1. Discovering the Causes of Problem Gambling: Overcoming Methodological Challenges Donald Schopflocher, PhD Associate Professor, School of Public Health, University of Alberta

4. Preliminaries Datasets – LLLP 3 (of 4) waves, Alberta, initial N= 1808 – QERI 4 (of 5) waves, SE Ontario, initial N= 4121 – ‘07-’08 CCHS cross section, Ont., Quebec, Sask. N=81427 Analyses are exploratory – Sketch out some research questions – Examine some methods especially for Causal analysis Longitudinal (panel) data Analyses (so far) focus upon gambling as measured by the CPGI

5. Focus Questions Are there enough Problem Gamblers to study by survey methods? Is Problem Gambling a category, or part of a continuum? What causes changes in gambling?

6. Focus Methods Latent Variable and Structural Equation Models Fixed Effects Regression Methods Multivariate Visualization Techniques

7. Question 1: Are there enough Problem Gamblers to study?

9. LLLP QERI Wave 1 Recruitment: CPGI categories

12. Question 2: Is Problem Gambling a category, or part of a continuum? QERI Waves 1-4

14. Further indications that gambling and problem gambling may be stable characteristics on a continuum: QERI Intraclass Correlations (proportion of o/a variance between individuals) – Gambling Activities 0.78 – Gambling Frequency 0.76 – Ln (Gambling Expenditures) 0.71 – CPGI Problem Score 0.77 QERI Autocorrelations in change scores – Lag 1 Change in Gambling Activities -0.176 – Lag 1 Change in Gambling Frequency -0.254 – Lag 1 Change in Ln (Gambling Expenditures) -0.279 – Lag 1 Change in CPGI Problem Score -0.212

15. Question 3a: What causes gambling ? Traits

17. Question 3a: What causes gambling ? Mental disorders implicated?

19. LLLP Wave 1

20. Aside: Maybe Gambling behaviour and Gambling Problems are dissociated. QERI Waves 1-4

21. Note that if we accept the dissociation of gambling behaviour from gambling problems there can now be path models of this type:

23. Question 3b: What causes gambling ? What causes CHANGES to gambling behaviour &/or gambling problems ? OR What’s time got to do with it?

24. Total model Y it = B 1T X it + B 2T Z i + e it where i indexes persons t indexes occasions Problem: – Persons will generally be more similar to themselves than to random others Review Regression Analysis of Longitudinal Panel Data

25. Between Model Problem -Ignores change Y i = B 0B + B 1B X i + B 2B Z i + e i

26. QERI Waves 1-4

27. Within Model (Fixed Effects) y it = B 0W + B 1W x it + A i D i + e it uses A i as a single person specific coefficient to stand in for the effects of all variables constant over time, measured or unmeasured.

28. QERI Waves 1-4

31. Some tentative conclusions ‘Gambling behaviour’ is largely a stable characteristic ‘Having gambling problems’ is largely a stable characteristic The two are dissociated, but related (here as elsewhere behaviour ‘causes’ problems). Personality traits are differentially related to gambling behaviour and having gambling problems. Changes in mental health and in stressful life events are related to changes in gambling behaviour and gambling problems Relationships in general are quite small

32. Random Effects Model (Multilevel Models) Y it = B 0R + B 1R X it + B 2R Z i + V i + e it Assumes a specific distribution for V. A critical assumption is that V and E are independent of X and Z Appendix: An Alternative to Fixed Effect Regression