Journal of Gambling Studies

, Volume 28, Issue 1, pp 47–68 | Cite as

Dimensions of Problem Gambling Behavior Associated with Purchasing Sports Lottery

  • Hai Li
  • Luke Lunhua Mao
  • James J. Zhang
  • Yin Wu
  • Anmin Li
  • Jing Chen
Original Paper

Abstract

The purpose of this study was to identify and examine the dimensions of problem gambling behaviors associated with purchasing sports lottery in China. This was accomplished through the development and validation of the Scale of Assessing Problem Gambling (SAPG). The SAPG was initially developed through a comprehensive qualitative research process. Research participants (N = 4,982) were Chinese residents who had purchased sports lottery tickets, who responded to a survey packet, representing a response rate of 91.4%. Data were split into two halves, one for conducting an EFA and the other for a CFA. A five-factor model with 19 items (Social Consequence, Financial Consequence, Harmful Behavior, Compulsive Disorder, and Depression Sign) showed good measurement properties to assess problem gambling of sports lottery consumers in China, including good fit to the data (RMSEA = 0.050, TLI = 0.978, and CFI = 0.922), convergent and discriminate validity, and reliability. Regression analyses revealed that except for Depression Sign, the SAPG factors were significantly (P < 0.05) predictive of purchase behaviors of sports lottery. This study represents an initial effort to understand the dimensions of problem gambling associated with Chinese sports lottery. The developed scale may be adopted by researchers and practitioners to examine problem gambling behaviors and develop effective prevention and intervention procedures based on tangible evidence.

Keywords

Problem gambling Reliability Validity Sports lottery Diagnostic criteria 

References

  1. Abbott, M. W., & Volberg, R. A. (2006). The measurement of adult problem and pathological gambling. International Gambling Studies, 6(2), 175–200.CrossRefGoogle Scholar
  2. American Gaming Association (2009). Gaming revenue: Current-year data. Retrieved 20 Dec 2010. Available from: http://www.americangaming.org/Industry/factsheets/statistics.
  3. American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: American Psychiatric Association.Google Scholar
  4. Becona, E. (1997). Pathological gambling in Spanish children and adolescents: An emerging problem. Psychological Reports, 81(1), 275–287.PubMedCrossRefGoogle Scholar
  5. Becoña, E. (1996). Prevalence surveys of problem and pathological gambling in Europe: The cases of Germany, Holland, and Spain. Journal of Gambling Studies, 12, 179–192.CrossRefGoogle Scholar
  6. Blaszczynski, A. P. (1995).Workshop on the assessment and treatment of pathological gambling. Paper presented at Australian and New Zealand Association of Psychiatry, Psychology and the Law Conference, Melbourne, Australia.Google Scholar
  7. Blaszczynski, A., Ladouceur, R., & Shaffer, H. S. (2004). A science-based framework for responsible gambling: The Reno model. Journal of Gambling Studies, 20, 301–317.PubMedCrossRefGoogle Scholar
  8. Blaszczynski, A. P., & McConaghy, N. (1989). Anxiety and/or depression in the pathogenesis of addictive gambling. International Journal of Addictions, 24, 337–350.Google Scholar
  9. Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97, 487–499.PubMedCrossRefGoogle Scholar
  10. Bondolfi, G., Osiek, C., & Ferrero, F. (2000). Prevalence estimates of pathological gambling in Switzerland. Acta Psychiatrica Scandinavica, 101, 473–475.PubMedCrossRefGoogle Scholar
  11. Broughton, D., Lee, J., & Nethery, R. (1999). The question: How big is US sports industry? Street and Smiths Sports Business Journal, 2(35), 23–29.Google Scholar
  12. Browne, M. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111–150.CrossRefGoogle Scholar
  13. Chin, W. (1998). The partial least squares approach to structural equation modeling: Modern methods for business research. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  14. Productivity Commission. (1999). Australia’s gambling industries. Report No. 10. Canberra, Australia: AusInfo.Google Scholar
  15. Gambling Commission (2010). Annual report and accounts 2009/10. Available from: www.gamblingcommission.gov.uk.
  16. Cotte, J. (1997). Chances, trances, and lots of slots: Gambling motives and consumption experiences. Journal of Leisure Research, 29(4), 380–406.Google Scholar
  17. Cunningham-Williams, R. M., Grucza, R. A., Cottler, L. B., Womack, S. B., Books, S. J., & Przybeck, T. R. (2005). Prevalence and predictors of pathological gambling: Results from the St. Louis personality, health and lifestyle (SLPHL) study. Journal of Psychiatric Research, 39(4), 377–390.PubMedCrossRefGoogle Scholar
  18. Daghestani, A. N., Elenz, E., & Crayton, J. W. (1996). Pathological gambling in hospitalised substance abusing veterans. Journal of Clinical Psychiatry, 57(8), 360–363.PubMedGoogle Scholar
  19. Dannon, P. N., Lowengrub, K., Sasson, M., Shalgi, B., Tuson, L., & Saphir, Y. (2004). Comorbid psychiatric diagnoses in kleptomania and pathological gambling: A preliminary comparison study. European Psychiatry, 19(5), 299–302.PubMedCrossRefGoogle Scholar
  20. Dannon, P. N., Lowengrub, K., Shalgi, B., Sasson, M., Tuson, L., & Saphir, Y. (2006). Dual psychiatric diagnosis and substance abuse in pathological gamblers: A preliminary gender comparison study. Journal of Addictive Diseases, 25(3), 49–54.PubMedCrossRefGoogle Scholar
  21. Desai, R. A. (2006). Pathological gambling. Southern Medical Journal, 99(1), 12.PubMedCrossRefGoogle Scholar
  22. Dickerson, M. G. (1993). A preliminary exploration of a two-stage methodology in the assessment of the extent and degree of gambling-related problems in the Australian population. In W. R. Eadington & J. A. Cornelius (Eds.), Gambling behavior and problem gambling (pp. 347–364). Reno: University of Nevada Press.Google Scholar
  23. Dumont, M., & Ladouceur, R. (1990). Evaluation of motivation among video-poker players. Psychological Reports, 66, 95–98.CrossRefGoogle Scholar
  24. Echeburu′a, E., Ba′ez, C., Ferna′ndez, J., & Pa′ez, D. (1994). Cuestionario de juego patolo′gico de South Oaks (SOGS): Validacio′n espan˜ola. Ana′lisis y Modificacio′n de Conducta, 20, 769–791.Google Scholar
  25. Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.CrossRefGoogle Scholar
  26. Fernandez-Aranda, F., Jimenez-Murcia, S., Alvarez-Moya, E. M., Granero, R., Vallejo, J., & Bulik, C. M. (2006). Impulse control disorders in eating disorders: Clinical and therapeutic implications. Comprehensive Psychiatry, 47(6), 482–488.PubMedCrossRefGoogle Scholar
  27. Ferris, J., & Wynne, H. J. (2001). The Canadian problem gambling index final report. Ottawa, ON: Canadian Centre on Substance Abuse.Google Scholar
  28. China Financial and Economic Publishing House (2007, 2008, 2009). China Lottery Almanac 2007, 2008, 2009. Beijing: China Financial and Economic Publishing House.Google Scholar
  29. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.CrossRefGoogle Scholar
  30. Galski, T. (1987). Psychological testing of pathological gamblers: research, uses and new directions. In T. Galeski (Ed.), The handbook of pathological gambling (pp. 123–135). New York: Charles C. Thomas.Google Scholar
  31. General Administration of Sport of China (1998). Provisional regulations on administration of public welfare fund collected from sports lottery. Available from: http://www.sport.gov.cn.
  32. General Administration of Sport of China (2010). Statistical report on the sports sector of China. General Administration of Sport of China. Available from: http://www.sport.gov.cn/n16/index.html.
  33. Griffiths, M. D. (1991). Amusement machine playing in childhood and adolescence: A comparative analysis of video games and fruit machines. Journal of Adolescence, 14, 53–73.PubMedCrossRefGoogle Scholar
  34. Griffiths, M. D. (1993). Factors in problem adolescent fruit machine gambling. Journal of Gambling Studies, 9, 31–45.CrossRefGoogle Scholar
  35. Griffiths, M. (2003). Internet gambling: Issues, concerns, and recommendations. CyberPsychology and Behavior, 6(6), 557–568.PubMedCrossRefGoogle Scholar
  36. Griffiths, M. D., & Parke, J. (2002). The social impact of Internet gambling. Social Science Computer Review, 20(3), 312–320.Google Scholar
  37. Griffiths, M., & Wood, R. T. A. (2000). Risk factors in adolescence: The case of gambling, videogame playing, and the Internet. Journal of Gambling Studies, 16(2–3), 199–225.PubMedCrossRefGoogle Scholar
  38. Gupta, R., & Derevensky, J. (1997). Familial and social influences on juvenile gambling behavior. Journal of Gambling Studies, 13(3), 179–192.PubMedCrossRefGoogle Scholar
  39. Henze, N., & Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in Statistics Theory and Methods, 19(10), 3595–3617.CrossRefGoogle Scholar
  40. Hong Kong Jockey Club (2010). Annual Report for the year ended 30 June 2010. Available from: http://www.hkjc.com/home/english/index.asp.
  41. Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
  42. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.CrossRefGoogle Scholar
  43. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford.Google Scholar
  44. Koning, R. H., & Velzen, B. V. (2009). Betting exchanges: The future of sports betting? International Journal of Sport Finance, 4(1), 42–62.Google Scholar
  45. Ladouceur, R., Gaboury, A., Bujold, A., Lachance, N., & Tremblay, S. (1991). Ecological validity of laboratory studies of video poker gambling. Journal of Gambling Studies, 7, 109–116.CrossRefGoogle Scholar
  46. Ladouceur, R., Sylvain, C., Boutin, C., & Doucet, C. (2002). Understanding and treating pathological gamblers. London: Wiley.Google Scholar
  47. Ladouceur, R., & Walker, M. (1996). A cognitive perspective on gambling. In P. M. Salkovskis (Ed.), Trends in cognitive and behavioural therapies (pp. 89–120). New York: Wiley.Google Scholar
  48. LaFleur, T., & LaFleur, B. (2010). LaFleur’s 2010 world lottery almanac. Boyds: TLF.Google Scholar
  49. Lattin, J., Carroll, J., & Green, P. (2003). Analyzing multivariate data. Belmont: Thomson Brooks/Cole.Google Scholar
  50. Lesieur, H. R. (1987). Current research into pathological gambling and gaps in the literature. In T. Galski (Ed.), Handbook of psychological gambling (pp. 225–248). Springfield, IL: Charles C. Thomas.Google Scholar
  51. Lesieur, H. R. (1988). Altering the DSM-III criteria for pathological gambling. Journal of Gambling Behavior, 4, 38–47.CrossRefGoogle Scholar
  52. Lesieur, H. R., & Rosenthal, R. J. (1991). Pathological gambling: A review of the literature. Journal of Gambling Studies, 7(1), 5–37.CrossRefGoogle Scholar
  53. 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.PubMedGoogle Scholar
  54. Loo, J., Raylu, N., & Oei, T. (2008). Gambling among the Chinese: A comprehensive review. Clinical Psychology Review, 28, 1152–1166.PubMedCrossRefGoogle Scholar
  55. Mardia, K. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519.CrossRefGoogle Scholar
  56. Mardia, K. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhy The Indian Journal of Statistics Series B, 36(2), 115–128.Google Scholar
  57. Moore, Thomas L. (2002). The etiology of pathological gambling. Oregon Gambling Addiction Treatment Foundation, December, http://www.gamblingaddiction.org/.
  58. Murray, J. (1993). Review of research on pathological gambling. Psychological Reports, 72, 791–810.PubMedCrossRefGoogle Scholar
  59. Muthen, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38(2), 171–189.CrossRefGoogle Scholar
  60. Neal, P., Delfabbro, P., & O’Neil, M. (2004). Problem gambling and harm: A national definition. Adelaide, Australia: South Australian Centre for Economic Studies.Google Scholar
  61. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.Google Scholar
  62. Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results from the national epidemiologic survey on alcohol and related conditions. The Journal of Clinical Psychiatry, 66(5), 564–574.PubMedCrossRefGoogle Scholar
  63. Pietrzak, R. H., & Petry, N. M. (2005). Antisocial personality disorder is associated with increased severity of gambling, medical, drug and psychiatric problems among treatment-seeking pathological gamblers. Addiction (Abingdon, England), 100(8), 1183–1193.CrossRefGoogle Scholar
  64. Raylu, N., & Oei, T. P. (2002). Pathological gambling: A comprehensive review. Clinical Psychology Review, 22(7), 1009–1061.PubMedCrossRefGoogle Scholar
  65. Plunkett Research. (2010). Sports industry trends and statistics. Rockville, MD: Market Research.Google Scholar
  66. Nieminen, R. (2006). Sports betting and social responsibility. European State Lotteries and Toto Association., 22, 6.Google Scholar
  67. Rosenthal, R. J. (1989). Pathological gambling and problem gambling: Problems of definition and diagnosis. In H. Shaffer (Ed.), Compulsive gambling: Theory, research and practice (pp. 101–125). Lexington, MA: Lexington.Google Scholar
  68. 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
  69. Shaffer, H. J., Hall, B. A., & Vander, B. J. (1999). Estimating the prevalence of disordered gambling behavior in the United States and Canada: A research synthesis. American Journal of Public Health, 89, 1369–1376.PubMedCrossRefGoogle Scholar
  70. Shaffer, H. J., & Korn, D. A. (2002). Gambling and related mental disorders: A public health analysis. Annual Review of Public Health, 23, 171–212.PubMedCrossRefGoogle Scholar
  71. Smeaton, M., & Griffiths, M. D. (2004). Internet gambling and social responsibility: An exploratory study. Cyber Psychology and Behaviour, 7, 49–58.CrossRefGoogle Scholar
  72. Steiger, J. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87(2), 245–251.CrossRefGoogle Scholar
  73. Steiger, J., & Lind, J. (1980). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City.Google Scholar
  74. Stinchfield, R. (2003). Reliability, validity, and classification accuracy of a measure of DSM-IV diagnostic criteria for pathological gambling. The American Journal of Psychiatry, 160(1), 180–182.PubMedCrossRefGoogle Scholar
  75. Stinchfield, R., Govoni, R., & Frisch, G. R. (2004). Screening and assessment instruments. In J. E. Grant & M. N. Potenza (Eds.), Pathological gambling: A clinical guide to treatment (pp. 207–231). Washington, DC: American Psychiatric Association.Google Scholar
  76. 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(1), 73–82.PubMedCrossRefGoogle Scholar
  77. Svettieva, E., & Walker, M. (2008). Inconsistency between concept and measurement: the Canadian Problem Gambling Index (CPGI). Journal of Gambling Issues, 22.Google Scholar
  78. Sylvain, C., Ladouceur, R., & Boisvert, J. -M. (1997). Cognitive and behavioral treatment of pathological gambling: a controlled study. Journal of Consulting and Clinical Psychology, 65, 727–732.PubMedCrossRefGoogle Scholar
  79. Tang, C., Wu, A., & Tang, J. (2007). Gender differences in characteristics of Chinese treatment-seeking problem gamblers. Journal of Gambling Studies, 23, 145–156.PubMedCrossRefGoogle Scholar
  80. Toneatto, T., Blitz-Miller, T., Calderwood, K., Dragonetti, R., & Tsanos, A. (1997). Cognitive distortions in heavy gambling. Journal of Gambling Studies, 13(3), 253–266.PubMedCrossRefGoogle Scholar
  81. Tucker, L., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10.CrossRefGoogle Scholar
  82. Volberg, R. A., & Abbott, M. W. (1994). Lifetime prevalence estimates of pathological gambling in New Zealand. International Journal of Epidemiology, 23, 976–983.PubMedCrossRefGoogle Scholar
  83. Volberg, R. A., Abbott, M. W., Roennberg, S., & Munck, I. (2001). Prevalence and risks of pathological gambling in Sweden. Acta Psychiatrica Scandinavica, 104(4), 250–256.PubMedCrossRefGoogle Scholar
  84. Volberg, R. A., & Banks, S. M. (1990). A review of two measures of pathological gambling in the United States. Journal of Gambling Behavior, 6(2), 153–163.CrossRefGoogle Scholar
  85. Volberg, R. A., & Steadman, H. J. (1992). Accurately depicting pathological gamblers: Policy and treatment implications. Journal of Gambling Studies, 8(4), 401–412.CrossRefGoogle Scholar
  86. Volberg, R. A., & Young, M. M. (2008). Using the South Oaks Gambling Screen vs. the Canadian Problem Gambling Index in problem gambling screening and assessment. Guelph: The Ontario Problem Gambling Research Centre.Google Scholar
  87. Walker, M. B. (1992). Irrational thinking among slot machine players. Journal of Gambling Studies, 8(3), 245–261.CrossRefGoogle Scholar
  88. Walker, M. (1996). The medicalisation of gambling as an “addiction”. In J. McMillen (Ed.), Gambling cultures: Studies in history and interpretation (pp. 223–242). New York: Routledge.Google Scholar
  89. Wang, X. (2008). Policy alternatives for the development of China’s gaming industry. Beijing: China Financial and Economic.Google Scholar
  90. Westermeyer, J., Canive, J., Garrard, J., Thuras, P., & Thompson, J. (2005). Lifetime prevalence of pathological gambling among American Indian and Hispanic American Veterans. American Journal of Public Health, 95(5), 860–866.PubMedCrossRefGoogle Scholar
  91. Williams, R. J., West, B. L., & Simpson, R. I. (2007). Prevention of problem gambling: A comprehensive review of the evidence. Guelph, Ontario, Canada: Report Prepared for the Ontario Problem Gambling Research Centre.Google Scholar
  92. Wolohan, J. T. (2009). Sports Betting in the United States. International Sports Law Review Pandektis, 3(4), 124–126.Google Scholar
  93. Wong, I. L., & So, E. M. (2003). Prevalence estimates of problem and pathological gambling in Hong Kong. American Journal of Psychiatry, 160, 1353–1354.PubMedCrossRefGoogle Scholar
  94. Yates, A. (1987). Multivariate exploratory data analysis: A perspective on exploratory factor analysis. New York: State University of New York.Google Scholar
  95. Yu, C. Y., & Muthen, B. (2002). Evaluation of model fit indices for latent variable models with categorical and continuous outcomes (Technical Report). Los Angeles: University of California at Los Angeles, Graduate School of Education and Information Studies.Google Scholar
  96. Zhang, J. J., & Cianfrone, B. A. (2011). Sport coaching and management. In A. Lee & G. Reeves (Eds.), Introduction to physical education, exercise science, sport and recreation. Beijing, China: Higher Education.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Hai Li
    • 1
    • 2
  • Luke Lunhua Mao
    • 2
    • 3
  • James J. Zhang
    • 2
    • 3
  • Yin Wu
    • 1
  • Anmin Li
    • 4
  • Jing Chen
    • 5
  1. 1.School of Sport Economics and Management, Sport Events Research CenterShanghai University of SportShanghaiPeople’s Republic of China
  2. 2.Department of Tourism, Recreation and Sport Management, College of Health and Human PerformanceUniversity of FloridaGainesvilleUSA
  3. 3.Sport Event Research CenterShanghai University of SportShanghaiChina
  4. 4.School of KinesiologyShanghai University of SportShanghaiChina
  5. 5.Physical Education DepartmentShanghai University of Finance and EconomicsShanghaiChina

Personalised recommendations