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Innovative methods for gambling data

Innovative methods for gambling data. Trends in research methodology: A workshop for early stage investigators. Bethany C. Bray, Ph.D. bcbray@psu.edu Research Associate, The Methodology Center, Penn State http://methodology.psu.edu. Contact info. Brief overview of three innovative methods

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Innovative methods for gambling data

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  1. Innovative methods for gambling data Trends in research methodology: A workshop for early stage investigators

  2. Bethany C. Bray, Ph.D.bcbray@psu.edu • Research Associate, The Methodology Center, Penn Statehttp://methodology.psu.edu Contact info

  3. Brief overview of three innovative methods • Research questions • Modeling approach • Tools needed (i.e., software) • Gambling applications • Resources for more information Goals

  4. What is my research question? • What are the data I have to address my research question? Questions to ask …

  5. Question: What are the risk factors for developing a gambling disorder? • Data: total number of DSM-5 diagnostic criteria endorsed • Question: Are there types of gamblers at higher risk for developing gambling disorder compared to others? • Data: multiple indicators of gambling activity engagement For example …

  6. Question: Does the relation between gender and gambling vary across time? • Data: amount wagered online every day for two years For example …

  7. I want to model a count outcome? • e.g., total number of DSM-5 diagnostic criteria endorsed • I want to identify types of individuals? • e.g., using multiple indicators of gambling behavior • I want to model intensively-collected data? • e.g., daily online wagering over time What do I do when ……

  8. Count outcomes

  9. What are the predictors of number of daysgambled? • e.g., measured by total number of days in a month during which an individual gambled • What are the predictors of severity of gambling behavior? • e.g., measured by total number of endorsed DSM-5 diagnostic criteria Research questions

  10. What are the risk factors (i.e., gambling behaviors, substance abuse, other problem behaviors, sociodemographic characteristics) for disordered gambling? (Welte et al., 2004) • e.g., frequency of 15 types of gambling, count of diagnostic criteria • Does the “gambler's fallacy” predict fluctuations in lottery play? • e.g., number of winning bets conditional on the history of draws (Papachristou, 2004) Research questions

  11. Do college student athletes have higher levels of disordered gambling than non-athletes? What are the risk factors for athletes and non-athletes? (Weinstock et al., 2007) • e.g., gambling frequency Research questions

  12. Do gender, age, race/ethnicity, family socioeconomic status, sensation seeking, and participation in risky behaviors predict problematic financial behavior among college students? (Worthy et al., 2010) • e.g., number of problematic financial behaviors Research questions

  13. At-risk sample

  14. What would this distribution look like for a general population sample?

  15. Poisson regression • Negative binomial regression • Zero-inflated Poisson regression • Zero-inflated negative binomial regression Modeling approach

  16. Behaviors like gambling can generate data that are characterized by excess zeros • e.g., much of the population may not engage in the behavior or not have problems • These behaviors can also generate data with over-dispersion • Variance substantially exceeds the mean • e.g., because a small number of individuals engage in extreme levels of the behavior Modeling approach

  17. Several closely related models are available for predicting a count variable… • Poisson regression (mean and variance assumed to be equal) • Negative binomial regression (adds over-dispersion) Modeling approach

  18. Models can be extended to accommodate excess zeros • Zero-inflated Poisson (ZIP) regression • Zero-inflated NB (ZINB) regression • These models posit two types of individuals who report zero gambling… • Those who are non-gamblers • And those who have the potential to engage in gambling but report zero acts during that time Modeling approach

  19. Models estimate population-average associations between predictors and gambling • Can do model comparisons between options to determine which is optimal for examining the association between predictors and outcome • e.g., fit indices like AIC and BIC Modeling approach

  20. Total number of gambling acts expressed as a function of an intercept, a dispersion parameter, and eight risk indices • The intercept, 0.309, corresponds to the log of the mean gambling count among all adolescents when all predictors in the model are set to zero • e.g., for adolescents with average levels on all risk indices, the expected number of gambling acts in the past year was e0.309=1.3 • A positive dispersion parameter (4.418) indicates that the outcome was over-dispersed Modeling approach

  21. All risk indices were significantly and positively associated with the number of gambling acts in the overall population • Coefficients reflect association between each risk index and gambling count, after adjusting for other predictors in the model • For example, a one-standard-deviation increase in family risk corresponds to e0.305=1.36 times more gambling acts, holding all other predictors constant • For example, associating with antisocial peers had the largest coefficient, corresponding to e0.823=2.28 times more delinquent acts for every one-unit increase Modeling approach

  22. SPSS • http://www.ats.ucla.edu/stat/spss/dae/poissonreg.htm • http://www.ats.ucla.edu/stat/spss/dae/neg_binom.htm • SAS • http://www.ats.ucla.edu/stat/sas/dae/poissonreg.htm • http://www.ats.ucla.edu/stat/sas/dae/negbinreg.htm Software

  23. Papachristou, G. (2004). The British gambler's fallacy. Applied Economics, 36, 2073-2077. • Weinstock, J., Whelan, J. P., Meyers, A. W., & Watson, J. M. (2007). Gambling behavior of student-athletes and a student cohort: What are the odds?Journal of Gambling Studies, 23, 13-24. Gambling applications

  24. Welte, J. W., Barnes, G. M., Wieczorek, W. F., Tidwell, M. C. O., & Parker, J. C. (2004). Risk factors for pathological gambling. Addictive behaviors, 29, 323-335. • Worthy, S. L., Jonkman, J., & Blinn-Pike, L. (2010). Sensation-seeking, risk-taking, and problematic financial behaviors of college students. Journal of Family and Economic Issues, 31, 161-170. Gambling applications

  25. Comprehensive texts… • Agresti, A. (2012).Categorical data analysis. Hoboken, NJ: Wiley. • Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data. New York, NY: Cambridge University Press. Resources

  26. Recommended journal articles… • Atkins, D. C., & Gallop, R. J. (2007). Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models. Journal of Family Psychology, 21, 726-735. • Coxe, S., West, S. G., & Aiken, L. S. (2009). The analysis of count data: A gentle introduction to Poisson regression and its alternatives. Journal of personality assessment, 91, 121-136. Resources

  27. Recommended journal articles continued… • Lanza, S. T., Cooper, B. R., & Bray, B. C. (in press). Population heterogeneity in the salience of multiple risk factors for adolescent delinquency. Journal of Adolescent Health. Resources

  28. Identifying subgroups

  29. Are there identifiable patterns of gambling behaviors? If so, what are the related individual characteristics and health consequences? (Boldero et al., 2010; Cunningham-Williams & Hong, 2007; Lloyd et al., 2010) • Are there identifiable types of gamblers based on the DSM diagnostic criteria? (Carragher & McWilliams, 2011; McBride et al., 2010; Xian et al., 2008) Research questions

  30. Latent class analysis (LCA) • Individuals can be divided into subgroups, or latent classes, based on unobservable construct • True class membership is unknown • Classes are mutually exclusive and exhaustive Modeling approach

  31. Measurement of construct typically based on several categorical indicators • There is error associated with the measurement of the latent classes • Like factor analysis in that you have to identify the number and structure of the classes, but the latent variable is categorical Modeling approach

  32. Interested in two sets of parameters… • Latent class prevalences • e.g., probability of membership in the ‘table and sports gambling’ latent class • Item-response probabilities • e.g., probability of responding ‘yes’ to a question about betting on sports in the past month given membership in the ‘table and sports gambling’ latent class Modeling approach

  33. Gambling Classes lotto poker sports …

  34. Conduct model selection procedure to determine optimal number of latent classes • Use model fit criteria like the AIC and BIC • Somewhat similar to process for exploratory factor analysis • After model selection, use item-response probabilities to interpret the latent classes Modeling approach

  35. Modeling approach

  36. Might also want to examine group differences in… • Latent class structure • e.g., test measurement invariance • Latent class prevalences • e.g., test distribution across groups • e.g., gender differences in gambling behavior patterns… Modeling approach

  37. Modeling approach

  38. Lots of extensions to these models… • e.g., add covariates to predict subgroup membership • e.g., use subgroup membership to predict a distal outcome • e.g., examine changes in subgroup membership over time Modeling approach

  39. Gambling Classes Gender Drug Use casino games sports lotto …

  40. Gambling Classes Mental Heath Disorders casino games sports lotto …

  41. Time 2 Gambling Classes Time 1 Gambling Classes

  42. SAS (PROC LCA)http://methodology.psu.edu/downloads • Mplushttp://www.statmodel.com/ • Latent Goldhttp://statisticalinnovations.com/products/latentgold.html Software options

  43. Boldero, J. M., Bell, R. C., & Moore, S. M. (2010). Do gambling activity patterns predict gambling problems? A latent class analysis of gambling forms among Australian youth. International Gambling Studies, 10, 151-163. • Carragher, N., & McWilliams, L. A. (2011). A latent class analysis of DSM-IV criteria for pathological gambling: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychiatry Research, 187, 185-192. Gambling applications

  44. Cunningham-Williams, R. M., & Hong, S. I. (2007). A latent class analysis (LCA) of problem gambling among a sample of community-recruited gamblers. The Journal of Nervous and Mental Disease, 195, 939-947. • Lloyd, J., Doll, H., Hawton, K., Dutton, W. H., Geddes, J. R., Goodwin, G. M., & Rogers, R. D. (2010). Internet gamblers: A latent class analysis of their behaviours and health experiences. Journal of Gambling Studies, 26, 387-399. Gambling applications

  45. McBride, O., Adamson, G., & Shevlin, M. (2010). A latent class analysis of DSM-IV pathological gambling criteria in a nationally representative British sample. Psychiatry Research, 178, 401-407. • Xian, H., Shah, K. R., Potenza, M. N., Volberg, R., Chantarujikapong, S., True, W. R., ... & Eisen, S. A. (2008). A latent class analysis of DSM-III-R pathological gambling criteria in middle-aged men: Association with psychiatric disorders. Journal of addiction medicine, 2, 85-95. Gambling applications

  46. Comprehensive text… • Collins, L. M., & Lanza, L. M. (2010).Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New York, NY: Wiley. Resources

  47. Recommended journal articles… • Lanza, S. T., Bray, B. C., & Collins, L. M. (2013). An introduction to latent class and latent transition analysis. In J. A. Schinka, W. F. Velicer, & I. B. Weiner (Eds.), Handbook of psychology (2nd ed., Vol. 2, pp. 691-716). Hoboken, NJ: Wiley. • Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14, 671-694. Resources

  48. Recommended journal articles continued… • Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671-694. • Lanza, S. T. & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14, 157-168. Resources

  49. Recommended journal articles continued… • Lanza, S. T., Rhoades, B. L., Nix, R. L., Greenberg, M. T., & the Conduct Problems Prevention Research Group (2010). Modeling the Interplay of Multilevel Risk Factors for Future Academic and Behavior Problems: A Person-Centered Approach. Development and Psychopathology, 22, 313-335. Resources

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