Measuring Behavioural Dependence in Gambling: A Case for Removing Harmful Consequences from the Assessment of Problem Gambling Pathology

Abstract

Behavioural dependence (BD) for gambling has traditionally been subsumed under the concept of ‘problems’: a hybrid construct that includes both indicators of BD, and adverse consequences (harm) arising from excessive time and money expenditure. Although progress has been made towards specific measurement of harm, dedicated measures of BD do not exist. Theory led us to expect that (1) dependence and harm are measurably distinct constructs, (2) harm mediates the relationship between dependence and wellbeing, and finally, that (3) separate measures should be more effective than a unidimensional problems measure in predicting wellbeing. Candidate BD items from six existing measures of gambling problems were extracted and evaluated with respect to DSM-5 criteria and content overlap, leading to 17 candidate items. This was further reduced to 8 items based on both item content and psychometric criteria, using data from an online panel of 1524 regular gamblers, with demographic characteristics similar to Australian population norms. Participants also completed measures of harm, problems, and subjective wellbeing. All three hypotheses were confirmed. BD was shown to be highly reliable and unidimensional, and measurably distinct from gambling harms. Harm mediated the negative relationship between BD and wellbeing. The harm + BD model yielded better predictions of personal wellbeing that a unidimensional, continuous problems measure—and explained about twice the variance of a simple contrast between problem and non-problem gamblers. We conclude that is psychometrically justified to specifically measure gambling BD, and this may be of particular use in theoretically-driven applications.

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References

  1. Abbott, M. (2006). Do EGMs and problem gambling go together like a horse and carriage? Gambling Research: Journal of the National Association for Gambling Studies (Australia), 18(1), 7.

    Google Scholar 

  2. Aiken, L. R. (1983). Number of response categories and statistics on a teacher rating scale. Educational and Psychological Measurement, 43(2), 397–401.

    Google Scholar 

  3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). ‎Philadelphia: American Psychiatric Pub.

    Google Scholar 

  4. Awad, Z., Taghi, A. S., Sethukumar, P., Ziprin, P., Darzi, A., & Tolley, N. S. (2014). Binary versus 5-Point Likert scale in assessing otolaryngology trainees in endoscopic sinus surgery. Otolaryngology-Head and Neck Surgery: Official Journal of American Academy of Otolaryngology-Head and Neck Surgery, 151(1_suppl), P113–P113.

    Google Scholar 

  5. Battersby, M. W., Thomas, L. J., Tolchard, B., & Esterman, A. (2002). The South Oaks gambling screen: A review with reference to Australian use. Journal of Gambling Studies, 18(3), 257–271.

    PubMed  Google Scholar 

  6. Blaszczynski, A., Ladouceur, R., & Shaffer, H. J. (2004). A science-based framework for responsible gambling: The Reno model. Journal of Gambling Studies, 20(3), 301–317.

    PubMed  Google Scholar 

  7. Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97(5), 487–499.

    PubMed  Google Scholar 

  8. Blaszczynski, A., Walker, M., Sagris, A., & Dickerson, M. (1999). Psychological aspects of gambling behaviour: An Australian psychological society position paper. Australian Psychologist, 34(1), 4–16.

    Google Scholar 

  9. Browne, M., Goodwin, B. C., & Rockloff, M. J. (2017a). Validation of the short gambling harm screen (SGHS): A tool for assessment of harms from gambling. Journal of Gambling Studies. https://doi.org/10.1007/s10899-017-9698-y.

    Article  PubMed  Google Scholar 

  10. Browne, M., Greer, N., Rawat, V., & Rockloff, M. (2017b). A population-level metric for gambling-related harm. International Gambling Studies, 17, 1–14.

    Google Scholar 

  11. Browne, M., Rawat, V., Greer, N., Langham, E., Rockloff, M., & Hanley, C. (2017c). What is the harm? Applying a public health methodology to measure the impact of gambling problems and harm on quality of life. Journal of Gambling Issues, 36, 28–50.

    Google Scholar 

  12. Browne, M., & Rockloff, M. J. (2017). The dangers of conflating gambling-related harm with disordered gambling. Journal of Behavioral Addictions, 6, 1–4.

    Google Scholar 

  13. Browne, M., & Rockloff, M. J. (2018). Prevalence of gambling-related harm provides evidence for the prevention paradox. Journal of Behavioral Addictions, 7(2), 410–422.

    PubMed  PubMed Central  Google Scholar 

  14. Cummins, R. A., Eckersley, R., Pallant, J., van Vugt, J., & Misajon, R. (2003). Developing a national index of subjective wellbeing: The Australian Unity Wellbeing Index. Social Indicators Research, 64(2), 159–190.

    Google Scholar 

  15. Cummins, R. A., Woerner, J., Gibson, A., Weinberg, M., Collard, J., & Chester, M. (2004). Australian Unity Wellbeing Index Survey 10. Australian Centre for Quality of Life, Deakin University, Melbourne, Report, 10. Retrieved from https://www.researchgate.net/profile/Melissa_Weinberg/publication/261702245_Australian_Unity_Wellbeing_Index_Survey_20/links/004635350cfe162cc6000000/Australian-Unity-Wellbeing-Index-Survey-20.pdf.

  16. Delfabbro, P. (2013). Problem and pathological gambling: A conceptual review. The Journal of Gambling Business and Economics, 7(3), 35–53.

    Google Scholar 

  17. Ferris, J., & Wynne, H. (2001). The Canadian problem gambling index. Ottawa, ON: Canadian Centre on Substance Abuse. Retrieved from http://ccgr.ca/sites/default/files/CPGI-Final-Report-English.pdf.

  18. Gebauer, L., LaBrie, R., & Shaffer, H. J. (2010). Optimizing DSM-IV-TR classification accuracy: A brief biosocial screen for detecting current gambling disorders among gamblers in the general household population. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 55(2), 82–90.

    PubMed  Google Scholar 

  19. Govoni, R., Frisch, G. R., & Stinchfield, R. (2001). A critical review of screening and assessment instruments for problem gambling. Windsor, ON: Problem Gambling Research Group, University of Windsor.

    Google Scholar 

  20. Grassi, M., Nucera, A., Zanolin, E., Omenaas, E., Anto, J. M., Leynaert, B., et al. (2007). Performance comparison of Likert and binary formats of SF-36 version 1.6 across ECRHS II adults populations. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 10(6), 478–488.

    Google Scholar 

  21. Holtgraves, T. (2009). Evaluating the problem gambling severity index. Journal of Gambling Studies, 25(1), 105–120.

    PubMed  Google Scholar 

  22. 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.

    Google Scholar 

  23. Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 6–23.

    PubMed  Google Scholar 

  24. Johnson, E. E., Hamer, R., Nora, R. M., Tan, B., Eisenstein, N., & Engelhart, C. (1997). The Lie/Bet Questionnaire for screening pathological gamblers. Psychological Reports, 80(1), 83–88.

    CAS  PubMed  Google Scholar 

  25. Johnson, E. E., Hamer, R. M., & Nora, R. M. (1998). The Lie/Bet Questionnaire for screening pathological gamblers: A follow-up study. Psychological Reports, 83(3 Pt 2), 1219–1224.

    CAS  PubMed  Google Scholar 

  26. Komorita, S. S., & Graham, W. K. (1965). Number of scale points and the reliability of scales. Educational and Psychological Measurement, 25(4), 987–995.

    Google Scholar 

  27. Langham, E., Thorne, H., Browne, M., Donaldson, P., Rose, J., & Rockloff, M. (2016). Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health, 16(1), 80.

    PubMed  PubMed Central  Google Scholar 

  28. Lau, A. L. D., Cummins, R. A., & Mcpherson, W. (2005). An investigation into the cross-cultural equivalence of the personal wellbeing index. Social Indicators Research, 72(3), 403–430.

    Google Scholar 

  29. Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. The American Journal of Psychiatry, 144(9), 1184–1188.

    CAS  PubMed  Google Scholar 

  30. Li, C.-H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949.

    PubMed  Google Scholar 

  31. Lozano, L. M., García-Cueto, E., & Muñiz, J. (2008). Effect of the number of response categories on the reliability and validity of rating scales. Methodology: European Journal of Research Methods for the Behavioral & Social Sciences, 4(2), 73–79.

    Google Scholar 

  32. Matell, M. S., & Jacoby, J. (1971). Is there an optimal number of alternatives for Likert scale items? Study I: Reliability and validity. Educational and Psychological Measurement, 31(3), 657–674.

    Google Scholar 

  33. McElroy, S. L., & Hudson, J. I. (1992). The DSM-III-R impulse control disorders not elsewhere classified: clinical characteristics and relationship to other psychiatric disorders. The American Journal of Psychiatry, 149(3), 318.

    CAS  PubMed  Google Scholar 

  34. McMillen, J., Marshall, D., Wenzel, M., & Ahmed, A. (2004). Validation of the Victorian gambling screen. Melbourne, VIC: Gambling Research Panel.

    Google Scholar 

  35. Merkle, E., You, D., Schneider, L., Bae, S., & Merkle, M. E. (2018). Package “nonnest2”. Psychological Methods, 21, 151–163.

    Google Scholar 

  36. Mueller, R. O., & Hancock, G. R. (2008). Best practices in structural equation modeling. Best Practices in Quantitative Methods, 488508. Retrieved from http://www.corwin.com/sites/default/files/upm-binaries/18067_Chapter_32.pdf.

  37. Neal, P. N., Delfabbro, P. H., & O’Neil, M. G. (2005). Problem gambling and harm: Towards a national definition. Melbourne: Gambling Research Australia. Retrieved from https://hekyll.services.adelaide.edu.au/dspace/handle/2440/40558.

  38. Nower, L., & Blaszczynski, A. (2006). Impulsivity and pathological gambling: A descriptive model. International Gambling Studies, 6(1), 61–75.

    Google Scholar 

  39. Preston, C. C., & Colman, A. M. (2000). Optimal number of response categories in rating scales: Reliability, validity, discriminating power, and respondent preferences. Acta Psychologica, 104(1), 1–15.

    CAS  PubMed  Google Scholar 

  40. R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org.

  41. Raju, N. S., Laffitte, L. J., & Byrne, B. M. (2002). Measurement equivalence: A comparison of methods based on confirmatory factor analysis and item response theory. Journal of Applied Psychology, 87(3), 517.

    PubMed  Google Scholar 

  42. Raykov, T. (1997). Scale reliability, Cronbach’s coefficient alpha, and violations of essential tau-equivalence with fixed congeneric components. Multivariate Behavioral Research, 32(4), 329–353.

    CAS  PubMed  Google Scholar 

  43. Revelle, W. (2011). An overview of the psych package. Psychology Northwestern University. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.190.7429&rep=rep1&type=pdf.

  44. Rose, G. (1985). Sick individuals and sick populations. International Journal of Epidemiology, 14(1), 32–38.

    CAS  PubMed  Google Scholar 

  45. Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 48(2), 1–36.

    Google Scholar 

  46. Satorra, A. (2000). Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In R. D. H. Heijmans, D. S. G. Pollock, & A. Satorra (Eds.), Innovations in multivariate statistical analysis: A Festschrift for Heinz Neudecker (pp. 233–247). Boston, MA: Springer.

    Google Scholar 

  47. Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological Assessment, 8(4), 350.

    Google Scholar 

  48. 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.

    Google Scholar 

  49. Svetieva, E., & Walker, M. (2008). Inconsistency between concept and measurement: The Canadian Problem Gambling Index (CPGI). Journal of Gambling Issues, 22, 157–173.

    Google Scholar 

  50. Toneatto, T. (2008). Reliability and validity of the gamblers anonymous twenty questions. Journal of Psychopathology and Behavioral Assessment, 30(1), 71–78.

    Google Scholar 

  51. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 57(2), 307–333.

    Google Scholar 

  52. West, S. G., Taylor, A. B., Wu, W., et al. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). New York: The Guilford Press.

    Google Scholar 

  53. Wiebe, J. M., Cox, B. J., & Mehmel, B. G. (2000). The South Oaks Gambling Screen revised for adolescents (SOGS-RA): Further psychometric findings from a community sample. Journal of Gambling Studies, 16(2–3), 275–288.

    CAS  PubMed  Google Scholar 

  54. Williams, R. J., & Volberg, R. A. (2010). Best practices in the population assessment of problem gambling. Faculty of Health Sciences. Retrieved from https://www.uleth.ca/dspace/handle/10133/1259.

  55. Winters, K. C., Stinchfield, R. D., & Fulkerson, J. (1993). Toward the development of an adolescent gambling problem severity scale. Journal of Gambling Studies, 9(1), 63–84.

    Google Scholar 

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Funding

Funding for the present study was provided by the Victorian Responsible Gambling Foundation (No reference number provided). MB and MR have received funding from the New South Wales Office of Liquor and Gaming, the Victorian Responsible Gambling Foundation, the Queensland Government Department of Health, the Tasmanian Department of Treasury and Finance, the Alberta Gambling Research Institute, Gambling Research Australia, the New Zealand Ministry of Health, the Department of Families, Housing, Community Services and Indigenous Affairs. MB has also received funding from the Australian Department of Innovation, Industry, Science and Research, and the Department of Foreign Affairs and Trade.

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Correspondence to Matthew Browne.

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Appendix

Appendix

Label Item
fin_sav Reduction of my savings
fin_spend Reduction of my available spending money
fin_ccard Increased credit card debt
fin_items Sold personal items
fin_rec Less spending on recreational expenses such as eating out, going to movies or other entertainment
rel_time Spent less time with people I care about
emo_distress Felt distressed about my gambling
emo_shame Felt ashamed of my gambling
emo_failure Felt like a failure
emo_regrets Had regrets that made me feel sorry about my gambling

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Browne, M., Rockloff, M.J. Measuring Behavioural Dependence in Gambling: A Case for Removing Harmful Consequences from the Assessment of Problem Gambling Pathology. J Gambl Stud 36, 1027–1044 (2020). https://doi.org/10.1007/s10899-019-09916-2

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Keywords

  • Gambling
  • Behavioural dependence
  • Addiction
  • Psychometrics