Detection of Problem Gambler Subgroups Using Recursive Partitioning

  • Francis Markham
  • Martin Young
  • Bruce Doran


The multivariate socio-demographic risk factors for problem gambling have been well documented. While this body of research is valuable in determining risk factors aggregated across various populations, the majority of studies tend not to specifically identify particular subgroups of problem gamblers based on the interaction between variables. The identification of problem gambling subgroups offers the potential for improved harm-reduction initiatives in particular geographic contexts. We introduce an analytical approach termed recursive partitioning, commonly used in the health sciences but infrequently employed in gambling research, to identify specific gambler subgroups based on the interaction of a range of predictor variables. Recursive partitioning creates groups of cases (e.g. gamblers) with similar outcomes by repeatedly splitting each group into smaller and more homogenous subgroups. We employ it to define problem gambler subgroups within a diverse population context (i.e. northern Australia) and compare the results with a multivariate analysis of the same dataset using a generalized linear regression model. We assess the advantages and disadvantages of each approach, and argue that recursive partitioning is an easily-interpretable approach that may be useful both in identifying problem gambling subgroups and in developing targeted harm-minimisation strategies.


Recursive partitioning Problem gambling Subgroups Decision trees Gambling behavior 



This research was supported in part by grants from the Community Benefit Fund of the Northern Territory Government, the Northern Territory Research and Innovation Fund and Australian Research Council Project LP0990584.


  1. Australian Bureau of Statistics. (2007). Census of Population and Housing: Basic Community Profile, 2006 (No. 2001.0). Canberra: Australian Bureau of Statistics. Retrieved from archived at
  2. Australian Bureau of Statistics. (2008). An Introduction to Socio-Economic Indexes for Areas (SEIFA), 2006 (No. 2039.0). Canberra: Australian Bureau of Statistics. Retrieved from archived at
  3. Australian Bureau of Statistics. (2009). Australian and New Zealand Standard Classification of Occupations, First Edition, Revision 1 (No. 1220.0). Canberra: Australian Bureau of Statistics. Retrieved from archived at
  4. Australian Bureau of Statistics. (2011). Australian Social Trends, June 2011 (No. 4102.0). Canberra: Australian Bureau of Statistics. Retrieved from$File/41020_ASTJun2011.pdf archived at
  5. Berk, R. A. (2008). Statistical learning from a regression perspective. New York: Springer Verlag.Google Scholar
  6. Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97(5), 487–499. doi: 10.1046/j.1360-0443.2002.00015.x.PubMedCrossRefGoogle Scholar
  7. Bondolfi, G., Osiek, C., & Ferrero, F. (2000). Prevalence estimates of pathological gambling in Switzerland. Acta Psychiatrica Scandinavica, 101(6), 473–475. doi: 10.1034/j.1600-0447.2000.101006473.x.PubMedCrossRefGoogle Scholar
  8. Breiman, L. (2001a). Random forests. Machine Learning, 45(1), 5–32. doi: 10.1023/A:1010933404324.CrossRefGoogle Scholar
  9. Breiman, L. (2001b). Statistical modeling: the two cultures. Statistical Science, 16(3), 199–231. doi: 10.1214/ss/1009213726.CrossRefGoogle Scholar
  10. Breiman, L., Friedman, J., Stone, C., & Olshen, R. A. (1984). Classification and regression trees. Belmont: Wadsworth International Group.Google Scholar
  11. Delen, D., & Sirakaya, E. (2006). Determining the efficacy of data-mining methods in predicting gaming ballot outcomes. Journal of Hospitality & Tourism Research, 30(3), 313–332. doi: 10.1177/1096348006286795.CrossRefGoogle Scholar
  12. Doran, B., & Young, M. (2010). ‘Mobile mindsets’: EGM venue usage, gambling participation, and problem gambling among three itinerant groups on the Sunshine Coast of Australia. International Gambling Studies, 10(3), 269–288. doi: 10.1080/14459795.2010.531040.CrossRefGoogle Scholar
  13. Feigelman, W., Kleinman, P. H., Lesieur, H. R., Millman, R. B., & Lesser, M. L. (1995). Pathological gambling among methadone patients. Drug and Alcohol Dependence, 39(2), 75–81. doi: 10.1016/0376-8716(95)01141-K.PubMedCrossRefGoogle Scholar
  14. Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index: User Manual. Ottawa: Canadian Centre on Substance Abuse. Retrieved from archived at
  15. Goldman, L., Cook, E. F., Brand, D. A., Lee, T. H., Rouan, G. W., Weisberg, M. C., Acampora, D., et al. (1988). A computer protocol to predict myocardial infarction in emergency department patients with chest pain. The New England Journal of Medicine, 318(13), 797–803. doi: 10.1056/NEJM198803313181301.PubMedCrossRefGoogle Scholar
  16. Goldman, L., Cook, E. F., Johnson, P. A., Brand, D. A., Rouan, G. W., & Lee, T. H. (1996). Prediction of the need for intensive care in patients who come to the emergency departments with acute chest pain. The New England Journal of Medicine, 334(23), 1498–1504. doi: 10.1056/NEJM199606063342303.PubMedCrossRefGoogle Scholar
  17. Gruenewald, T. L., Mroczek, D. K., Ryff, C. D., & Singer, B. H. (2008). Diverse pathways to positive and negative affect in adulthood and later life: an integrative approach using recursive partitioning. Developmental Psychology, 44(2), 330–343. doi: 10.1037/0012-1649.44.2.330.PubMedCrossRefGoogle Scholar
  18. Hall, G. W., Carriero, N. J., Takushi, R. Y., Montoya, I. D., Preston, K. L., & Gorelick, D. A. (2000). Pathological gambling among cocaine-dependent outpatients. The American Journal of Psychiatry, 157(7), 1127–1133. doi: 10.1176/appi.ajp.157.7.1127.PubMedCrossRefGoogle Scholar
  19. Hawley, C. E., Glenn, M. K., & Diaz, S. (2007). Problem gambling in the workplace, characteristics of employees seeking help. Work: A Journal of Prevention, Assessment and Rehabilitation, 29(4), 331–340.Google Scholar
  20. Hing, N., & Nuske, E. (2011). Assisting problem gamblers in the gaming venue: An assessment of practices and procedures followed by frontline hospitality staff. International Journal of Hospitality Management, 30(2), 459–467. doi: 10.1016/j.ijhm.2010.09.013.CrossRefGoogle Scholar
  21. Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased recursive partitioning: a conditional inference framework. Journal of Computational and Graphical Statistics, 15, 651–674. doi: 10.1198/106186006X133933.CrossRefGoogle Scholar
  22. Johansson, A., Grant, J. E., Kim, S. W., Odlaug, B. L., & Götestam, K. G. (2008). Risk factors for problematic gambling: a critical literature review. Journal of Gambling Studies, 25(1), 67–92. doi: 10.1007/s10899-008-9088-6.CrossRefGoogle Scholar
  23. Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society. Series C (Applied Statistics), 29(2), 119–127. doi: 10.2307/2986296.Google Scholar
  24. Ladouceur, R., Boudreault, N., Jacques, C., & Vitaro, F. (1999). Pathological gambling and related problems among adolescents. Journal of Child & Adolescent Substance Abuse, 8(4), 55–68. doi: 10.1300/J029v08n04_04.CrossRefGoogle Scholar
  25. Loh, W. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14–23. doi: 10.1002/widm.8.CrossRefGoogle Scholar
  26. Markham, F., Young, M., & Doran, B. (2012). The relationship between alcohol consumption, gambling behaviour and problem gambling during a single visit to a gambling venue. Drug and Alcohol Review, Advance online publication.. doi: 10.1111/j.1465-3362.2012.00430.x.
  27. McKenzie, D. P., McFarlane, A. C., Creamer, M., Ikin, J. F., Forbes, A. B., Kelsall, H. L., Clarke, D. M., et al. (2006). Hazardous or harmful alcohol use in Royal Australian Navy veterans of the 1991 Gulf War: identification of high risk subgroups. Addictive Behaviors, 31(9), 1683–1694. doi: 10.1016/j.addbeh.2005.12.027.PubMedCrossRefGoogle Scholar
  28. Merkle, E. C., & Shaffer, V. A. (2011). Binary recursive partitioning: background, methods, and application to psychology. British Journal of Mathematical and Statistical Psychology, 64(1), 161–181. doi: 10.1348/000711010X503129.PubMedCrossRefGoogle Scholar
  29. Morgan, R. D., Olson, K. R., Krueger, R. M., Schellenberg, R. P., & Jackson, T. T. (2000). Do the DSM decision trees improve diagnostic ability? Journal of Clinical Psychology, 56(1), 73–88. doi:10.1002/(SICI)1097-4679(200001)56:1<73::AID-JCLP7>3.0.CO;2-I.PubMedCrossRefGoogle Scholar
  30. Potenza, M. N., Steinberg, M. A., McLaughlin, S. D., Wu, R., Rounsaville, B. J., & O’Malley, S. S. (2001). Gender-related differences in the characteristics of problem gamblers using a gambling helpline. The American Journal of Psychiatry, 158(9), 1500–1505. doi: 10.1176/appi.ajp.158.9.1500.PubMedCrossRefGoogle Scholar
  31. Potenza, M. N., Steinberg, M. A., & Wu, R. (2005). Characteristics of gambling helpline callers with self-reported gambling and alcohol use problems. Journal of Gambling Studies, 21(3), 233–254. doi: 10.1007/s10899-005-3098-4.PubMedCrossRefGoogle Scholar
  32. PSMA Australia. (2010). Product description: G-NAF version 1.11. Canberra: PSMA Australia. Retrieved from
  33. Quinlan, J. R. (1993). C4.5: programs for machine learning. San Mateo: Morgan Kaufmann Publishers.Google Scholar
  34. R Development Core Team. (2011). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  35. Raylu, N., & Oei, T. P. (2004). Role of culture in gambling and problem gambling. Clinical Psychology Review, 23(8), 1087–1114. doi: 10.1016/j.cpr.2003.09.005.PubMedCrossRefGoogle Scholar
  36. Schmitz, N., Kugler, J., & Rollnik, J. (2003). On the relation between neuroticism, self-esteem, and depression: results from the National Comorbidity Survey. Comprehensive Psychiatry, 44(3), 169–176. doi: 10.1016/S0010-440X(03)00008-7.PubMedCrossRefGoogle Scholar
  37. Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychological Methods, 14(4), 323–348. doi: 10.1037/a0016973.PubMedCrossRefGoogle Scholar
  38. Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S. Statistics and computing (4th ed.). New York: Springer.CrossRefGoogle Scholar
  39. Volberg, R. A., Abbott, M. W., Rönnberg, S., & Munck, I. M. E. (2001). Prevalence and risks of pathological gambling in Sweden. Acta Psychiatrica Scandinavica, 104(4), 250–256. doi: 10.1111/j.1600-0447.2001.00336.x.PubMedCrossRefGoogle Scholar
  40. Welte, J. W., Barnes, G. M., Wieczorek, W. F., & Tidwell, M.-C. (2004a). Gambling participation and pathology in the United States - A sociodemographic analysis using classification trees. Addictive Behaviors, 29(5), 983–989. doi: 10.1016/j.addbeh.2004.02.047.PubMedCrossRefGoogle Scholar
  41. Welte, J. W., Barnes, G. M., Wieczorek, W. F., Tidwell, M.-C. O., & Parker, J. C. (2004b). Risk factors for pathological gambling. Addictive Behaviors, 29(2), 323–335. doi: 10.1016/j.addbeh.2003.08.007.PubMedCrossRefGoogle Scholar
  42. Winters, K. C., Stinchfield, R., & Fulkerson, J. (1993). Patterns and characteristics of adolescent gambling. Journal of Gambling Studies, 9(4), 371–386. doi: 10.1007/BF01014628.CrossRefGoogle Scholar
  43. Young, M., & Stevens, M. (2008). SOGS and CPGI: parallel comparison on a diverse population. Journal of Gambling Studies, 24(3), 337–356. doi: 10.1007/s10899-007-9087-z.PubMedCrossRefGoogle Scholar
  44. Young, M., Stevens, M., & Morris, M. (2008). Problem gambling within the non-Indigenous population of the Northern Territory of Australia: a multivariate analysis of risk factors. International Gambling Studies, 8(1), 77–93. doi: 10.1080/14459790701870571.CrossRefGoogle Scholar
  45. Zeileis, A., Hothorn, T., & Hornik, K. (2008). Model-based recursive partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514. doi: 10.1198/106186008X319331.CrossRefGoogle Scholar
  46. Zhang, H., & Bracken, M. B. (1995). Tree-based risk factor analysis of preterm delivery and small-for-gestational-age birth. American Journal of Epidemiology, 141(1), 70–78.PubMedGoogle Scholar
  47. Zhang, H., & Singer, B. (2010). Recursive partitioning and applications (2nd ed.). New York: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Menzies School of Health ResearchDarwinAustralia
  2. 2.Southern Cross UniversityCoffs HarbourAustralia
  3. 3.Fenner School of Environment and SocietyAustralian National UniversityCanberraAustralia

Personalised recommendations