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

1. The development and assessment of a Quality of Life measure (CASP-19) in the context of research on ageing Dick Wiggins Department of Quantitative Social Science The Institute of Education The University of London Email: r.wiggins@ioe.ac.uk CCSR Seminar University of Manchester, 4th December 2007

3. 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 Blane Paul Higgs Martin Hyde Dick Wiggins

4. Followed by: Quality of Life and Resilience in Early Old Age 2003-06 David Blane, Dick Wiggins, Scott Montgomery, Gopal Netuveli and Zoë Hildon ESRC’s Priority Network on Human Capability and Resilience Network Coordinator: Mel Bartley , UCL

5. Research Settings for evaluation: • The Boyd-Orr sample • The English Longitudinal Study of Ageing (ELSA) • The British Household Panel Survey (BHPS, Wave 11)

6. 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 measures Berney and Blane, Social Science and Medicine, 45, 1997 2000 Postal Questionnaire Hyde et al., Aging and Mental Health, 2003 Boyd-Orr 2000

7. 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

8. Concepts and indicators…… Quality of life

9. Concepts and indicators…… Control Autonomy Quality of life Self- realisation Pleasure

10. Concepts and indicators…… Control Autonomy Quality of life Self- realisation Pleasure

11. Concepts and indicators…… Item 1 Control Item 2 Autonomy Item 3 Quality of life Item 4 Self- realisation Pleasure Item 19

12. Concepts and indicators…… Item 1 Control Item 2 Autonomy Item 3 Quality of life Item 4 Self- realisation Pleasure Item 19

13. 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 control • I feel free to plan for the future • I feel left out of things Alpha = 0.6

14. AUTONOMY • I can do the things I want to do • Family responsibilities prevent me from doing what I want to do • I feel that I can please myself what I do • My health stops me from doing the things I want to do • Shortage of money stops me from doing the things I want to do Alpha = 0.6

15. Self-realisation • I feel full of energy these days • I choose to do things that I have never done before • I fell satisfied with the way my life has turned out • I feel that life is full of opportunities • I feel that the future looks good for me Alpha = 0.8

16. Pleasure • I look forward to each day • I feel that my life has meaning • I enjoy the things that I do • I enjoy being in the company of others • On balance, I look back on my life with a sense of happiness Alpha = 0.8

17. 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 in Eastern Europe (HAPPIE) An evaluation of Camden’s Quality of Life Strategy for older citizens NCDS 2008 as they reach 50 years of age

18. 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

19. Some reflection and acknowledgement Ed Diener

20. Some reflection and acknowledgement Ed Diener Subjective Measures of Well-Being

21. 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

22. Measurement Models

23. 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.

24. CASP19 First order model

25. CASP19 second order model

26. Assessing goodness of fit Aim: to reproduce covariance/correlation matrix Criteria are typically functions of discrepancy

27. 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 more pragmatic 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

28. 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)

29. 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’

30. Model fit indices continued • Tucker Lewis Index (TLI) { (χ20 /df0 ) - (χ21 /df1 ) } / { (χ20 /df0 ) -1 } • Comparative Fit Index (CFI) { (χ20 /df0 ) - (χ21 /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’.

31. Moving on….. Multigroup analysis Testing the invariance of the factorial measurement and structure across sample settings Involves comparing an unconstrained model for the samples as a whole with a constrained model across the three groups.

32. Modelling Strategy…………. • separate analyses for three settings

33. Modelling Strategy…………. • separate analyses for three settings Complete data only BO-2000 : 198 ELSA : 9910 BHPS : 6471 All aged 50 +

34. Modelling Strategy…………. • separate analyses for three settings Boyd-Orr 2000 ELSA BHPS Wave 11 • combined MULTIGROUP analysis

35. Software………. AMOS James L. Arbuckle http://www.smallwaters.com AMOS Graphics

36. 1st order model with standardised regression weights

37. 1st order model (errors correlated) with standardised regression weights

38. Model fit indices for 1st order model

39. Model fit indices for 1st order model with errors correlated

40. CASP19 second order model

41. 2nd order model with standardised regression weights

42. 2nd order model (errors correlated) with standardised regression weights

43. Model fit indices for 2nd order model

44. Model fit indices for 2nd order model with errors correlated

45. The search for empirical stability Structures that don’t let you down…..

46. CASP12 second order model

47. Rank Order Correlations for Boyd-Orr 2000

48. Dilemma Compromise or re-examine theory ??

49. 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 control • I feel free to plan for the future • I feel left out of things Alpha = 0.6 , remains at 0.6

50. AUTONOMY • I can do the things I want to do • Family responsibilities prevent me from doing what I want to do • I feel that I can please myself what I do • My health stops me from doing the things I want to do • Shortage of money stops me from doing the things I want to do Alpha = 0.6, remains at 0.6