance. There are a number of

possibilities for this finding. The first, is that the performance measure used in this

study was inadequate. Rather than using a previously validated scale, a purpose built

measure was designed to measure specific performance of a Prison Officer.

However, the scale was based upon the performance measures already used within

the organisation so it should be considered an indication of what is viewed as

positive performance within the organisation.

A more likely explanation as to why PsyCap scores were not related to

performance ratings is that the relationship between PsyCap and performance is

dependant upon the nature of the job. This assertion would align to evidence

discussed in the literature review of this thesis, that optimism is only predictive of

job performance in certain job types. The study conducted by Satterfield, Monahan

and Seligman (1997) using law students led to the suggestion that in domains that

require more caution and reality appreciation than initiative or creativity, pessimism

as opposed to optimism may be advantageous to job performance. The results of the

current study suggest that the same may be true for PsyCap. Interestingly, the

correlations between each of the individual PsyCap components (self-efficacy, hope,

optimism and resilience) taken on the first day of training and performance measured

at both the end of training and end of probation were all non-significant but in the

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negative direction. Thus, this research highlights that a more thorough examination

of the relationship between PsyCap and performance in different job types is

required to fully understand the scale’s relationship with job performance.

The convergent and discriminant validity analyses show for the most part that

the correlations between Generalised PsyCap and age, Agreeableness, Openness to

Experience, Conscientiousness and Extraversion, were of similar magnitude to those

reported by Luthans and colleagues (Luthans, Avolio, et al., 2007) for their original

PsyCap scale (PCQ-24) and the same variables. Their study reported no significant

correlation between age (r= .01) or Agreeableness (r= .06). The correlations in the

current study between these variables and Generalised PsyCap were also non-

significant and similar in magnitude (r= .01 and r= .14 for age and Agreeableness

respectively. The moderate positive correlations between PsyCap and

Conscientiousness and Extraversion (r= .39 and r= .36 respectively) reported by

Luthans et al. were also found in the current study using the Generalised PsyCap

scale (r= .44 and .37 respectively). The similarity of these correlations indicate that

attempts to change the wording of the PCQ-24 to tap into a trait construct rather than

the state-like construct have not significantly changed the dimension being

measured.

The relationship between Generalised PsyCap and Openness to Experience

(r= .39, p < .01) however was significantly different to the relationship between the

PCQ-24 score and Openness to Experience (r= -.10, p> .05). Similarly, the

relationship between Generalised PsyCap and Neuroticism (r= -.387) was larger than

that reported for the PCQ-24 and Neuroticism (r= -.12). As there has been a lack of

studies reporting the correlation between PCQ-24 scores and the Big Five personality

domains, it is difficult to determine if the result in the current study represents a

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change in the Generalised PsyCap variable compared to the PCQ-24, or whether the

result in either this study or the study conducted by Luthans et al. (2007) were

abnormal. Theoretically however, it appears logical that individuals who were more

confident that they would succeed in new situations (ie. higher self-efficacy and

optimism) would be more likely to be open to new experiences. In support of this,

some studies have shown positive correlations between optimism and Openness to

Experience (Lounsbury, Saudargas, & Gibson, 2004; Sharpe, Martin, & Roth, 2011),

although these findings have not been consistent shown in the literature (Sharpe et

al., 2011). Similarly Parker (2000), reported a significant moderate positive

relationship (r= .42) between Role Breadth Self-Efficacy (the way self-efficacy is

also conceptualised in the PCQ-24 and Generalised PsyCap scales) and a construct

termed change receptiveness, which contained items such as “I am most comfortable

with a stable work environment in which things tend to stay the same” and “I like

being in a work environment where there is a lot of change occurring”. Thus it

appears that there are both theoretical reasons, and some research to support the

theory that PsyCap may be expected to correlate positively with Openness to

Experience as found in this study.

In terms of the relationship between PsyCap and Neuroticism, it is noted that

the results of the current study are in line with those reported in the previous chapter,

adding weight to the possibility that the relationship between PsyCap and

Neuroticism is greater than that reported by Luthans et al. (Luthans, Avolio, et al.,

2007).

Limitations. A key limitation to this study is that while attempting to

validate the Generalised PsyCap scale as a selection tool, it was only possible to

complete the study using Prison Officers selected to the role using the organisation’s

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pre-existing selection criteria. This involves not only psychometric assessment as

discussed above, but also psychological interview assessment with potential

candidates. It is plausible that this may have distorted the relationship between

Generalised PsyCap and the outcome variables as only employees already deemed

psychologically fit for the role were included as part of this study.

In addition, conducting this study within employees from a very specific

occupation limits the extent to which findings can be generalised to a wider range of

occupations. Similar research in other occupations and organisations would be

required to allow findings to be generalised.

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CHAPTER SIX: GENERAL DISCUSSION

PsyCap, conceptualised as a ‘state-like’ construct measured by the PCQ-24

has received a degree of attention in recent years of research. This research has

shown that PsyCap is related to a number of positive outcomes for both individuals

and their workplaces alike. For example individual’s higher in PsyCap have been

reported to have more positive attitudes towards their work, increased well-being and

higher performance levels (Avey et al., 2011). However, the review of the literature

in chapter one showed that each of the PsyCap components has been successfully

conceptualised and measured as a trait, opening the possibility that there may also a

trait component to PsyCap. This research was therefore concerned with determining

if PsyCap could be measured as a pure state and a stable trait, and whether the trait

version of the scale could be used as an employee selection tool.

The review of the PCQ-24 reported in chapter two revealed that the survey

was composed of some items considered appropriate for measuring a trait, and some

items considered appropriate for measuring a state. Thus, to obtain a more clear

picture as to whether PsyCap should be conceptualised as a state, a trait, or both, two

new scales, the Moment-Specific and the Generalised PsyCap scales were created.

Throughout the research conducted, these scales have demonstrated adequate internal

reliability (indicated by Cronbach’s alpha). The research then sought to examine a

number of key questions regarding the stability of these scales over time and their

validity. The key questions were;

• Should be Moment Specific PsyCap scale be considered a state or a

trait measure?

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• Should the Generalised PsyCap scale be considered a state or a trait

measure?

• Does the Moment Specific PsyCap scale demonstrate the predicted

relationships with other key variables?

• Does the Generalised PsyCap scale demonstrate the predicted

relationships with the other key variables?

• Can be Generalised PsyCap scale be utilised as a valid employee

selection tool?

Should the Moment Specific PsyCap scale be considered a state or trait

measure?

To determine if the Moment Specific and Generalised PsyCap scales should

be considered to be measures of state or trait constructs, the assessments

recommended by Wright and Quick (2009) were conducted. The criteria set out in

chapter three outlined that for a construct to be considered a state, a test-retest

reliability of less than .50 was considered sufficient.

The first assessment of the Moment Specific PsyCap scale presented in this

research is not considered a valid examination of the scale’s stability over time, as

evidence presented in chapter four has shown that responses to the state scale in

chapter three were highly influenced by order effects. Therefore, the only study by

which to assess the stability of the Moment Specific PsyCap scale is the study

presented in chapter four. In this study, only the two conditions in which the Moment

Specific PsyCap scale was presented before the Generalised PsyCap scale and

therefore not influenced by order effects will be considered here.

The test-retest reliabilities for the Moment Specific PsyCap scale reported in

those conditions was .82 when there was a 3 week gap between testing sessions,

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indicating a construct more stable than a state. The test-retest reliabilities dropped to

ranging from between .56-.59 when this gap was extended to five or eight weeks.

These fell closer to the .50 criteria set for a state, but still does not meet the criteria

set for classification as a state.

An interesting finding in these studies is that the test-retest reliability of the

Moment Specific PsyCap scale was significantly higher than that of the PCQ-24

(which has been reported to be .52 over a two week period (Luthans, Avolio, et al.,

2007)), despite the modifications made to the PCQ-24 to form the Moment Specific

PsyCap scale. These changes involved the re-wording of many items because the

review and rating process described in chapter two indicated that many PCQ-24

items were considered more appropriate trait than state items. Thus, results of this

thesis align with Luthans et. al’s conclusion for the PCQ-24, that the Moment

Specific PsyCap scale should be considered a ‘state-like’ construct, which has been

described as “they are not as stable and are more open to change and development

compared with “trait-like” constructs such as Big Five personality dimensions or

core self-evaluations, but importantly that they also are not momentary states

(Luthans, Avolio, et al., 2007, p. 544).” This appears to be the case even when the

wording of the items is framed to capture a momentary state. The finding that the

Moment Specific PsyCap scale was more stable than the PCQ-24, does point towards

the need for more longitudinal research to be conducted to allow greater

understanding of both the PCQ-24 and the Moment Specific PsyCap scale.

Should the Generalised PsyCap scale be considered a state or trait measure?

Following the argument presented by Wright and Quick (2009) the

Generalised PsyCap scale was required to meet the following criteria in order to

conclude that it reflected trait-like qualities; (i) the test-retest correlation was

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required to be higher than .70, (ii) the mean score for the scale across a whole sample

would not change between testing periods and (iii) there would be homogeneity of

variance of the scale score across testing periods.

In the current research the stability of the Generalised PsyCap scale was

assessed in four different samples in which the scale was presented either before, or

in the absence of the Moment Specific PsyCap scale and therefore not influenced by

order effects. The summary of the findings from these studies presented in the table

below shows that the Generalised PsyCap scale met the criteria for being considered

a trait in all samples except for the final study involving Prison Officers.

Table 36.

Summary of the findings for the Generalised PsyCap scale meeting the

criteria for classification as a trait.

Sample Time frame for study

Test re-test reliability

range

Invariance of means?

Homogeneity of variance?

1. Not for profit employees (chapter 3)

8 weeks .711 Yes Yes

2. Student sample 1 (G-MS-4 condition, chapter 4)

5 weeks .722- .844 Yes Yes

3. Student sample 2 (G-MS-6 condition, chapter 4)

8 weeks .812- .857 Yes Yes

4. Prison Officer sample (chapter 5)

9 months .711- .536 No Yes

The results of the current study therefore show that when the items of the

PCQ-24 are framed to tap into a general tendency to think or fell a particular way, it

is possible to measure a much more stable construct than when the questions are

framed in a moment specific or state manner. However, the Generalised PsyCap

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scale still did not meet the criteria for a trait in all testing conditions, perhaps

indicating that it is also best described as ‘state-like’. Results show that the

Generalised PsyCap scale is not sufficiently stable to be considered a valid employee

selection tool, and point towards the need to understand what factors may influence

an individual’s PsyCap to change. This will be discussed further in the ‘limitations

and further research’ section of this chapter.

Does the Moment Specific PsyCap scale demonstrate the predicted relationships

with other key variables?

The Moment Specific PsyCap scale was hypothesised to correlate with a

number of variables in a similar manner to the correlations reported by Luthans and

colleagues (Luthans, Avolio, et al., 2007). They reported no significant correlation

between PsyCap and age, Agreeableness or Openness to Experience, a small

negative correlation between PsyCap and Neuroticism, moderate positive

relationships with Extraversion and Conscientiousness and a strong positive

relationship with Core Self-Evaluations. In considering the answer to this question,

only the two samples of participants who completed the Moment Specific PsyCap

scale in conditions where it was not influenced by the Generalised PsyCap scale will

be considered. The correlations reported in the current study for these conditions are

all of the same magnitude and direction as those reported by Luthans et al. with the

exception of Neuroticism, for which moderate rather than small correlations were

reported. The finding of similar correlation sizes for the majority of variables

indicates that the changes made to the PCQ-24 to form the Moment Specific PsyCap

scale did not significantly change the construct measured by the scale. The moderate

correlation between PsyCap and Neuroticism makes theoretical sense and has been

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supported by previous literature based upon the individual PsyCap components (as

discussed in chapter four), and has been found consistently throughout this research.

The Moment Specific PsyCap measure was also hypothesised to correlate

with job satisfaction, affective organisational commitment and self-rated

performance when the participants completed self-report measures of these

constructs at the same point in time. In the conditions considered accurate

representations of the scale, moderate to strong positive correlations were reported in

all cases. This finding shows the importance of ensuring that an individual can

experience high levels of PsyCap while at work, as it has important implications for

their job attitudes and behaviours.

Although hypothesised to be related to concurrent measures of job

satisfaction, affective organisational commitment and performance, the Moment

Specific PsyCap scale was not hypothesised to be able to predict future levels of

these variables because levels of Moment Specific PsyCap were hypothesised to

fluctuate too much over time to be able to predict future outcomes. Examination of

the relevant conditions show that this hypothesis was mostly not supported. For both

conditions Moment Specific PsyCap at time one had moderate positive correlations

with job satisfaction, affective commitment and performance at times two and three.

This finding is reflective of the fact that the Moment Specific PsyCap scale was more

stable over time than hypothesised.

Does the Generalised PsyCap scale demonstrate the predicted relationships with

the other key variables?

Similarly to the Moment Specific PsyCap scale, the Generalised PsyCap

scale was also hypothesised to correlate with age, Core Self-Evaluations and the

relevant Big Five personality dimensions in a similar manner to those reported by

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Luthans, Avolio et al. (2007). The correlations between the Generalised PsyCap

scale and these variables are shown below for all samples in which the Generalised

PsyCap scale was presented prior to the Moment Specific scale.

Table 37.

Correlation coefficients for the Generalised PsyCap scale correlated with age,

core self-evaluations and Big Five personality dimensions in all relevant samples in

this thesis.

Not for profit employees (chapter 3)

Student sample 1 (G-MS-4 condition, chapter 4)

Student sample 2 (G-MS-6 condition, chapter 4)

Prison Officer sample (chapter 5)

Age .289** .223 .043 .014

Agreeableness - - - .139

Openness to Experience

- - - .389**

Extraversion .359** .293** .340** .368**

Conscientiousness .377** .329** .248** .436**

Neuroticism - -.348** -.448** -.387**

Core self-evaluations

.651** .661** .658** -

*p < .05

**p < .01

The table above shows that there were small and mostly non-significant

correlations between Generalised PsyCap and age and Agreeableness, moderate

positive correlations between Generalised PsyCap and Extraversion and

Conscientiousness and large positive correlations between Generalised PsyCap and

Core Self-Evaluations. These correlations are as hypothesised and reflective of those

reported by Luthans and colleagues.

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However, in contrast to the findings of Luthans et al. (Luthans, Avolio, et al.,

2007) a moderate relationship was found between Generalised PsyCap and Openness

to Experience. As discussed in the previous chapter, this result makes theoretical

sense (as an individual who was more confident that they would succeed in new

situations (higher self-efficacy and optimism) would be more likely to be open to

new experiences) and is in line with some previous research showing correlations

between optimism and self-efficacy and openness to experience (Lounsbury et al.,

2004; Parker, 2000; Sharpe et al., 2011). Similarly, a moderate relationship between

Generalised PsyCap and Neuroticism has been consistently reported in this thesis.

The finding also makes theoretical sense, and supports previously reported literature

based on the PsyCap constructs. Therefore results of this study suggest that contrary

to the findings of Luthans, Avolio et al. (Luthans, Avolio, et al., 2007) a moderate

relationships exists between the PsyCap construct and Openness to Experience and

the PsyCap construct and Neuroticism.

The Generalised PsyCap scale was also hypothesised to correlate with both

concurrent and future reported levels of job satisfaction, affective organisational

commitment, performance, intention to resign, engagement and need for recovery.

The summary of the findings across all studies (shown in table 38 below) shows that

Generalised PsyCap was consistently correlated with concurrent measures of job

satisfaction, affective commitment and engagement. The correlations between

PsyCap and intention to resign and need for recovery were only found in half of the

observations, indicating a need for more research to fully understand these

relationships.

Of note is the significant difference in the relationship between performance

and Generalised PsyCap in the first three samples reported compared to the Prison

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Officer sample. There are a number of possibilities as to why this relationship

difference occurred. Firstly, a key difference between the studies that may have

contributed to the difference is that the final study is the only one in which the

performance measure was not a self-report measure. It is plausible that for two

people performing at the same level, the one with high PsyCap may interpret their

performance as being superior. However, previous studies have shown that PsyCap

is predictive of performance when measured via self-report, supervisor ratings or

through objective organisational ratings. In fact, a recent meta-analysis conducted by

Avey et al. (2011) concluded that there was no meaningful difference in the

relationship between PsyCap and performance when performance was measured

through the three different sources (self-report, supervisor rated or objective data).

A more likely reason that the relationship between Generalised PsyCap and

performance was different in the final study compared to other studies is the nature

of the work conducted by the Prison Officers who participated in the final study. As

discussed in the previous chapter and literature review, research has shown that there

are some job types for which higher levels of optimism are beneficial to performance

and some types for which it is not. This appears to be the case in the current study,

where the relationships between Generalised PsyCap and performance were all non-

significant, but trending in the negative direction. This makes sense when

considering that items such as “I usually expect the best when things are uncertain

for me at work”, from the optimism component of the PsyCap scale. In a job where a

high degree of vigilance is required (such as being a Prison Officer), this attitude

towards the unknown is unlikely to result in increased job performance. Interestingly

however, the negative trend in the relationship between PsyCap and performance

was consistent for all four PsyCap components individually (Hope, Self-efficacy,

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Resilience as well as Optimism), as well as the composite measure. In light of this,

further research is recommended to determine for which job types PsyCap is

beneficial to performance and for which job types it may have no benefit or be

detrimental to performance.

Table 38 shows that, as hypothesised, PsyCap was positively correlated with

future measures of job satisfaction, affective commitment and performance for the

first three samples listed. The findings were much less consistent for the Prison

Officer sample. This is due to the fact that in this sample, the scale did not meet the

criteria for a trait, and would therefore not be expected to be able to predict future

outcomes.

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Table 38.

Correlation coefficients for the Generalised PsyCap scale correlated with

concurrent and future measures of job satisfaction, affective commitment,

performance, intention to resign, engagement and need for recovery in all relevant

samples in this thesis.

Not for profit employees (chapter 3)

Student sample 1 (G-MS-4 condition, chapter 4)

Student sample 2 (G-MS-6 condition, chapter 4)

Prison Officer sample (chapter 5)

Concurrent Measures

Job satisfaction .279**-.586** .485**-.608** .464**-.659** .414**-.464**

Affective Commitment

.046-.219* .346**-.359** .381**-.451** .291*-.398**

Performance

.389**-.565** .380**-.596** .351**-.655** -.080- -.248

Intention to Resign

- - - -.144- -.308**

Engagement - - - .317*- .486**

Need for recovery - - - -.211- -.380**

Future Measures

Job satisfaction .452** .519**-.554** .441**-.508** .113-.295*

Affective Commitment

.260 .247- .294** .317**-.345** -.057- .245*

Performance

.332* .297**-.483** .431**-.578** -.117- -.147

Intention to Resign

- - - -.115- -.153

Engagement - - - .136- .346**

Need for recovery - - - -.088- -.207

*p < .05

**p < .01

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Can the Generalised PsyCap scale be utilised as a valid employee selection tool?

The premise of this research was that if it was possible to measure a stable, or

trait component of PsyCap then this may mean that organisations are able to select

employees already high in PsyCap into new job roles, either as well as or instead of

investing in training to increase employee PsyCap levels. Results of the current study

suggest that although it is possible to measure a considerably more stable component

of PsyCap than that measured by the PCQ-24, it is not sufficiently stable to be

considered a valid selection tool. Despite the studies reported in chapters three and

four showing that the Generalised PsyCap scale did meet the criteria for being

classified as a trait (in the conditions where it was presented before the Moment

Specific PsyCap scale), it did not remain stable through the final study and was

therefore not able to predict job-related attitudes, performance or well-being at the

end of the Prison Officer’s probation period. Although replicating this research in a

variety of samples is recommended to ensure generalisability, the current study

suggested that the Generalised PsyCap scale should not be considered a valid

employee selection tool.

Limitations and Further Research

There are a number of limitations to this research, the first of which relates to

the finding that PsyCap did not meet the criteria for a trait in the final study using

Prison Officers. A key limitation with respect to this finding is that the current

research does not provide definitive insight into the reason that the results of this

final study differed from the studies reported in the preceding chapters. This is

because two key factors were varied in the final study compared to the previous

studies; it was conducted over a longer period of time (nine rather than two months)

than the previous studies, and the participants in the final study were experiencing a

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period of significant change (the transition to a new job), compared to the relative

stability (continuing in a current job) of participants in the proceeding studies. Thus,

two explanations for the difference in findings are plausible. The first is that

Generalised PsyCap changed in participants in the final study merely as a function of

time, and that the same change may have been found for the participants in the other

studies if the study was conducted over nine rather than two months. The second is

that the change in PsyCap levels in the final study was in some way related to the

environmental changes as the participants moved through their training process and

into their new job roles as Prison Officers.

Research aiming to determine whether time or environmental changes related

to the job transition caused the change in PsyCap should be conducted using

longitudinal research in two different samples of employees over the same length of

time. One sample should include employees who have already worked in their

current job role for a substantial period of time, and who continue in the same job

role for the period of the study. The other should include participants experiencing a

period of transition. The study may begin on their first day of a new job (in a similar

manner to the Prison Officer study), or even during the job search period. If these

two groups of employees complete the Generalised PsyCap questionnaire at regular

intervals over the same extended length of time the source of the change to

Generalised PsyCap could be determined. If PsyCap levels changed for both samples

this would indicate that changes in Generalised PsyCap occur as a function of time,

if PsyCap levels changed for only the transition group this would indicate that

changes in Generalised PsyCap occur as a result of environmental changes. It is

possible that changes could occur in only the non-transition group or in both groups

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of employees, but the research reported here indicates that these findings are

unlikely.

A second question unanswered by both the current research and the suggested

research design above is if changes in Generalised PsyCap are caused by

environmental factors, which environmental factors have an impact? It is important

to identify what factors may cause an increase or reduction in an individual’s PsyCap

given its important implications for work performance, attitudes and well-being. The

study reported for Prison Officers showed a mean decrease in participants’ PsyCap

levels between the end of training and end of probation periods. A greater

understanding of factors that may have contributed to this could allow for

organisations to better design the ‘on-boarding’ or transition to a new workplace

process so that employees experience minimal change or even an increase in PsyCap

as they move to new job roles.

Some research has been conducted in factors that influence an individual’s

PsyCap (as measured by the PCQ-24). Early research conducted by Luthans and

colleagues has shown that short training sessions focused upon individual skills and

thought processes have been able to increase individual’s PsyCap levels (Luthans,

Avey, et al., 2006; Luthans, Avey, et al., 2008). Additionally, research has shown

that employees who perceive a higher degree of organisational support (defined in

terms of “the overall amount of support employees receive from their immediate

peers, other departments, and their supervisor” (Luthans, Norman, et al., 2008, p.

225)) are more likely to experience higher levels of PsyCap (Luthans, Norman, et al.,

2008). A greater understanding of specific job-related factors that are within an

organisation’s control could allow for organisations to ensure the all employees

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experience maximal levels of PsyCap, and to ensure that the transition to work in a

new job role doesn’t reduce an individual’s PsyCap.

Some evidence of factors to investigate are revealed through a recent

longitudinal study that showed a reciprocal relationship between job and personal

resources by which job resources at time one were correlated with personal resources

at time two, but also personal resources at time one were correlated with job

resources at time two (Xanthopoulou et al., 2009). In this study job resources were

operationalised in terms of autonomy, social support, supervisory coaching,

performance feedback and opportunities for professional development. Personal

resources were very similar to PsyCap, measured as a combination of self-efficacy,

organisation-based self-esteem and optimism. Thus, results of this study strongly

suggest that the same job resources may influence individual’s PsyCap levels. It is

recommended that further longitudinal research is conducted into the role that these

organisational factors have on individual’s PsyCap levels.

This research also points towards strategies that could be used at on-boarding

that may help to ensure that indivudals don’t experience a drop in PsyCap as a result

of the transition to a new job. For example, buddying the new with an existing

employee could increase levels of social support, an orientation which outlines

developmental and career pathways within the organisation could increase their

immediate awareness of professional development opportunities, and regular

scheduled coaching sessions between a new employee and their supervisor could

allow the employee to experience increased supervisory support, and receive

adequate feedback. Research to determine which of these interventions result in

PsyCap levels remaining stable (or even increasing) during the transition period

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could provide valuable information to allow the development of effective on-

boarding programs for new employees.

The results of the current research additionally show the need to investigate

the types of jobs for which increased levels of PsyCap are beneficial for

performance, attitudes and well-being and those for which it is not. The results of the

Prison Officer study still suggest that implementing interventions or strategies to

increase individual PsyCap would be beneficial to organisations involved in the

Corrective Services and similar job types, as this would allow for improved

employee attitudes and well-being, without having an impact on performance.

However, the negative trending of the correlations between performance and PsyCap

in this sample raise the question as to whether there are specific job types for which

higher levels of PsyCap are detrimental to performance. Research with optimism

suggests that this may be the case is jobs that require a high degree of scepticism,

vigilance, caution or reality appreciation (such as lawyers) rather than initiative,

creativity and a positive outlook (Satterfield et al., 1997). Furthermore, should there

be some occupations for which PsyCap is detrimental to performance, it remains

unclear as to whether the employee would still experience improved work attitudes

and well-being as a result of higher PsyCap. It seems plausible that if high PsyCap

resulted in poor job performance, this would also translate into less desirable

attitudes towards work and decreased well-being. To answer these questions it is

necessary to examine the relationships between PsyCap and job attitudes,

performance and well-being across a wide range of jobs. Despite the benefits of

longitudinal research, it is recommended that this research stream would benefit from

cross sectional research that could more easily involve a wide range of types of

occupation.

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A final question that remains unanswered in this research is why the Moment

Specific PsyCap scale demonstrated a higher degree of stability than that reported for

the PCQ-24. A limitation of this research is that the PCQ-24 was not administered

alongside the Moment Specific and Generalised PsyCap scales, thus it remains

unknown if the difference between the stability of the PCQ-24 and the Moment

Specific PsyCap scales is an artefact of the samples used, or a true difference that

exists. Longitudinal research in which both the Moment Specific PsyCap scale and

the PCQ-24 are administered to participants is required to answer this question.

Conclusion

Although research conducted with the PCQ-24 has shown the PsyCap

construct to be related to important outcomes including improved employee

attitudes, performance and well-being (Avey et al., 2011), a review of the literature

concerning each of the PsyCap components (hope, optimism, self-efficacy and

resilience) revealed that each of the constructs had been successfully conceptualised

as both state and trait measures, which differ from the ‘state-like’ conceptualisation

of PsyCap presented by Luthans and colleagues (Luthans, Avolio, et al., 2007).

Given the different uses of state and trait constructs for organisations (ie.

development or selection), this research aimed to construct pure state and pure trait

versions of the PCQ-24, termed the Moment Specific and Generalised PsyCap

scales. The internal reliability, stability over time and validity of these scales were

then assessed.

The initial studies suggested that it was possible to measure a trait component

of PsyCap using the Generalised PsyCap scale, although the Moment Specific

PsyCap scale was not sufficiently variable over time to meet the classification of

being a state. Analysis conducted within these studies showed that the scales

188$$

generally correlated with personality variables and work attitudes and performance in

a similar manner to the way the PCQ-24 correlated with these variables, indicating

that the changes made to the PCQ-24 to create the Moment Specific and Generalised

PsyCap scales did not significantly alter the underlying construct being measured.

The final study aimed to assess the validity of the Generalised PsyCap scale

as an employee selection tool. Results of this study indicated that it should not be

used for employee selection as in this final study the scale did not meet the criteria

for classification as a trait, and therefore was not able to predict future employee

attitudes and behaviours. Instead, the mean drop in PsyCap scores for participants in

this study point towards the need to better understand the environmental or

organisational factors that can influence an individual’s PsyCap so as to allow the

development of employee on-boarding processes which allow high levels of PsyCap

to be maintained or low levels increased. Additionally, results of the final study

showed that while Generalised PsyCap scores were related to concurrent measures of

employee attitudes and well-being, there was no relationship between Generalised

PsyCap and performance. Therefore the results of this study indicate that it is

important to develop a greater understanding of the job types for which PsyCap is

beneficial to performance and those for which it is not.

189$$

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