Advertisement

Journal of Gambling Studies

, Volume 31, Issue 3, pp 807–823 | Cite as

Gambling Motives: Application of the Reasons for Gambling Questionnaire in an Australian Population Survey

  • K. L. Francis
  • N. A. Dowling
  • A. C. Jackson
  • D. R. Christensen
  • H. Wardle
Original Paper

Abstract

The Reasons for Gambling Questionnaire (RGQ) consist of 15 items forming five factors: enhancement, social, money, recreation and coping. The RGQ was developed for use in the 2010 British Gambling Prevalence Survey (BGPS) and has now been employed in the second Social and Economic Impact Study (SEIS) of Gambling in Tasmania study conducted in 2011 in Australia. Given differences between Britain and Australia in terms of socio-demographic profiles, gambling cultures and attitudes, gambling access and availability, gambling regulation, and rates and patterns of gambling participation, the aims of this study were to analyse the RGQ data from the SEIS to: (1) determine the most commonly endorsed gambling motives in an Australian jurisdiction, (2) explore the factor structure of the RGQ in an Australian sample, and (3) explore how motives for gambling vary among different Australian population sub-groups. A representative sample of the Tasmanian population who had gambled in the previous 12 months (n = 2,796) were administered the RGQ via computer-assisted telephone interviewing. The five most commonly endorsed reasons for gambling were for fun (62 %), followed by the chance of winning big money (52 %), it being something to do with friends and family (48 %), to be sociable (40 %), and excitement (38 %). A principal component analysis revealed a five-factor structure that is slightly different from that derived in the BGPS: money, regulate internal state, positive feelings, social, and challenge reasons. Finally, gambling motives varied according to socio-demographic factors, number of gambling activities, problem gambling severity, and participation on different gambling activities. Although some of these findings are consistent with those from the BGPS, there are also some slight differences, suggesting that there may be regional-specific variations in gambling motives.

Keywords

Problem gambling Pathological gambling Gambling Motives Measurement Reasons for Gambling Questionnaire 

Notes

Acknowledgments

The second Social and Economic Impact Study of Gambling in Tasmania was funded by the Tasmanian Department of Treasury and Finance (Project IGFP 2010 01).

References

  1. Abdi, H. (2003). Factor rotations in factor analyses: Encyclopedia for research methods for the social sciences (pp. 792–795). Thousand Oaks, CA: Sage.Google Scholar
  2. Allen Consulting Group, Problem Gambling Research and Treatment Centre, and the Social Research Centre. (2011a). Social and economic impact study of gambling in Tasmania, Volume 1: Gambling industry trends and economic impacts. Prepared for the Tasmanian Government Department of Treasury and Finance http://www.treasury.tas.gov.au/domino/dtf/dtf.nsf/LookupFiles/Volume1secondgamblingSEIS.PDF/$file/Volume1secondgamblingSEIS.PDF.
  3. Allen Consulting Group, Problem Gambling Research and Treatment Centre, and the Social Research Centre. (2011b). Social and economic impact study of gambling in Tasmania, Volume 2: Gambling survey. Prepared for the Tasmanian Government Department of Treasury and Finance: http://www.tenders.tas.gov.au/domino/dtf/dtf.nsf/LookupFiles/Volume2secondgamblingSEIS.PDF/$file/Volume2secondgamblingSEIS.PDF.
  4. Allen Consulting Group, Problem Gambling Research and Treatment Centre, and the Social Research Centre. (2011c). Social and economic impact study of gambling in Tasmania, Volume 3: Assessment of harm minimisation measures. Prepared for the Tasmanian Government Department of Treasury and Finance: http://www.treasury.tas.gov.au/domino/dtf/dtf.nsf/LookupFiles/Volume3secondgamblingSEIS.PDF/$file/Volume3secondgamblingSEIS.PDF.
  5. Casey, D. M., Williams, R. J., Mossière, A. M., Schopflocher, D. P., el-Guebaly, N., Hodgins, D. C., et al. (2011). The role of family, religiosity, and behaviour in adolescent gambling. Journal of adolescence, 34(5), 841–851.CrossRefPubMedGoogle Scholar
  6. Central Intelligence Agency (2013) .Guide to Country Comparison. In: The world factbook 2013–14. https://www.cia.gov/library/publications/the-world-factbook/rankorder/rankorderguide.html.
  7. Chantal, Y., Vallerand, R. J., & Vallieres, E. F. (1994). Construction et validation l’echelle de motivation relative aux jeux de hasard et d’argent. On the development and validation of the Gambling Motivation Scale (GMS). Society and Leisure, 17, 189–212.Google Scholar
  8. Christensen, D. R., Dowling, N. A., Jackson, A. C., Brown, M., Russo, J., Francis, K. L., et al. (2013). A pilot of an abridged dialectical behaviour therapy program as a treatment for problem gamblers. Behaviour Change, 30(2), 117–137.CrossRefGoogle Scholar
  9. Clarke, D. (2004). Impulsiveness, locus of control, motivation and problem gambling. Journal of Gambling Studies, 20(4), 319–345. doi: 10.1007/s10899-004-4578-7.CrossRefPubMedGoogle Scholar
  10. Clarke, D. (2005). Motivational differences between slot machine and lottery players. Psychological Reports, 96(3), 843–848. doi: 10.2466/pr0.96.3.843-848.CrossRefPubMedGoogle Scholar
  11. de Lisle, S. M., Dowling, N. A., & Allen, J. S. (2011). Mindfulness-based cognitive therapy for problem gambling. Clinical Case Studies, 10(3), 210–228.CrossRefGoogle Scholar
  12. Dechant, K., & Ellery, M. (2011). The effect of including a monetary motive item on the gambling motives questionnaire in a sample of moderate gamblers. Journal of Gambling Studies, 27(2), 331–344.PubMedCentralCrossRefPubMedGoogle Scholar
  13. Dickson, L. M., Derevensky, J. L., & Gupta, R. (2002). The prevention of gambling problems in youth: A conceptual framework. Journal of Gambling Studies, 18(2), 97–159.CrossRefPubMedGoogle Scholar
  14. Dowling, N. A. (2013). The cognitive-behavioural treatment of female problem gambling. In D. Richard, A. Blaszczynski, & L. Nower (Eds.), The Wiley-Blackwell handbook of disordered gambling (pp. 225–250). West Sussex: John Wiley and Sons Ltd.CrossRefGoogle Scholar
  15. Dowling, N., Smith, D., & Thomas, T. (2005). Electronic gaming machines: Are they the “crack-cocaine” of gambling? Addiction, 100(1), 33–45.CrossRefPubMedGoogle Scholar
  16. Dowling, N., Smith, D., & Thomas, T. (2006). Treatment of female pathological gambling: The efficacy of a cognitive-behavioural approach. Journal of Gambling Studies, 22(4), 355–372.CrossRefPubMedGoogle Scholar
  17. Dowling, N., Smith, D., & Thomas, T. (2007). A comparison of individual and group cognitive-behavioural treatment for female pathological gambling. Behaviour Research and Therapy, 45(9), 2192–2202.CrossRefPubMedGoogle Scholar
  18. Dowling, N., Smith, D., & Thomas, T. (2009). A preliminary investigation of abstinence and controlled gambling as self-selected goals of treatment for female pathological gambling. Journal of Gambling Studies, 25(2), 201–214.CrossRefPubMedGoogle Scholar
  19. el-Guebaly, N., Casey, D. M., Hodgins, D. C., Smith, G. J., Williams, R. J., Schopflocher, D. P., et al. (2008). Designing a longitudinal cohort study of gambling in Alberta: Rationale, methods, and challenges. Journal of Gambling Studies, 24(4), 479–504.CrossRefPubMedGoogle Scholar
  20. Ferris, J., & Wynne, H. J. (2001). The Canadian Problem Gambling Index. Ottawa: Canadian Centre on Substance Abuse.Google Scholar
  21. Holtgraves, T. (2009). Evaluating the Problem Gambling Severity Index. Journal of Gambling Studies, 25, 105–120.CrossRefPubMedGoogle Scholar
  22. Hurley, A. E., Scandura, T. A., Schriesheim, C. A., Brannick, M. T., Seers, A., Vandenberg, R. J., et al. (1997). Exploratory and confirmatory factor analysis: Guidelines, issues, and alternatives. Journal of Organizational Behaviour, 18(6), 667–683.CrossRefGoogle Scholar
  23. Jackson, A. C., Francis, K. L., Byrne, G., & Christensen, D. R. (2013). Leisure substitution and problem gambling: Report of a proof of concept group intervention. International Journal of Mental Health and Addiction, 11(1), 64–74. doi: 10.1007/s11469-012-9399-9.CrossRefGoogle Scholar
  24. Jackson, A. C., Wynne, H., Dowling, N. A., Tomnay, J. E., & Thomas, S. A. (2010). Using the CPGI to determine problem gambling prevalence in Australia: Measurement issues. International Journal of Mental Health and Addiction, 8(4), 570–582.CrossRefGoogle Scholar
  25. Lam, D. (2007). An exploratory study of gambling motivations and their impact on the purchase frequencies of various gambling products. Psychology and Marketing, 24(9), 815–827.CrossRefGoogle Scholar
  26. Lee, H. P., Chae, P. K., Lee, H. S., & Kim, Y. K. (2007). The five-factor gambling motivation model. Psychiatry Research, 150(1), 21–32.CrossRefPubMedGoogle Scholar
  27. Lee, C. K., Lee, Y. K., Bernhard, B. J., & Yoon, Y. S. (2006). Segmenting casino gamblers by motivation: A cluster analysis of Korean gamblers. Tourism Management, 27(5), 856–866.CrossRefGoogle Scholar
  28. Lloyd, J., Doll, H., Hawton, K., Dutton, W. H., Geddes, J. R., Goodwin, G. M., et al. (2010). How psychological symptoms relate to different motivations for gambling: An online study of internet gamblers. Biological Psychiatry, 68(8), 733–740. doi: 10.1016/j.biopsych.2010.03.038.CrossRefPubMedGoogle Scholar
  29. Matsunaga, M. (2010). How to factor-analyse your data right: Do’s, don’ts, and how-to’s. International Journal of Psychological Research, 3(1), 97–110.Google Scholar
  30. McGrath, D. S., Stewart, S. H., Klein, R. M., & Barrett, S. P. (2010). Self-generated motives for gambling in two population-based samples of gamblers. International Gambling Studies, 10(2), 117–138. doi: 10.1080/14459795.2010.499915.CrossRefGoogle Scholar
  31. McMillen, J., Marshall, D., Wenzel, M., & Ahmed, A. (2004). Validation of the Victorian gambling screen. Melbourne, Victoria: Gambling Research Panel.Google Scholar
  32. Mond, J., Davidson, T., & McAllister, I. (2011). ANU Poll 2011: Public opinion on Gambling (computer file). Canberra: Australian Data Archive, The Australian National University.Google Scholar
  33. Myrseth, H., Brunborg, G., & Eidem, M. (2010). Differences in cognitive distortions between pathological and non-pathological gamblers with preferences for chance or skill games. Journal of Gambling Studies, 26(4), 561–569. doi: 10.1007/s10899-010-9180-6.CrossRefPubMedGoogle Scholar
  34. Neal, P., Delfabbro, P., & O’Neil, M. (2005). Problem gambling and harm: A national definition. Adelaide: South Australian Centre for Economic Studies.Google Scholar
  35. Orford, J., Griffiths, M., Wardle, H., Sproston, K., & Erens, B. (2009). Negative public attitudes towards gambling: Findings from the 2007 British gambling prevalence survey using a new attitude scale. International Gambling Studies, 9(1), 39–54.CrossRefGoogle Scholar
  36. Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psycho educational Assessment, 29(4), 304–321.CrossRefGoogle Scholar
  37. Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modelling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338.CrossRefGoogle Scholar
  38. Stewart, S.H. (2013). Short-term outcome of a motive-matched treatment for coping and enhancement gamblers: a randomized controlled trial. Paper presented at Alberta Gambling Research Institute’s 12th Annual Conference on Gambling Research, “Research to Practice in Gambling Disorders”, Banff, Alberta, Canada.Google Scholar
  39. Stewart, S. H., & Zack, M. (2008). Development and psychometric evaluation of a three-dimensional Gambling Motives Questionnaire. Addiction, 103(7), 1110–1117. doi: 10.1111/j.1360-0443.2008.02235.x.CrossRefPubMedGoogle Scholar
  40. Stewart, S. H., Zack, M., Collins, P., & Klein, R. M. (2008). Sub typing pathological gamblers on the basis of affective motivations for gambling: Relations to gambling problems, drinking problems, and affective motivations for drinking. Psychology of Addictive Behaviours, 22(2), 257.CrossRefGoogle Scholar
  41. Suhr, D. D. (2006). Exploratory or confirmatory factor analysis? (pp. 200–231). Cary: SAS Institute.Google Scholar
  42. Tao, V. Y., Wu, A. M., Cheung, S. F., & Tong, K. K. (2011). Development of an indigenous inventory GMAB (gambling motives, attitudes and behaviours) for Chinese gamblers: An exploratory study. Journal of Gambling Studies, 27(1), 99–113.CrossRefPubMedGoogle Scholar
  43. Thomas, A., Allen, F., & Phillips, J. (2009). Electronic gaming machine gambling: Measuring motivation. Journal of Gambling Studies, 25(3), 343–355. doi: 10.1007/s10899-009-9133-0.CrossRefPubMedGoogle Scholar
  44. Wardle, H., Dobbie, F., Kerr, J., & Reith, G. (2009). Questionnaire development for a longitudinal study of gamblers. Phase 1: Gambling commission. http://www.gamblingcommission.gov.uk/pdf/Questionnairedevelopmentforalongitudinalstudyofgamblers-phase1-June2009.pdf.
  45. Wardle, H., Moody, A., Spence, S., Orford, J., Volberg, R., & Jotangia, D. et al. (2011). British gambling prevalence survey 2010 London: National Centre for Social Research.Google Scholar
  46. Watkins, M. (2008). Monte Carlo PCA for Parallel Analysis 2.3 http://www.softpedia.com/get/Others/Home-Education/Monte-Carlo-PCA-for-Parallel-Analysis.shtml.
  47. Wu, A. M., Tao, V. Y., Tong, K. K., & Cheung, S. F. (2012). Psychometric evaluation of the inventory of gambling motives, attitudes and behaviours (GMAB) among Chinese gamblers. International Gambling Studies, 12(3), 331–347.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • K. L. Francis
    • 1
  • N. A. Dowling
    • 1
    • 2
    • 3
  • A. C. Jackson
    • 1
  • D. R. Christensen
    • 1
  • H. Wardle
    • 4
  1. 1.Problem Gambling Research and Treatment CentreUniversity of MelbourneMelbourneAustralia
  2. 2.School of PsychologyDeakin UniversityBurwoodAustralia
  3. 3.School of Psychology and PsychiatryMonash UniversityMelbourneAustralia
  4. 4.National Centre for Social Research (NatCen)LondonUK

Personalised recommendations