#90 A Critical Appraisal of Online Poker Gambling Among University Students: Risky. Olivia Fox. Newcastle University. Mathematics and Psychology

Transcription

1 #90 A Critical Appraisal of Online Poker Gambling Among University Students: Risky Endeavour or Harmless Pastime? Olivia Fox Newcastle University Mathematics and Psychology (I have not studied Statistics in Psychology) Date: 12 December 2015 I (named above) declare that this assignment is my own work and that I have correctly acknowledged the work of others. This assignment is in accordance with University and School guidance on good academic conduct (and how to avoid plagiarism and other assessment irregularities). I give permission to have my work submitted to an electronic plagiarism checker, which will permanently store my work in a central database against which future work can be checked.

2 Overview Gambling is increasingly popular amongst university students [1] with the number of online gambling sites increasing due to it being more advertised and accessible. Therefore, it is no surprise that significantly more people entered in to the World Series of Poker Main Event in 2006 [2]. This rise in online gambling, especially amongst year olds [3], has caused concern and led to investigative study in this area. For instance, online poker playing amongst students in the UK was researched including the predictors of problem gambling. 18% of the participants fit the criteria of problem gambling [4]. Contributing to this research, the authors aimed to describe online poker gambling patterns and associated problems. They hypothesized that compared with offline poker gamblers, online poker gamblers will be more likely to (1) exhibit excessive gameplay patterns with higher spending and debt, gamble more frequently and bet larger sums of money, (2) be characterized by higher rates of pathological gambling and greater involvement in other addictive behaviours. A sample of 366 participants of university students were given a questionnaire to complete. It assessed gambling patterns, including type of gambling activity and frequency of play, associated problems, such as gambling problem severity and alcohol or drug use. Multinomial and binomial logistic regression analysed the findings. The results showed that 19.5% of participants gambled on poker in the previous year and about one fifth of those played poker online. Those who used both methods, played online more frequently and gender and culture biases were found. It was found to be associated with problem gambling severity, alcohol dependence or drug use, and reported problems in their studies, finances and in their families due to gambling. These results support the hypotheses. Critique The hypotheses of this study built on previous research in the field aimed at preventing problem gambling in university students. The University Student Gambling Habit Survey 2008 [5] targeted 6000 full time university students, evenly distributed between French and English speaking universities in Montreal. The final sample of 366 participants who had been poker gamblers in the past year had an average age of

3 22.6 years. The age of the participants resonates with a focus of research into young adults tendency to take risks [6]. This is especially a concern in the student population because of their accessibility to internet gambling sites. Compared to non-students, undergraduate students may be more likely to take financial risks because they may see the money as belonging to somebody else in the form of loans or grants. However, risk taking behaviour is also prominent in adolescents and gambling is accessible to those younger than 18; lottery tickets are legally available to buy for 16 year olds in the UK and it is easier to lie about age online. Research has shown that 2% of year olds in Britain experience gambling problems [7]. Although the sample is representative of the bilingual divide in Montreal, culture was not investigated and this study lacks external validity. Once the sample of students was obtained, the participants were sent a questionnaire that could be returned in paper form or completed online. This option may increase response rates and is suitable when addressing socially threatening items. However, this may have limited the validity of the sample as only a certain type of person may complete a survey mailed to them. The response suggested that two thirds of the respondents preferred online activity which may be a bias in the final sample. The type and amount of gambling was measured by an adaptation of the Canadian Problem Gambling Index [8]. Participants were asked about frequency of betting or spending money in the previous 12 months for 10 gambling activities. It is unclear how these activities were deciphered and they may have been difficult to differentiate between these when answering with yes or no. These binary options limit the response compared to a Likert scale with a numerical value attached to how much the statement applies to the participant. The frequency of gambling was measured in a number of locations which can again be difficult to answer as some of these locations may have an overlay, such as in a bar on campus. Gambling problem severity was assessed using the Problem Gambling Severity Index [8]. Nine items assessing loss of control with respect to spending and gambling-related problems were used with a Likert scale. Participants were also assessed on perceived problems in relationships with family members and friends, studies, and finances. These measures are all self-reported and so the responses may not be accurate, especially with regards to relationships with others. A problem gambler may not acknowledge having such problems due to gambling. Drinking patterns were assessed with a Likert scale with regards to frequency of alcohol use in the past year and month, and illicit drug use was assessed over lifetime and in the past year. The Alcohol Use Disorders

4 Identification Test [9] was used to determine potential alcohol dependence. This self-report measure relies on the honesty and accuracy of the participant. Social desirability bias applies as it is unlikely that respondents will admit to high alcohol consumption or may underestimate their intake. It may also be difficult to express how frequently alcohol is consumed when it may differ from week to week. This is especially relevant with students who may drink more in a university setting compared to a non-student or different ages. This has been supported by various research [10]. The results showed that 19.9% of gamblers used the internet to play poker. Even though 95.9% of these gambled offline too, they gambled online more frequently. Online poker gambling was significantly higher for men and those born outside Canada. This gender bias is supported by research illustrating that sexes differ in risk-taking of financial decisions [11]. The difference between Canada born participants and those not born in Canada can be explained by finding relief in gambling due to a feeling of not fitting in or having language difficulties. In support of this research shows that social exclusion leads to pursuing risks [12]. Online gamblers were found to be more likely to be frequent gamblers, engage in more gambling activities, report higher monthly spending on poker and higher annual debt as a result of poker gambling. Logistic regressions [13] showed an association between online gambling and the severity of gambling problems. Patterns for on- and off-line gambling can be explained by overconfidence, which is particularly prominent when accurate judgements are hard to make (as in gambling) [14], attributing winning to skill [15] rather than chance. Loss aversion retains the players interest, as people hate losing twice as much as they love gaining [16], and the gambler s fallacy [17] means players continues to gamble with the expectation of winning, even if previously losing. These effects are more prominent in online gamblers due to the ease of frequent playing, using virtual money, and being anonymous, especially as social facilitation [18] could affect risktaking behaviour. Online poker gamblers were more likely than offline gamblers to be dependent on alcohol and were more likely to report illicit drug use over the past year. Smaller immediate gains are preferred which increases the risk of addiction, such as gambling, alcohol or drug abuse [19]. This is not surprising considering gamblers tend to be more risk-taking and exhibit risky behaviour such as alcohol and drug consumption. However, the participants may not have a perception of risk [20] of these factors which could have been attributed to their self-reported responses.

5 Further Research Gambling activity is changing as technology develops and makes internet platforms more accessible to younger people. Therefore, further research should be conducted into the effects of online gambling compared to offline gambling since the introduction of mobile applications, comparing the on- and offline gambling patterns for many activities in the UK to cultivate the findings in this study. For instance, comparing offline gamblers to computer and application gamblers to a control group to assess if this medium has contributed to increases in gambling. Decision making can depend on the format. For instance, people are more risk averse when they perceive a substantial chance at a large gain, as with poker (certainty effect) but are more risk seeking when there is a small chance at a large gain, as with the lottery (possibility effect) [21]. The availability heuristic [22] may also be a factor, for instance advertisements of Bingo or the lottery advertising winners will increase the probability of the winning to a player. Thus an investigation into use of these gambling activities should be replicated in the UK. To address gambling by younger adults a random sample derived from sixth form/colleges as well as university students would be used. The study looks at university students but they are a certain type of person and so could be compared to people who do not attend university, and may still be at risk of problematic gambling and associated behaviours. The study of a younger sample could lead to preventative interventions to minimise problem behaviour. Additionally, a longitudinal design (such as every 10 years, starting at 15) rather than cross sectional can clarify if the behaviour is part of a transitional developmental stage of identity exploration [23] or if the behaviour is sustained. To enhance this study, I would use Gamcare s self-assessment [24] survey to investigate time spent on gambling, stakes, finances, and relationships. Furthermore, the inclusion of non-related questions would conceal the aim of the study more and reduce bias. Along with the explicit self-report measures, indirect measure can be used to assess the participants risk-proneness as a factor of gambling patterns and subsequent problematic behaviour. An Implicit Association Test [25] would be administered and the reaction times analysed, which could also be applied to assessing alcohol consumption and drug use. Alternatively, risk taking could be measured by Objective Personality Tests. [25]

6 These analyses could add to the research conducted in this study with further information about online gambling sites increasing with popularity, and a design that gives more validity and reduces bias. If this corresponded with the results and discussion of this study, it would support these findings and would be more applicable to the UK.

7 References 1. Petry, N.M., & Weinstock, J., (2007). Internet Gambling Is Common in College Students and Associated with Poor Mental Health. The American Journal on Addictions. 16 (5), pp Dalla, N. (2009). WSOP history from Moss to Gold Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American psychologist, 55(5), Wood, R.T.A., Griffiths, M.D.,& Parke, J., (2007). Acquisition, Development, and Maintenance of Online Poker Playing in a Student Sample. CyberPsychology and Behaviour. 10 (3), pp ENHJEU 2008, Concordia University 6. (i) Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American psychologist, 55(5), 469; (ii) Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Flint, K. H., Hawkins, J.,... & Wechsler, H. (2012). Youth risk behavior surveillance-united States, 2011.Morbidity and mortality weekly report. Surveillance summaries (Washington, DC: 2002), 61(4), Gamble Aware (2013). Young People and Problem Gambling. [ONLINE] Available at: 8. Ferris, J., & Wynne, H. (2001). The Canadian problem gambling index. Ottawa, ON: Canadian Centre on Substance Abuse. 9. Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). The alcohol use disorders identification test. Guidelines for use in primary care, Gill, J. S. (2002). Reported levels of alcohol consumption and binge drinking within the UK undergraduate student population over the last 25 years. Alcohol and Alcoholism, 37(2), Powell, M., & Ansic, D. (1997). Gender differences in risk behaviour in financial decision-making: An experimental analysis. Journal of economic psychology,18(6), Duclos, R., Wan, E. W., & Jiang, Y. (2013). Show me the honey! Effects of social exclusion on financial risk-taking. Journal of Consumer Research, 40(1), Burns, R.P., & Burns, R., (2009). Business Research Methods and Statistics Using SPSS. 1st ed. England: SAGE Publications Ltd. 14. (i) Cox. M., (2014) The Psychology of Financial Decision Making, lecture 1: slides 23-4, lecture 9: slides (ii) Lichtenstein, S., & Fischhoff, B. (1977). Do those who know more also know more about how much they know?. Organizational Behavior and Human Performance, 20(2), Thaler, R.H., & Sunstein, C.R., (2008). Nudge. 2nd ed. Great Britain: Penguin Books Ltd. Pp (i) Xu, J., & Harvey, N. (2014). The hot hand fallacy and the gambler's fallacy: What are they and why do people believe in them?. (ii) Plous, S., (1993). The Psychology of Judgement and Decision Making. 1st ed. United States of America: McGraw-Hill. Pp (i) Plous, S., (1993). The Psychology of Judgement and Decision Making. 1st ed. United States of America: McGraw-Hill. Pp (ii) Zajonc, R. B. (1965). Social facilitation. Research Center for Group Dynamics, Institute for Social Research, University of Michigan. 18. Hariri, A. R., Brown, S. M., Williamson, D. E., Flory, J. D., de Wit, H., & Manuck, S. B. (2006). Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. The journal of Neuroscience, 26(51),

8 19. Plous, S., (1993). The Psychology of Judgement and Decision Making. 1st ed. United States of America: McGraw-Hill. Pp (i) Cox. M., (2014) The Psychology of Financial Decision Making, Lecture 3: slide , Lecture 6: (ii) Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), (iii) Plous, S., (1993). The Psychology of Judgement and Decision Making. 1st ed. United States of America: McGraw-Hill. Pp , Plous, S., (1993). The Psychology of Judgement and Decision Making. 1st ed. United States of America: McGraw-Hill. Pp Mihaylova, T., Kairouz, S., & Nadeau, L. (2012). Online Poker Gambling Among University Students: Risky Endeavour or Harmless Pastime?. Journal of Gambling Issues, GamCare Sustainable Interaction (i) Plous, S., (1993). The Psychology of Judgement and Decision Making. 1st ed. United States of America: McGraw-Hill. Pp (ii) Langer, E. J. (1975). The illusion of control. Journal of personality and social psychology, 32(2), Dislich, F. X., Zinkernagel, A., Ortner, T. M., & Schmitt, M. (2010). Convergence of direct, indirect, and objective risk-taking measures in gambling.zeitschrift für Psychologie/Journal of Psychology, 218(1),