Research Article of International Journal of Addiction Research and Therapy
Modelling Predictors of Gambling Harms with Stochastic Search Variable Selection (SSVS)
Alysha Cooper, Harvey H. C. Marmurek
University of Guelph
The purpose of the present study was to introduce stochastic search variable selection (SSVS) as a procedure to identify a subset of important predictors of gambling harm. The target set of predictors were dimensions of trait impulsivity, gambling cognitions, and gambling motivations. Five types of gambling harm (feeling one has a personal problem; social criticism; feeling guilt; health; and, financial) were measured by the Problem Gambling Severity Index. Casino patrons completed the measures. As a first step, we identified the significant predictors that would be included in modelling an aggregate harm score. The most important predictors, the cognition that one is not able to stop gambling, and the motivation to escape or avoid life stressors, were positively associated with overall harm. Two weaker, but statistically significant, predictors were negatively associated with harm: sensation-seeking and illusion of control. Although a perceived inability to stop gambling was the most important predictor of each individual harm, the pattern of predictors varied across harms. For example, sensation-seeking was an important predictor only for the belief that one has a gambling problem, and escape/avoidance motivation was strongly predictive of financial harm. The results suggest that primary interventions designed to mitigate harm should address the belief that the gambler is unable to stop gambling, and motivations related to escape/avoid life stressors. Other interventions would be tailored to the specific harms experience by the gambler.
Keywords: Modelling Predictors, Gambling Harms, Stochastic Search Variable Selection (SSVS)
How to cite this article:
Alysha Cooper, Harvey H. C. Marmurek.Modelling Predictors of Gambling Harms with Stochastic Search Variable Selection (SSVS).International Journal of Addiction Research and Therapy, 2020, 3:20. DOI: 10.28933/ijart-2020-07-2005
1. Calado, F., & Griffiths, M. (2016). Problem gambling worldwide: An update and systematic review of empirical research (2000-2015). Journal of Behavioral Addictions, 5(4), 592-613. https://doi.org/ 10.1556/2006.5.2016.073
2. Delfabbro, P., King, D., Browne, M., & Dowling, N. (2020). Do EGMs have a stronger association with problem gambling than racing and casino table games? Evidence from a decade of Australian prevalence studies. Journal of Gambling Studies, 36(2), 499–511. https://doi.org/10.1007/s108 99 -020-09950-5
3. Canale, N., Vieno, A., & Griffiths, M. (2016). The extent and distribution of gambling-related harms and the prevention paradox in a British population survey. (FULL-LENGTH REPORT)(Report). 5(2), 204-212.http://doi.org/10.1556/2006.5.2016.023
4. Devos, M., Clark, L., Bowden-Jones, H., Grall-Bronnec, M., Challet-Bouju, G., Khazaal, Y., Maurage, P., & Billieux, J. (2020). The joint role of impulsivity and distorted cognitions in recreational and problem gambling: A cluster analytic approach. Journal of Affective Disorders, 260, 473–482. https://doi.org/10.1016/j.jad.2019.08.096
5. Hearn, N., Ireland, J., Eslea, M., & Fisk, J. (2020). Exploring pathways to gambling: Pro-posing the integrated risk and protective factors model of gambling types. Journal of Gambling Studies. https://doi.org/10.1007/s10899-020-09929-2
6. Cyders, M., Smith, G., Spillane, N., Fischer, S., Annus, A., & Peterson, C. (2007). Integration of impulsivity and positive mood to predict risky behavior: Development and validation of a measure of positive urgency. Psychological Assessment, 19(1), 107–118. https://doi.org/10.1037/ 10 40-3522.214.171.124
7. Raylu, N., & Oei, T. (2004). The Gambling Related Cognitions Scale (GRCS): development, confirmatory factor validation and psychometric properties. Addiction, 99(6), 757–769. https://doi.org/ 10 .1111/j.1360-0443.2004.00753.x
8. Lee, H., Chae, P., Lee, H., & Kim, Y. (2007). The five-factor gambling motivation model. Psychiatry Research, 150(1), 21–32. https://doi.org/10.1016/ j.psychres.2006.04.005
9. Smith, G. (2018). Step away from stepwise. J Big Data 5, 32. https://doi.org/10.1186/s40537-018-0143-6
10. Bainter, S., McCauley, T. G., Wager, T., & Losin, E. A. R. (2020). Improving practices for selecting a subset of important predictors in psychology: An application to predicting pain. Advances in Methods and Practices in Psychological Science,3, 66-80. https://doi.org/10.31234/osf.io/j8t7s.
11. Ferris, J. A., & Wynne, H. J. (2001). The Canadian problem gambling index. Canadian Centre
1. on Substance Abuse Ottawa, ON.
12. Caler, K., Garcia, J., & Nower, L. (2016). Assessing problem gambling: A review of classic and specialized measures. Current Addiction Reports, 3(4), 437–444.https://doi.org/10.1007/s40 429-016-0118-7
13. Wynne, H. (2003). Introducing the Canadian Problem Gambling Index. Edmonton, AB: Wynne Resources.
14. Dong, G., & Potenza, M. N. (2014). A cognitive-behavioral model of Internet gaming disorder: Theoretical underpinnings and clinical implications. Journal of Psychiatric Research, 58, 7-11. https://doi.org/10.1016/j.jpsychires.2014.07.005.
15. Tolchard, B. (2017). Cognitive-behavior therapy for problem gambling: a critique of current treatments and proposed new unified approach. Journal of Mental Health, 26(3), 283–290. https://doi.org/10.1080/09638237.2016.1207235
16. Binde, P., Romild, U., & Volberg, R. A. (2017) Forms of gambling, gambling involvement and problem gambling: evidence from a Swedish population survey, International Gambling Studies, 17:3,490-507,DOI:10.1080/14459795.2017.1360 928
17. Callan, M. J., Shead, N. W., & Olson, J. M. (2011). Personal relative deprivation, delay discounting, and gambling. Journal of Personality and Social Psychology,101(5)955–973. https://doi.org/10. 10 37/a0024778
18. McInnes, A., Hodgins, D. C., & Holub, A. (2014). The Gambling Cognitions Inventory: scale development and psychometric validation with problem and pathological gamblers. International Gambling Studies, 14(3), 410-431, https://doi.org/10. 1080/ 14459795.2014.923483
19. Shinaprayoon, T., Carter, N. T., and Goodie, A. S. (2017). The modified gambling motivation scale: Confirmatory factor analysis and links with problem gambling. Journal of Gambling Issues, 37, 108-135.
20. Shannon, K., Anjoul, F., & Blaszczynski, A. (2017). Mapping the proportional distribution of gambling-related harms in a clinical and community sample. International Gambling Studies, 17(3), 366–385. https://doi.org/10.1080/1445 9795.2017.1333131
21. Beynon, C., Pearce-Smith, N., & Clark, R. (2020). Harms associated with gambling: Abbreviated systematic review protocol. Systematic Reviews, 9:148.https://doi.org/10.1186/s13643-020-01397-4
CC BY 4.0
This work and its PDF file(s) are licensed under a Creative Commons Attribution 4.0 International License.