Advertisement

Journal of Gambling Studies

, 23:479 | Cite as

Examining DSM-IV Criteria for Pathological Gambling: Psychometric Properties and Evidence from Cognitive Biases

  • Chad E. Lakey
  • Adam S. Goodie
  • Charles E. Lance
  • Randy Stinchfield
  • Ken C. Winters
Original Paper

Abstract

We examined the DSM-IV criteria for pathological gambling as assessed with the DSM-IV-based Diagnostic Interview for Gambling Severity (DIGS; Winters, Specker, & Stinchfield, 2002). We first analyzed the psychometric properties of the DIGS, and then assessed the extent to which performance on two judgment and decision-making tasks, the Georgia Gambling Task (Goodie, 2003) and the Iowa Gambling Task (Bechara, Damasio, Damasio, & Anderson, 1994), related to higher reports of gambling pathology. In a sample of frequent gamblers, we found strong psychometric support for the DSM-IV conception of pathological gambling as measured by the DIGS, predictive relationships between DIGS scores and all cognitive performance measures, and significant differences in performance measures between individuals with and without pathological gambling. Analyses using suggested revisions to the pathological gambling threshold (Stinchfield, 2003) revealed that individuals meeting four of the DSM-IV criteria aligned significantly more with pathological gamblers than with non-pathological gamblers, supporting the suggested change in the cutoff score from five to four symptoms. Discussion focuses on the validity of the DSM-IV criteria as assessed by the DIGS and the role of cognitive biases in pathological gambling.

Keywords

Pathological gambling DSM-IV criteria Georgia Gambling Task Iowa Gambling Task Overconfidence 

Notes

Acknowledgements

This research was supported in part by National Institute of Mental Health research grant R01 MH067827 to A.S. Goodie, National Institute on Aging research grant AG15321, National Institute of Drug Abuse research grant R01 DA019460, and National Institute of Health research grant R03 CA117470 to C.E. Lance, and National Institute on Drug Abuse research grant K02 DA15347 to K.C. Winters.

References

  1. American Psychiatric Association (DSM-III). (1980). Diagnostic and statistical manual of mental disorders, 3rd ed. Washington, DC: American Psychiatric Press.Google Scholar
  2. American Psychiatric Association (DSM-IV-TR). (2000). Diagnostic and statistical manual of mental disorders, 4th ed., text revision. Washington, DC: American Psychiatric Press.Google Scholar
  3. Baboushkin, H. R., Hardoon, K. K., Derevensky, J. L., & Gupta, R. (2001). Underlying cognitions in gambling behavior among university students. Journal of Applied Social Psychology, 31, 1409–1430.CrossRefGoogle Scholar
  4. Bechara, A. (2001). Risky business: Emotion, decision-making, and addiction. Journal of Gambling Studies, 19, 23–51.CrossRefGoogle Scholar
  5. Bechara, A., & Damasio, H. (2002). Decision-making and addiction (part I): Impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia, 40, 1675–1689.CrossRefGoogle Scholar
  6. Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7–15.CrossRefGoogle Scholar
  7. Bechara, A., Damasio, H., & Damasio, A. R. (2000a). Emotion, decision-making, and the orbitofrontal cortex. Cerebral Cortex, 10, 295–307.CrossRefGoogle Scholar
  8. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293–1295.CrossRefGoogle Scholar
  9. Bechara, A., Dolan, S., & Hindes, A. (2002). Decision-making and addiction (part II): myopia for the future or hypersensitivity to reward? Neuropsychologia, 40, 1690–1705.CrossRefGoogle Scholar
  10. Bechara, A., Tranel, D., & Damasio, H. (2000b). Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain, 123, 2189–2202.CrossRefGoogle Scholar
  11. Bradford, J., Geller, J., Lesieur, H. R., Rosenthal, R., & Wise, M. (1996). Impulse control disorders. In T. A. Widger, A. J. Francis, H. A. Pincus, R. Ross, M. B. First, & W. Wakefield Davis (Eds.), DSM-IV sourcebook, vol. 2 (pp. 1007–1031). Washington, DC: American Psychiatric Association.Google Scholar
  12. Camchong, J., Goodie, A. S., McDowell, J. E., Gilmore, C. S., & Clementz, B. A. (in press). A cognitive neuroscience approach to the role of overconfidence in pathological gambling. Journal of Gambling Studies.Google Scholar
  13. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.CrossRefGoogle Scholar
  14. Fischhoff, B., Slovic, P., & Lichtenstein, S. (1977). Knowing with certainty: The appropriateness of extreme confidence. Journal of Experimental Psychology: Human Perception and Performance, 3, 552–564.CrossRefGoogle Scholar
  15. Goodie, A. S. (2003). The effects of control on betting: Paradoxical betting on items of high confidence with low value. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 598–610.CrossRefGoogle Scholar
  16. Goodie, A. S. (2005). The role of percieved control and overconfidence in pathological gambling. Journal of Gambling Studies, 21, 481–502.CrossRefGoogle Scholar
  17. Hardy, D. J., Hinkin, C. H., Levine, A. J., Castellon, S. A., & Lam, M. N. (2006). Risky decision-making assessed with the Gambling Task in adults with HIV. Neuropsychology, 20, 355–360.CrossRefGoogle Scholar
  18. Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling. Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.CrossRefGoogle Scholar
  19. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  20. James, L. R., Demaree, R. G., & Mulaik, S. A. (1986). A note on validity generalization procedures. Journal of Applied Psychology, 71, 440–450.CrossRefGoogle Scholar
  21. Jöreskog, K. G., & Sörbom, D. (2005). LISREL 8.72 [Computer software]. Lincolnwood, IL: Scientific Software International.Google Scholar
  22. Ladouceur, R. (2004). Gambling: The hidden addiction. Canadian Journal of Psychiatry, 49, 501–503.Google Scholar
  23. Ladouceur, R., Bouchard, C., Rheaume, N., Jacques, C., Ferland, F., Leblond, J., & Walker, M. (2000). Is the SOGS an accurate measure of pathological gambling among children, adolescents and adults? Journal of Gambling Studies, 16, 1–24.CrossRefGoogle Scholar
  24. Ladouceur, R., Ferland, F., Poulin, C., Vitaro, F., & Wiebe, J. (2005). Concordance between the SOGS-RA and the DSM-IV criteria for pathological gambling among youth. Psychology of Addictive Behaviors, 19, 271–276.CrossRefGoogle Scholar
  25. Ladouceur, R., Sylvain, C., Boutain, C., Lachance, S., Doucet, C., & Leblond, J. (2003). Group therapy for pathological gamblers: A cognitive approach. Behaviour Research and Therapy, 41, 587–596.CrossRefGoogle Scholar
  26. Lakey, C. E., Goodie, A. S., & Campbell, W. K. (in press). Frequent card playing and pathological gambling: The utility of the Georgia Gambling Task and Iowa Gambling Task for predicting pathology. Journal of Gambling Studies.Google Scholar
  27. Lance, C. E., & Vandenberg, R. J. (2002). Confirmatory factor analysis. In F. Drasgow, & N. Schmitt (Eds.), Advances in measurement and data analysis (pp. 221–254). San Francisco, CA: Jossey-Bass.Google Scholar
  28. Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 1184–1188.Google Scholar
  29. Lesieur, H. R., & Rosenthal, R. J. (1991). Pathological gambling: A review of the literature (prepared for the American Psychiatric Association Task Force on DSM-IV Committee on Disorders of Impulse Control not elsewhere classified). Journal of Gambling Studies, 7, 5–39.CrossRefGoogle Scholar
  30. Lesieur, H. R., & Rosenthal, R. J. (1998). Analysis of pathological gambling. In T. A. Widger, A. J. Francis, H. A. Pincus, R. Ross, M. B. First, W. Davis, & M. Kline (Eds.), DSM-IV sourcebook, vol. 4 (pp. 393–401). Washington, DC: American Psychiatric Association.Google Scholar
  31. Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.Google Scholar
  32. Petry, N. M. (2005). Gamblers anonymous and cognitive-behavioral therapies for pathological gamblers. Journal of Gambling Studies, 21, 27–33.CrossRefGoogle Scholar
  33. Productivity Commission. (1999). Australia’s gambling industries: Final report. Canberra: Government Press.Google Scholar
  34. Raylu, N., & Oei, T. P. S. (2004). The Gambling Related Cognitions Scale (GRCS): development, confirmatory factor validation and psychometric properties. Addiction, 99, 757–769.CrossRefGoogle Scholar
  35. Raylu, N., & Oei, T. P. S. (2002). Pathological gambling: A comprehensive review. Clinical Psychology Review, 22, 1009–1061.CrossRefGoogle Scholar
  36. Shaffer, H. J., & Hall, M. N. (1996). Estimating the prevalence of adolescent gambling disorders: A quantitative synthesis and guide toward standard gambling nomenclature. Journal of Gambling Studies, 12, 193–214.CrossRefGoogle Scholar
  37. Steenbergh, T. A., Meyers, A. W., May, R. K., & Whelan, J. P. (2002). Development and validation of the Gamblers’ Beliefs Questionnaire. Psychology of Addictive Behaviors, 16, 143–149.CrossRefGoogle Scholar
  38. Stinchfield, R. (2002). Reliability, validity, and classification accuracy of the South Oaks Gambling Screen (SOGS). Addictive Behaviors, 27, 1–19.CrossRefGoogle Scholar
  39. Stinchfield, R. (2003). Reliability, validity, and classification accuracy of a measure of DSM-IV diagnostic criteria for pathological gambling. American Journal of Psychiatry, 160, 180–182.CrossRefGoogle Scholar
  40. Stinchfield, R., Govoni, R., & Frisch, G. R. (2005). DSM-IV diagnostic criteria for pathological gambling: Reliability, validity, and classification accuracy. The American Journal on Addictions, 14, 73–82.CrossRefGoogle Scholar
  41. Stinchfield, R., & Winters, K. C. (2001). Outcome of Minnesota’s gambling treatmentprograms. Journal of Gambling Studies, 17, 217–245.CrossRefGoogle Scholar
  42. Stinchfield, R., & Winters, K. C. (1998). Gambling and problem gambling among youths. AAPSS Annals, 556, 172–185.Google Scholar
  43. Toce-Gerstein, M., Gerstein, D. R., & Volberg, R. A. (2003). A hierarchy of gambling disorders in the community. Addiction, 98, 1661–1672.CrossRefGoogle Scholar
  44. Toneatto, T. (1999). Cognitive psychopathology of problem gambling. Substance Use and Misuse, 34, 1593–1604.CrossRefGoogle Scholar
  45. Toneatto, T., Blitz-Miller, T., Calderwood, K., Dragonetti, R., Tsanos, A. (1997). Cognitive distortions in heavy gambling. Journal of Gambling Studies, 13, 253–266.CrossRefGoogle Scholar
  46. Toneatto, T., & Ladouceur, R. (2003). Treatment of pathological gambling: A critical review of the literature. Psychology of Addictive Behaviors, 17, 284–292.CrossRefGoogle Scholar
  47. Toneatto, T., & Millar, G. (2004). Assessing and treating problem gambling: Empirical status and promising trends. Canadian Journal of Psychiatry, 49, 517–525.Google Scholar
  48. Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 2, 4–69.CrossRefGoogle Scholar
  49. Winters, K. C., Bengston, P., Dorr, D., & Stinchfield, R. (1998). Prevalence and risk factors of problem gambling among college students. Psychology of Addictive Behaviors, 12, 127–135.CrossRefGoogle Scholar
  50. Winters, K. C., Specker, S., & Stinchfield, R. (2002). Measuring pathological gambling with The Diagnostic Interview for Gambling severity (DIGS). In J.J. Marotta, J.A. Cornelius, & W. R. Eadington (Eds.), The downside: Problem and pathological gambling (pp. 143–148). Reno, NV: University of Nevada, Reno.Google Scholar
  51. Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychological Science, 16, 973–978.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2007

Authors and Affiliations

  • Chad E. Lakey
    • 1
  • Adam S. Goodie
    • 1
  • Charles E. Lance
    • 1
  • Randy Stinchfield
    • 2
  • Ken C. Winters
    • 2
  1. 1.Department of PsychologyUniversity of GeorgiaAthensUSA
  2. 2.University of MinnesotaMinneapolisUSA

Personalised recommendations