the development and assessment of a quality of life measure (casp-19) in the context of research on...

The development and assessment of a Quality of Life measure (CASP-19) in the

context of research on ageing

Dick WigginsDepartment of Quantitative Social ScienceThe Institute of EducationThe University of LondonEmail: r.wiggins@ioe.ac.uk

CCSR Seminar University of Manchester, 4th December 2007

Some history…………..

CASP-19 is a theory based Quality of Life Measure developed under the UK’s

Economic and Social Research Council’s Growing Older Programme (2000-2003)

Original Team:

David BlanePaul HiggsMartin HydeDick Wiggins

Followed by:

Quality of Life and Resilience in Early Old Age

2003-06

David Blane, Dick Wiggins, Scott Montgomery,Gopal Netuveli and Zoë HildonESRC’s Priority Network on Human Capability and Resilience

Network Coordinator: Mel Bartley , UCL

Research Settings for evaluation:

• The Boyd-Orr sample

• The English Longitudinal Study of Ageing (ELSA)

•The British Household Panel Survey (BHPS, Wave 11)

The Boyd-Orr sample

1937-39 Boyd-Orr Study; childhood diet and health Gunnell at al, Public Health 110, 1999

1997-98 Life Grid Interview: retrospective data,Physiological and anthropmorphic measuresBerney and Blane, Social Science and Medicine, 45, 1997

2000 Postal QuestionnaireHyde et al., Aging and Mental Health, 2003

Boyd-Orr 2000

Some theory………….

Needs Satisfaction and Quality of Life

Maslow, A.H. (1963) Toward a psychology of being

Giddens, A. (1990). The consequences of Modernity

Doyal, L. and Gough, I. (1991). A theory of human need

Laslett, P. (1996). A fresh map of life

Concepts and indicators……

Quality of life

Concepts and indicators……

Quality of life

Control

Autonomy

Self-realisation

Pleasure

Concepts and indicators……

Quality of life

Control

Autonomy

Self-realisation

Pleasure

Concepts and indicators……

Quality of life

Control

Autonomy

Self-realisation

Pleasure

Item 1

Item 2

Item 3

Item 4

Item 19

Concepts and indicators……

Quality of life

Control

Autonomy

Self-realisation

Pleasure

Item 1

Item 2

Item 3

Item 4

Item 19

CONTROL

My age prevents me from doing the things I would like to do I feel that what happens to me is out of my controlI feel free to plan for the futureI feel left out of things

Alpha = 0.6

AUTONOMY

I can do the things I want to doFamily responsibilities prevent me from doing what I want to do I feel that I can please myself what I doMy health stops me from doing the things I want to doShortage of money stops me from doing the things I want to do

Alpha = 0.6

Self-realisation

I feel full of energy these daysI choose to do things that I have never done beforeI fell satisfied with the way my life has turned outI feel that life is full of opportunitiesI feel that the future looks good for me

Alpha = 0.8

Pleasure

I look forward to each dayI feel that my life has meaningI enjoy the things that I doI enjoy being in the company of othersOn balance, I look back on my life with a sense of happiness

Alpha = 0.8

The scale found a niche…..

Take up in …..

English Longitudinal Study of Ageing (ELSA)

British Household Panel Survey (BHPS) Retirement Module Wave 11

Study of Health, Alcohol and Psychosocial factors inEastern Europe (HAPPIE)

An evaluation of Camden’s Quality of Life Strategy for older citizens

NCDS 2008 as they reach 50 years of age

Fuelling motivation

• ‘so much is known about the variations which can be produced, and so little is known about which variation is most nearly correct’, McNemar, 1946.

• Confirmatory factor analysis of the GHQ-12: can I see that again? Campbell et al., Australian and New Zealand J of Psychiatry 2003; 37: 475-483

Some reflection and acknowledgement

Ed Diener

Some reflection and acknowledgement

Ed Diener

Subjective Measures of Well-Being

Three possibly four pillars• Self-report: perception is reality

• Positive and negative aspects of central concept: life domains are important

• The need for global assessment

• Theory distinguishes the usefulness of your measure

Measurement Models

Evaluation Strategy

• Fit three measurement models for complete data across three research settings using multigroup analysis in AMOS.

• Reflect, assess three measurement models for two national data sets taking account of measurement level and item non-response in Mplus.

Control

Autonomy

Pleasure

Self-realization

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

1

1

1

1

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

CASP19 First order model

QOL

Control

Autonomy

Pleasure

Self-realization

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

1

1

1

1

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

rC

rA

rP

rS

v ar_a

v ar_a

v ar_a

v ar_a

CASP19 second order model

Assessing goodness of fit

Aim: to reproduce covariance/correlation matrix

Criteria are typically functions of discrepancy

A selection of criteria

2 or CMIN represents the discrepancy between the sample covariance matrix and the fitted matrix

Tends to be substantial when model does not fit or sample large

Resulting in a plethora of indexes which take a morepragmatic approach to the evaluation process (Byrne,2001).

Key reference: Bollen, K.A. and Long, J.S. Testing structural equation models. Newbury Park, CA: SAGE, 1993

2 / DF the first on the block

Other adjuncts to 2 include:

Goodness of fit index GFI

A measure of the relative amount of variance and covariance explained

Adjusted GFI Adjusts for degrees of freedom

Both GFI and AGFI range between 0 and 1 (near 1 good)

Root Mean Square Error of Approximation

RMSEA

A measure of discrepancy per degree of freedom

Values up to .08 indicate a reasonable fit

RMSEA > 0.10 ‘poor’ < 0.05 ‘good’

Model fit indices continued

• Tucker Lewis Index (TLI)

{ (χ20 /df0 ) - (χ2

1 /df1 ) } / { (χ20 /df0 ) -1 }

• Comparative Fit Index (CFI)

{ (χ20 /df0 ) - (χ2

1 /df1 ) } / (χ20 – df0 )

These measures are calculated in relation to the null model where all parameters are set to zero. For both, >0.90 ‘good’, >0.95 > ‘very good’.

Moving on…..

Multigroup analysis

Testing the invariance of the factorial measurement andstructure across sample settings

Involves comparing an unconstrained model forthe samples as a whole with a constrained modelacross the three groups.

Modelling Strategy………….

separate analyses for three settings

Modelling Strategy………….

separate analyses for three settings

Complete data only

BO-2000 : 198 ELSA : 9910

BHPS : 6471

All aged 50 +

Modelling Strategy………….

separate analyses for three settings

Boyd-Orr 2000

ELSA

BHPS Wave 11

combined MULTIGROUP analysis

Software……….

AMOS

James L. Arbuckle

http://www.smallwaters.com

AMOS Graphics

Control

Autonomy

Pleasure

Self-realization

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

0 .60

0 .540 .390 .58

0 .610 .460 .46

0 .47

0 .5 30 .43

0 .62

0 .70

0 .70

0 .58

0 .690 .470 .610 .69

0 .78

1 .02

0 .69

0 .68

0 .80

0 .95

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

0 .47

1st order model with standardised regression weights

Control

Autonomy

Pleasure

Self-realization

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

0 .64

0 .530 .4 30 .5 3

0 .630 .5 20 .5 6

0 .49

0 .520 .44

0 .60

0 .71

0 .7 0

0 .55

0 .670470 .580 .6 8

0 .7 9

0 .90

0 .67

0 .67

0 .77

0 .91

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

0 .46

0 .42

-0 .31

0 .21

0 .29

0 .22

-0 .29

0 .28

0 .33

1st order model (errors correlated) with standardised regression weights

Model fit indices for 1st order model

Data set CMIN/df GFI AGFI RMSEA

Boyd Orr 2.61 0.82 0.76 0.09

BHPS 46.98 0.88 0.85 0.08

ELSA 82.30 0.87 0.82 0.09

Multi-group

41.48 0.87 0.84 0.05

Model fit indices for 1st order model with errors correlated

Data set CMIN/df GFI AGFI RMSEA

Boyd Orr 1.67 0.89 0.85 0.06

BHPS 33.10 0.92 0.89 0.07

ELSA 57.22 0.91 0.88 0.08

Multi-group

28.95 0.92 0.89 0.04

QOL

Control

Autonomy

Pleasure

Self-realization

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

1

1

1

1

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

rC

rA

rP

rS

v ar_a

v ar_a

v ar_a

v ar_a

CASP19 second order model

QOL

Control

Autonomy

Pleasure

Self-realization

0 .92

0 .8 8

0 .71

0 .93

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

0 .67

0 .52

0 .47

0 .61

0 .660 .460 .4 9

0 .46

0 .550 .43

0 .67

0 .69

0 .69

0 .53

0 .690 .48

0 .60

0 .70

0 .79

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

rC

rA

rP

rS

2nd order model with standardised regression weights

QOL

Control

Autonomy

Pleasure

Self-realization

0 .92

0 .83

0 .71

0 .93

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

0 .68

0 .50

0 .48

0 .58

0 .670 .5 00 .57

0 .41

0 .550 .44

0 .58

0 .69

0 .70

0 .55

0 .680 .48

0 .57

0 .67

0 .79

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

rC

rA

rP

rS

0 .27

-0 .32

0 .47

0 .34

0 .32

2nd order model (errors correlated) with standardised regression weights

Model fit indices for 2nd order model

Data set CMIN/df GFI AGFI RMSEA

Boyd Orr 2.7 0.81 0.77 0.09

BHPS 49.48 0.87 0.84 0.09

ELSA 88.04 0.86 0.82 0.09

Multi-group

43.91 0.86 0.84 0.05

Model fit indices for 2nd order model with errors correlated

Data set CMIN/df GFI AGFI RMSEA

Boyd Orr 1.9 0.87 0.84 0.07

BHPS 34.02 0.92 0.89 0.07

ELSA 58.34 0.91 0.88 0.08

Multi-group

25.59 0.91 0.89 0.04

The search for empirical stability

Structures that don’t let you down…..

QOL

Control

Autonomy

Pleasure

Self-realization

CASP1

CASP4

CASP3

CASP2

CASP5

CASP6

CASP7

CASP8

CASP9

CASP10

CASP11

CASP12

CASP13

CASP14

CASP15

CASP16

CASP17

CASP18

CASP19

1

1

1

1

e1

e9

e8

e7

e6

e5

e4

e3

e2

e14

e13

e12

e11

e10

e19

e18

e17

e16

e15

rC

rA

rP

rS

v ar_a

v ar_a

v ar_a

v ar_a

CASP12 second order model

Rank Order Correlations for Boyd-Orr 2000

CASP -19 -12

- 19 1.0

-12 0.97 1.0

Dilemma

Compromise or

re-examine theory ??

CONTROL

My age prevents me from doing the things I would like to do I feel that what happens to me is out of my controlI feel free to plan for the futureI feel left out of things

Alpha = 0.6 , remains at 0.6

AUTONOMY

I can do the things I want to doFamily responsibilities prevent me from doing what I want to do I feel that I can please myself what I doMy health stops me from doing the things I want to doShortage of money stops me from doing the things I want to do

Alpha = 0.6, remains at 0.6

Self-realisation

I feel full of energy these daysI choose to do things that I have never done beforeI fell satisfied with the way my life has turned outI feel that life is full of opportunitiesI feel that the future looks good for me

Alpha = 0.8, remains at 0.8

Pleasure

I look forward to each dayI feel that my life has meaningI enjoy the things that I doI enjoy being in the company of othersOn balance, I look back on my life with a sense of happiness

Alpha = 0.8, now 0.7

Pairs of items with correlated error terms from CASP-19

Age inhibits activities (C) with My health stops me… (A)

Feel free to plan for the future… (C) with Life is full of… (SR)

I can do the things… (A) with I enjoy the things.. ((P)

Family responsibilities…(A) with I feel full of energy..(SR)

My health stops me .. (A) with I feel full of energy…(SR)

I enjoy being in the company ..(P) with I feel full of .. (SR)

On balance I look back… (P) with I feel satisfied about.. (SR)

I feel that I can please… (A) with My health stops me.. (A)

My health stops me… (A) with Life is full of opportunities (SR)

Now turning to Mplus to address

Measurement properties

Item non-response

Early results based on Version 3.01

Version 4.0 on order (www.statmodel.com)

Data set Percentage of complete cases

Degree of missingness

BHPS 86.5 7.9

ELSA 81.9 11.3

Full Information Maximum Likelihood (FIML) for missing data

• Imputation model is embedded in analytical model

• Schafer, J.L. and Graham, J. (2002). Missing data: our view of the state of the art. Psychological Methods, 7, 147-177

• Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modelling with data that are not completely missing at random. Psychometrika, 42, 431-462.

Goodness of fit indices :ELSA

Model CFI RMSEA TLI

Single 0.74 0.14 0.90

First Order 0.80 0.12 0.92

Second order

0.76 0.13 0.91

Goodness of fit indices :BHPS

Model CFI RMSEA TLI

Single 0.73 0.10 0.89

First Order 0.79 0.09 0.92

Second order

0.76 0.09 0.91

Correlations of ELSA and BHPS factor loadings:

Model Product moment correlation

Single factor 0.98

First order 0.98

Second order 0.98

Internal Consistency analysis: bottom-up

Cronbach’s Alpha

Domain/

DATA

Control Autonomy Self-realisation

Pleasure

ELSA 0.63 0.53 0.78 0.83

BHPS 0.64 0.53 0.76 0.80

Refinement

• Compromised with a 12-item version by a process of item elimination

• Combining domains for control and autonomy (alpha =0.67)

• Global index still attains an alpha of 0.87

The next steps and the need for more theory

• Further scale refinement – Use of modification indices as for AMOS analysis?

• Sample weights: in BHPS individual weights

compensate for differences in final stage of selection and a non response adjustment

• Multi-group analysis

-issue differential weights by group ?• Allow for clustering use multilevel analysis

Forthcoming publication

The evaluation of a self-enumerated scale of quality of life (CASP-19) in the context of researchon ageing: a combination of exploratory and confirmatoryapproaches.

Wiggins, Netuveli, Hyde, Higgs and Blane (2008).

Social Indicators Research