1 Gainsbury et al. (in press) How risky is Internet gambling? How risky is Internet gambling? A comparison of subgroups of Internet gamblers based on problem gambling status Sally M. Gainsburyᵃᵇ, Alex Russellᵃᵇ, Robert Woodᶜ, Nerilee Hingᵃ, Alex Blaszczynskiᵃᵇ Gainsbury, S., Russell, A., Wood, R., Hing, N., & Blaszczynski, A. (in press). How risky is Internet gambling? A comparison of subgroups of Internet gamblers based on problem gambling status. New Media & Society. Published OnlineFirst Jan 15, 2014. doi:10.1177/1461444813518185
ᵃ Centre for Gambling Education & Research, Southern Cross University ᵇ School of Psychology, University of Sydney ᶜ University of Lethbridge Please address all correspondence to: Dr. Sally Gainsbury Centre for Gambling Education & Research, Southern Cross University P.O. Box 157, Lismore NSW 2480, Australia Email:
[email protected] Declaration of Conflicting Interests: The authors have no conflicts of interest to declare in relation to this manuscript. Funding acknowledgement: This work was supported by the Menzies Foundation [Allied Health Grant to the first author]. Abstract Internet gambling offers unique features that may facilitate the development or exacerbation of gambling disorders. Higher rates of disordered gambling have been found amongst Internet as compared to land-based gamblers; however little research has explored whether Internet disordered gamblers are a distinct subgroup. The current study compared problem with nonproblem and at-risk Internet gamblers to further understand why some Internet gamblers experience gambling-related harms. A sample of 2,799 Australian Internet gamblers completed an online survey. Problem gambling respondents were younger, less educated, had higher household debt, lost more money and gambled on a greater number of activities, and were more likely to use drugs while gambling as compared to non-problem and at-risk gamblers. Problem gamblers had more irrational beliefs about gambling, were more likely to believe the harms of gambling to outweigh the benefits, that gambling is morally wrong, and that all types of gambling should be illegal. For problem gamblers, Internet gambling poses unique problems related to electronic payment and constant availability leading to disrupted sleeping and eating patterns. However, a significant proportion of Internet problem gambling respondents also had problems related to terrestrial gambling, highlighting the importance of considering overall gambling involvement when examining subgroups of gamblers. Policy makers should carefully consider how features of Internet gambling contribute to gambling disorders requiring the implementation of evidence-based responsible gambling strategies. Keywords: Internet gambling, problem gambling, disordered gambling, risk factors, Australia, addiction, irrational beliefs, demographic characteristics
2 Gainsbury et al. (in press) How risky is Internet gambling? Introduction Internet gambling has changed the nature of gambling through sophisticated electronic technology offering convenient and constant global access to novel and interactive types of gambling. Internet gambling, synonymous with online, remote or interactive gambling, refers to all forms of wagering and gambling accessible through computer, mobile phone or wireless Internet connected devices. The global Internet gambling market was estimated to be worth US$28.32 billion in 2012 and forecasted to rise to US$49.64 billion by 2017 (GBGC, 2013). Regulated markets with local licenses represent an increasing revenue share with 40% accounted for by local licensed operators in 2012; an increase from 38% in 2011 (GBGC, 2013). This reflects an increasing trend for international jurisdictions to regulate the provision of Internet gambling, including consumer protection and harm minimisation strategies, in addition to economic incentives such as taxation (Gainsbury and Wood, 2011). The importance of implementing strong consumer protection policies is based on findings from several large-scale surveys indicating that Internet gamblers are more likely to be at risk of developing gambling disorders and harms compared to gamblers only using land-based forms (Gainsbury, Wood, Russell, Hing, and Blaszczynski 2012; Gainsbury, Russell, Hing, Wood, & Blaszczynski, in press; Griffiths, Wardle, Orford et al., 2009; Wood and Williams, 2007, 2010). Several features unique to Internet gambling potentially make this mode of access more problematic for players. Internet gambling is highly accessible and convenient, and allows continuous uninterrupted periods of playing multiple games with rapidly determined outcomes. Used in private settings, predominately at home (Gainsbury et al., 2012; Wood and Williams, 2011), Internet gambling is easier to conceal from others. The immersive nature of the Internet may also contribute to dissociative states, in which players may lose track of time and money spent, facilitating excessive gambling (Corney and Davis, 2010; Griffiths, 2003; Griffiths and Parke, 2002; Monaghan, 2009). Electronic payment systems may have a lower psychological value than cash (Corney and Davis, 2010; Griffiths, 2003) by ‘tokenizing’ money, and by reducing the time taken to deposit funds, redeem payouts, or receive loyalty benefits; described by Schull (2012) in reference to electronic gaming machines, as improved cash acquisition modalities. As suggested by Schull (2012), large scale complex tracking and data mining of consumer behaviour and expenditure triangulated to demographic data allow operators to effectively offer customized products targeting distinct player profiles. Several studies have compared non-Internet or terrestrial gamblers with Internet gamblers, including investigation of rates of problem gambling (Gainsbury et al., 2012; Gainsbury et al., in press a, c; Wardle Moody, Griffiths et al., 2011; Wood and Williams, 2011; Woolley, 2003). It has been argued that several elements of Internet gambling may lead to more problem gambling, such as the ability to play in isolation, the immersive nature of the Internet, the ability to wager large sums, and through the use of electronic funds and credit (Monaghan, 2009; Griffiths and Parke, 2002; Siemens and Kopp, 2011). However, there are relatively few studies examining subgroups of Internet gambling based on problem gambling severity, which are important to understand why some Internet gamblers may experience problems, while others do not. Given the potential for Internet gambling to lead to problems, this paper aims to examine differences and similarities between sub-groups of Internet gamblers at-risk and with and without gambling problems. The objective is to acquire a greater understanding of the specific features of Internet
3 Gainsbury et al. (in press) How risky is Internet gambling? gambling that may lead to harm and individual factors that may make players vulnerable to developing problems. Comparison of Internet and terrestrial gamblers Several studies have found that Internet gamblers are typified by individuals from higher socioeconomic strata, work full-time, have higher levels of education, and are more technologically savvy in comparison to land-based gamblers (Gainsbury et al., 2012; Gainsbury et al., in press b; Griffiths, et al., 2009; Wardle Moody, Griffiths et al., 2011; Wood and Williams, 2011; Woolley, 2003). Some research suggests that Internet compared to terrestrial gamblers are more likely to use alcohol and drugs (Griffiths et al., 2009; Wood and Williams, 2011), and to be relatively more involved by participating in multiple gambling forms and modes (Author 2012; Griffiths et al., 2009; Wardle et al., 2011; Wood and Williams, 2011). However, the characteristics of Internet gambling users appear to be changing over time. For example, a Swedish longitudinal study found that more recent adopters of Internet gambling were younger and had lower levels of education than those who started gambling online prior to 2008, and that the gender gap among Internet gamblers had decreased over time (Svensson and Romild, 2011). Newer Internet gamblers had a history of gambling on land-based forms, suggesting that gamblers migrated to online play, rather than adopting it in the first instance. These results suggest that Internet gamblers should not be considered as a homogeneous or static subpopulation. Risk factors for disordered Internet gambling Several analyses of actual online gambling expenditure indicate that the majority of Internet gamblers spend relatively moderate amounts, although a small proportion have high levels of expenditure and exhibit other risky patterns of play (Gainsbury et al., 2012; LaBrie et al, 2008; LaPlante et al., 2009; Nelson et al., 2008). The 2010 British Gambling Prevalence Survey revealed those who used the Internet for multiple types of gambling were more likely to be categorised as problem gamblers compared to Internet gamblers who engaged in fewer Internet gambling activities (Wardle et al., 2011). This is consistent with previous findings that versatility (or the number of gambling activities engaged in) and frequency of play are important predictors of gambling problems (Gray et al., 2012; Holtgraves, 2009; LaPlante et al., 2009). Moreover, the strength of the relationship between Internet and gambling problems is substantially diminished when controlling for such factors as frequency and versatility of gambling (Halme, 2011; LaPlante et al., 2009; Philander & MacKay, 2013; Vaughan Williams et al., 2008; Welte et al., 2004; Welte et al., 2009). A study of Australian problem gamblers found that compared to terrestrial gamblers, Internet gamblers were younger and engaged in a greater number of gambling activities, and these differences were significantly greater for problem than moderaterisk gamblers (Gainsbury et al., in press c). Taken together, these results indicate that Internet problem gamblers, as a group, are characterized by important distinctions from their nonproblem gambling counterparts. The current study As participation and expenditure for online gambling increase (Gainsbury and Wood, 2011; Wardle et al., 2011) it is important to understand risk factors associated with this activity. Theoretical models of gambling disorders have largely been developed based on previous research, which generally has not specifically considered Internet gamblers (Siemens and Kopp, 2011). Similarly, the majority of existing prevention, harm minimisation and treatment programs
4 Gainsbury et al. (in press) How risky is Internet gambling? do not cater purposefully for gamblers who have problems with, or are at risk of problems related to Internet gambling. This research attempts to determine how problem Internet gamblers differ from non-problem Internet gamblers and what identifiable risk factors may predict Internet gambling problems. The objective is to advance the conceptual understanding of problem gambling and guide harm reduction policies and treatment programs. Methods Procedure Two Australian universities human research ethics committees approved the research study. Recruitment advertisements inviting participants to complete an online survey were placed on a variety of websites between December 2010 and August 2011. These included websites relating to land-based gambling venues, Australian Internet wagering sites, and sites hosted by sporting organisations, government offices and help-providers. Paid advertisements were also placed on Google and Facebook. The majority of respondents indicated that they completed the survey based on advertisements from participating websites (72.9%) with only a minority recruited from Facebook (10.0%), Google (4.2%), or other via other sources. Individuals who clicked on these advertisements were directed to the survey home page which outlined the inclusion criteria, informed consent, the purpose of the survey, and voluntary nature of participation. This online survey was adapted from an instrument previously used by Wood and Williams (2010); however, independent researchers have not been validated the measure’s psychometric properties. The survey contained several sections: 1. Gambling Behavior Scale. This scale measured the frequency of participation over the past 12 months (seven options ranging from ‘4 or more times a week’ to ‘not at all in the past 12 months’) in ten different gambling (for money) activities, including: instant win scratch tickets, lottery tickets, keno, wagering on sporting events, wagering on dog or horse races, bingo, games of skill, poker (against individuals), electronic gaming machines (EGMs), and casino table games. Those who had participated at least once in each activity provided further information about their average monthly gambling expenditure (defined as ‘how much you are ahead or behind, or your net win (+) or loss () in an average MONTH in the past 12 months’) as well as the proportion of each gambling activity they conducted online. Participants were also asked about their alcohol and drug consumption while gambling, with five response options ranging from ‘never’ to ‘always’. 2. Internet Gambling Questions. Participants completed 13 questions, including the primary location where they gamble online (home, work, or in transit), the year they first gambled online, the time of day they typically gamble online, and their preferred medium for online gambling. Participants were asked to nominate, from a designated list, the factors that motivated them to choose particular Internet gambling sites over others, and the advantages and disadvantages of Internet gambling as compared to land-based play. Participants were asked if their gambling had ever caused significant problems, if it had impacted their sleep or eating patterns, and whether the use of electronic money (e.g., credit cards or Internet bank transfers) had impacted their gambling expenditure. 3. Gambling Attitudes. Three questions assessed attitudes to gambling, specifically: perceived benefit or harm of gambling, whether gambling is morally wrong and whether gambling should be legalised.
5 Gainsbury et al. (in press) How risky is Internet gambling? 4. Gambling Knowledge and Beliefs Test. Ten multiple choice items were designed to assess aspects of commonly held gambling fallacies, including independence of randomly determined events, personal luck, winning and losing streaks, probabilities of winning, and outcomes of prolonged gambling sessions. For example, questions included whether the probability of winning was different based on the numbers picked for a lottery draw or whether an EGM had recently paid out. Participants received a score of +1 for each correct response, for a total score of 0 to 10 (high scores reflecting greater resistance to gambling fallacies). 5. Problem Gambling Severity Index (Ferris & Wynne, 2001). Problem gambling status was measured using the nine-item Problem Gambling Severity Index (PGSI) of the Canadian Problem Gambling Index (CPGI). Nine questions assessed the extent of gambling-related harm experienced over the previous 12 months, with response options of ‘never’, ‘sometimes’, ‘most of the time’, and ‘almost always’. The scoring procedure and classification of participants followed the instruction described by Ferris and Wynne (2001); total score of zero indicated a no-risk gambler, 1-2 = a low risk gambler, 3-7 = moderate-risk and 7+ a possible problem gambler. The PGSI has been independently validated, and results indicate that it has excellent reliability, dimensionality, external/criterion validation, item variability, practicality, applicability, and comparability (McMillen and Wenzel, 2006; Neal, Delfabbro, and O’Neill, 2004). 6. Demographics. Standard questions assessed gender, age, marital status, education, employment, household income, and household debt. Analyses Internet gamblers were defined as those who indicated that they had gambled online at least once in the past 12 months. Although somewhat broad, this definition was considered appropriate for this study given that even infrequent Internet gambling may have an impact on the development of harm. Furthermore, this definition is consistent with previous studies (Griffiths et al., 2009; Ladd and Petry, 2002; Olason et al., 2011; Wood and Williams, 2011), and is similar to the definition of gambling used in most prevalence studies, thereby enabling inter-study comparison of results. Independent samples t-tests were employed for continuous dependent variables. Chi-square tests were used for all other dependent variables, including examining standardized residuals for dependent variables with more than two levels, using a critical standardized residual of 2. A logistic regression was conducted to determine the characteristics that differentiate Internet problem gamblers from Internet non-problem gamblers. A total of 14 predictor variables were used: gender, state of residence, employment status, marital status, household income, alcohol use, illicit drug use, level of education, household debt, age, gambling expenditure, number of different gambling behaviours, gambling attitudes and gambling knowledge/beliefs. All categorical variables were dummy coded using the following reference groups: gender (female), state (NSW), employment status (full-time employment), marital status (married) and education level (less than high school). All continuous variables were checked for skewness, corrected with the following transformations: PGSI score (log transformation), household debt (square root transformation) and number of gambling behaviours (square root transformation). The skewness of gambling
6 Gainsbury et al. (in press) How risky is Internet gambling? expenditure, gambling attitude and gambling knowledge/beliefs could not be corrected. Gambling expenditure was particularly leptokurtic, which was reduced by winsoring the extreme 1% of values (114 values in total) and the final result for all values was divided by 1,000 so that expenditure was measured in thousands of dollars. There were no issues with the variables measuring age or household income. Due to the large sample size, an alpha of 0.001 was used and effect sizes are reported for all ttests and chi-square analyses. For t-tests, Cohen’s d is reported and, using Cohen’s guidelines (Cohen, 1992), 0.2 indicates a small effect, 0.5 a medium effect and 0.8 a large effect. For chisquare, the (phi) coefficient was used, where values between -0.3 and 0.3 may be treated as trivial associations. However, all results that are statistically significant at alpha = 0.001 with a phi coefficient between -0.3 and 0.3 are reported, but should be interpreted with caution. Because a conservative alpha level was used for all analyses, no further type I error controls were used. All analyses were conducted using SPSS v18.0.3 on an Apple Intel MacBook Pro. Results Participants Of the 4,680 Internet gamblers that started the survey, 2,799 provided responses to the PGSI, giving a completion rate of 59.8%. Of these, 2,344 (83.7%) were identified as being below the threshold for possible problem gambling (retaining the original nomenclature for the scale), which included non-problem gamblers (27.8%), low-risk gamblers (27.1%), and moderate-risk gamblers (28.9%). For the analyses presented in this paper, this group will be referred to as nonproblem and at-risk gamblers (NP/ARGs) The remaining 455 were identified as possible problem gamblers (16.3%), referred to as problem gamblers (PGs) in the remainder of the current paper. Demographics PGs were, on average, younger (M = 39.2, SD = 13.1) than NP/ARGs (M = 47.4, SD = 14.4), t(684.4) = 12.05, p < 0.001, d = 0.60 (Table 1). PGs and NP/ARGs also differed in terms of relationship status, 2 (4, N=2,787) = 73.86, p < 0.001, = 0.1, education level, 2 (8, N=2,791) = 28.55, p < 0.001, = 0.10, and employment status, 2 (6, N=2,791) = 71.0, p < 0.001, = 0.16. From an inspection of standardised residuals, PGs are more likely to have never married, have less formal education, and be unemployed or a student in comparison to NP/ARGs.
7 Gainsbury et al. (in press) How risky is Internet gambling? Table 1. Demographic profile of non-problem and at-risk gamblers and problem gamblers Non-problem & at-risk gamblers (N=2,344)
Problem gamblers (N=455)
Gender Male Female ns Age bracket Under 18 18-19 20-29 30-39 40-49 50-59 60-69 70-79 80 or older
93.0% 7.0%
93.2% 6.8%
0.3% 0.8% 13.3% 16.4% 23.0% 23.8%* 17.4%* 4.6%* 0.7%
0.0% 1.8%* 26.2%* 25.9%* 23.5% 15.2% 6.6% 0.7% 0.2%
Marital status Married Living with partner Widowed Divorced or separated Never married
52.0%* 16.5% 1.6% 9.1% 20.7%
31.7% 21.4%* 1.3% 10.1% 35.5%*
60.1% 8.5% 1.6% 16.9%* 1.1% 3.2% 8.6%*
65.8%* 10.6% 5.6%* 5.8% 1.1% 5.6%* 5.4%
0.0% 0.0% 0.7% 11.0% 23.3% 11.2% 23.8%* 18.9%* 11.1%
0.4% 0.2% 1.3% 16.5%* 25.3% 12.8% 19.4% 15.0% 9.0%
2 (8, N = 2,799) = 122.61, p < 0.001, = 0.21
2 (4, N = 2,787) = 73.86, p < 0.001, = 0.16 Current employment status Employed full-time Employed part-time Unemployed and seeking work Retired Homemaker Full-time student Sick leave, maternity leave, on strike, on disability OR other
2 (6, N = 2,729) = 71.03, p < 0.001, = 0.16 Highest completed education level No school Some primary school Completed primary school Some high school Completed high school Some technical school, college or university Completed technical school/TAFE/diploma trade certification Completed undergraduate university degree Professional degree (law, medicine, dentistry), MSc, PhD
2 (8, N = 2,791) = 28.55, p < 0.001, = 0.10 Note: Asterices indicate significant differences between the groups. Gambling behaviour The difference between the average monthly expenditure over all forms of gambling was not statistically significantly different between problem gambling groups due to the large amount of variance in the obtained estimates. However, when the extreme 1% of values were trimmed from
8 Gainsbury et al. (in press) How risky is Internet gambling? the data, NP/ARGs were found to report being ahead by an average of AUD$437.76 per month as compared to a reported average monthly loss of AUD$895.80 for PGs, t(601.12) = 5.96, p < 0.001, d = 0.67. PGs engaged in a significantly lower proportion of sports betting online via computer (64.9%), compared to NP/ARGs (77.3%), t(377.36) = 6.40, p < 0.001, d = 0.43 (Table 2). The same was found for Internet race wagering, where a significantly lower proportion of betting for PGs (59.5%) occurred online, compared to 75.1% for NP/ARGs, t(420.25) = 8.21, p < 0.001, d = 0.51. Correspondingly, PGs conducted a significantly higher proportion of their race wagering in terrestrial agencies (38.0%), compared to 29.6% for NP/ARGs, t(403.82) = 4.35, p < 0.001, d = 0.29. No other statistically significant differences were found relating to gambling activities. Table 2 – Mean (and SD) of reported percentage of betting via each medium for non-problem and at-risk gamblers and problem gamblers
Sports betting Land-based agencies Internet via computer Internet via mobile or wireless device Telephone Interactive TV Horse and dog racing Land-based agencies Internet via computer Internet via mobile or wireless device Telephone Interactive TV
Non-problem & atrisk gamblers (N=2,344)
Problem gamblers (N=455)
26.1% (28.4%) 77.3% (27.2%) 20.5% (27.3%) 10.0% (16.7%) 0.4% (1.6%)
33.2% (29.1%) 64.9% (29.8%) 21.8% (27.0%) 10.4% (11.9%) 1.3% (5.4%)
29.6% (28.9%) 75.1% (29.0%) 21.6% (29.9%) 11.0% (18.3%) 2.0% (10.7%)
38.0% (29.3%) 59.5% (31.8%) 24.2% (29.5%) 13.8% (17.2%) 2.1% (7.8%)
The proportion of PGs who used alcohol while gambling (78.4%) was not significantly different to NP/ARGs (74.3%). However, 15.5% of PGs reported at least some drug use while gambling, compared to 8.6% for NP/ARGs, 2 (1, N=2,742) = 20.43, p < 0.001, = 0.09. Impact of Internet gambling on gambling problems Of all participants, 19% indicated that their gambling had caused them significant problems. This included 74.2% of the subset classified as PGs by the PGSI, in addition to a proportion of participants whose PGSI scores fell below this threshold but who self-reported at least some gambling-related problems. The majority of those reporting gambling problems (61.5%) stated that problems occurred after first gambling online. The use of electronic funds was reported to increase the amount spent by a greater proportion of PGs (53.5%), compared to NP/ARGs (11.8%), 2 (2, N=2,720) = 463.53, p < 0.001, = 0.41. PGs were also more likely to gamble online between midnight and 6am (3.3%) than NP/ARGs (1.5%), although the majority of both groups (50.9% vs. 61.0%) still
9 Gainsbury et al. (in press) How risky is Internet gambling? gambled between 12pm and 6pm. The disruptive effects of online gambling were reflected in the finding that 47.9% of PGs reported sleep patterns being affected, with only 8.6% of NP/ARGs reporting similar disturbances (1, N=2,743) = 456.98, p < 0.001, = 0.40. Similarly, 33.5% of PGs reported disruptions to eating patterns, compared to only 3.9% of NP/ARGs, 2 (1, N=2,720) = 416.75, p < 0.001, = 0.39. With respect to the selection of Internet gambling sites, significantly more PGs (18.7%) reported that they were influenced by incentives provided by online gambling sites, compared to NP/ARGs (11.8%), 2 (1, N=2,799) = 16.18, p < 0.001, = 0.08, although the effect was small. When asked about the advantages of Internet gambling compared to gambling at land-based facilities, PGs were more likely to report a preference for 24 hour availability/convenience (66.4% vs. 50.6%), 2 (1, N=2,799) = 37.89, p < 0.001, = 0.12; greater privacy/anonymity (41.3% vs. 32.1%), 2 (1, N=2,799) = 14.43, p < 0.001, = 0.07; and better game experiences (20.9% vs. 12.8%), 2 (1, N=2,799) = 20.53, p < 0.001, = 0.09, as compared NP/ARGs. In terms of disadvantages, PGs were more likely to say that online gambling was too convenient (57.8% vs. 28.6%), 2 (1, N=2,799) = 145.88, p < 0.001, = 0.23; more addictive (38.0% vs. 11.8%), 2 (1, N=2,799) = 193.92, p < 0.001, = 0.26; and easier to spend more money (56.0% vs. 27.0%), 2 (1, N=2,799) = 148.87, p < 0.001, = 0.23. Gambling knowledge and attitudes PGs were significantly more likely to believe that the harm of gambling far outweighs the benefits and significantly less likely to say the benefits are about equal to the harms or somewhat outweigh the harm compared to NP/ARGs. A significantly higher proportion of PGs reported the belief that gambling is morally wrong (or were unsure) compared to NP/ARGs. Finally, a significantly higher proportion of PGs believed that all types of gambling should be illegal, while a significantly lower proportion of PGs believed that all types of gambling should be legal (see Table 3). Regarding the knowledge and beliefs test, PGs, on average, had significantly lower scores (M = 7.13, SD = 1.83) than NP/ARGs (M = 7.60, SD = 1.67), t(2791) = 5.34, p < 0.001, d = 0.26. Table 3 – Percentage of responses to questions about gambling attitudes for non-problem/at-risk gamblers and problem gamblers Non-problem & at-risk gamblers (N=2,332) Belief about the benefit or harm that gambling has for society The harm far outweighs the benefits 18.3% The harm somewhat outweighs the benefits 26.3% The benefits are about equal to the harm 34.6%* The benefits somewhat outweigh the harm 12.5%* The benefits far outweigh the harm 8.4%* 2 (4, N = 2,764) = 268.26, p < 0.001, = 0.31 Do you believe that gambling is morally wrong? Yes 2.3% No 94.9%*
Problem gamblers (N=454) 52.0%* 26.3% 14.8% 4.0% 2.9%
15.0%* 75.1%
10 Gainsbury et al. (in press) How risky is Internet gambling? Unsure/don’t know 2.9% 9.9%* (2, N = 2,785) = 204.92, p < 0.001, = 0.27 Which of the following best describes your opinion about legalized gambling? All types of gambling should be illegal 1.3% 8.1%* Some types of gambling should be legal and some should be illegal OR don’t 59.3% 62.1% know/unsure All types of gambling should be legal 39.4%* 29.7% 2 (2, N = 2,786) = 84.25, p < 0.001, = 0.17 Note: Asterices indicate significant differences between the groups. 2
Characteristics statistically differentiating problem gamblers from non-problem and atrisk gamblers A total of 2,493 respondents had completed all relevant questions and were thus included in the logistic regression. Of these, 2,079 were NP/ARGs and 414 were PGs. The lowest tolerance measured for any variable was 0.451 for one of the education dummy variables, suggesting no problems with multicollinearity. The test of the overall model, with 14 predictors, was statistically significant, 2 (34, N=2,493) = 541.17, p < 0.001 (Nagelkerke pseudo-R2 = 0.329) indicating that, altogether, these predictors reliably differentiate problem Internet gamblers from non-problem Internet gamblers. Overall prediction success was found to be 85.6%. The percentage of PGs correctly predicted by the model was only 26.8%, but for NP/ARGs the correctly predicted percentage was 97.4%. Table 4 outlines the predictor variables, including regression coefficients, Wald statistics, significance and odds ratio for each of the predictors, including dummy variables. Controlling for all other variables in the model, the significant predictors differentiating PGs from NP/ARGs were: those who had completed a technical school, college or diploma (compared to those who have not finished school), higher household debt, lower age, losing more money through gambling, higher number of gambling activities, were more likely to believe that gambling is more harmful than beneficial and more likely to believe that gambling is morally wrong.
11 Gainsbury et al. (in press) How risky is Internet gambling? Table 4 – Logistic regression of characteristics differentiating problem gamblers from nonproblem & at-risk gamblers Odds Predictor b S.E. (b) Wald Significance Ratio Gender -0.276 0.254 1.179 0.278 0.759 State (ref NSW) ACT 0.253 0.569 0.198 0.656 1.288 Victoria -0.438 0.202 4.701 0.030 0.645 Queensland 0.079 0.171 0.215 0.643 1.083 South Australia 0.269 0.237 1.287 0.257 1.309 Western Australia -0.067 0.407 0.027 0.869 0.935 Tasmania 0.195 0.522 0.140 0.708 1.215 Northern Territory 0.248 0.552 0.201 0.654 1.281 Employment Status (ref employed full time) Employed part-time 0.056 0.223 0.064 0.800 1.058 Unemployed and seeking work -0.554 0.339 2.664 0.103 0.575 Retired 0.311 0.291 1.145 0.285 1.365 Homemaker -0.267 0.554 0.233 0.629 0.766 Full-time student 0.483 0.312 2.406 0.121 1.621 Sick leave, other 0.304 0.269 1.283 0.257 1.355 Marital Status (ref married) Living with partner -0.398 0.181 4.829 0.028 0.671 Widowed -0.252 0.544 0.215 0.643 0.777 Divorced or separated -0.383 0.239 2.566 0.109 0.682 Never married -0.377 0.194 3.784 0.052 0.686 Household income -0.071 0.024 8.960 0.003 0.931 Alcohol Use 0.049 0.161 0.094 0.759 1.051 Drug Use -0.176 0.182 0.935 0.334 0.839 Education level Completed high school 0.526 0.204 6.638 0.010 1.691 Some technical school, college or 0.594 0.246 5.808 0.016 1.811 university Completed technical school, 0.781 0.211 13.722
