University-Recruited Students Compared to General Population Respondents
Demographics
As shown in Table
1, UR students differed significantly from the general population sample in terms of demographic characteristics. UR students were more likely to be female (56.6 vs. 13.7 %) and significantly younger (M = 21.10,
SD = 5.97) than the general population respondents (M = 45.00,
SD = 15.10),
t (1144.18) = 67.49,
p < 0.001,
d = 2.08. UR students were most likely to have never married (88.1 %) while general population respondents were most likely to be married (45.7 %). A similar proportion of UR students (11.6 %) and general population respondents (13.1 %) reported a household income of more than $150,000; however, UR students were most likely to report a household income of less than $20,000 (49.8 %), while incomes were relatively evenly distributed across available categories for the general population respondents (Table
1).
Table 1
Demographic profile of UR students, GP student and general population respondents
Gender |
Male | 43.4 | 76.7 | 86.3 |
Female | 56.6 | 23.3 | 13.7 |
Age bracket |
Under 20 | 56.6 | 22.4 | 2.4 |
20 to 24 | 32.5 | 50.9 | 8.4 |
25 or older | 10.9 | 26.7 | 89.2 |
Marital status |
Married | 3.0 | 5.2 | 45.7 |
Living with partner | 7.6 | 9.1 | 16.7 |
Widowed | 0.4 | 0 | 1.6 |
Divorced or separated | 0.9 | 3.9 | 10.2 |
Never married | 88.1 | 81.8 | 25.8 |
Income brackets |
Less than $20,000 | 49.8 | 6.1 | 9.4 |
Between $20,000 and $30,000 | 6.6 | 14.9 | 7.5 |
Between $30,000 and $30,000 | 4.8 | 9.6 | 7.5 |
Between $40,000 and $30,000 | 4.2 | 3.1 | 8.9 |
Between $50,000 and $30,000 | 3.1 | 3.5 | 8.9 |
Between $60,000 and $30,000 | 2.6 | 3.5 | 7.4 |
Between $70,000 and $30,000 | 4.8 | 3.1 | 7.3 |
Between $80,000 and $30,000 | 2.4 | 2.2 | 6.0 |
Between $90,000 and $30,000 | 3.3 | 2.2 | 7.0 |
Between $100,000 and $120,000 | 4.4 | 3.1 | 8.9 |
Between $120,000 and $150,000 | 2.4 | 2.2 | 8.0 |
More than $150,000 | 11.6 | 6.6 | 13.1 |
Gambling Activities
UR students gambled significantly less often than the general population respondents on instant win scratch tickets (
b = −0.27,
SE = 0.11, Wald = 6.09,
p = 0.014), bingo (
b = −0.50,
SE = 0.20, Wald = 6.23,
p = 0.013) and games of skill against others not including poker (
b = −0.80,
SE = 0.13, Wald = 36.35,
p < 0.001). UR students used the following gambling activities significantly more often than general population respondents: lottery tickets (
b = 0.35,
SE = 0.12, Wald = 8.79,
p = 0.003), sporting events (
b = 0.89,
SE = 0.12, Wald = 59.53,
p < 0.001), horse and dog racing (
b = 1.28,
SE = 0.12, Wald = 112.15,
p < 0.001) and playing table games at the casino (
b = 0.40,
SE = 0.15, Wald = 9.88,
p = 0.002). There was no significant difference between the groups in terms of playing poker, using EGMs or Internet casino usage (Table
2a, b).
Table 2
Percentage of UR students and general population respondents who engaged in each gambling activity
(a) |
Not at all in the past 12 months (%) | 41.5 | 55.4 | 64.2 | 38.7 | 56.9 | 37.8 | 66.6 | 17.0 | 83.6 | 95.9 |
Less than once a month (%) | 38.0 | 28.7 | 22.7 | 26.1 | 24.4 | 15.4 | 24.1 | 10.0 | 11.8 | 2.4 |
Once a month (%) | 10.7 | 6.1 | 6.4 | 9.0 | 7.9 | 6.2 | 3.3 | 4.5 | 3.3 | 0.5 |
2–3 times a mont (%) | 4.1 | 4.3 | 2.2 | 6.5 | 4.4 | 7.9 | 1.8 | 7.5 | 0.4 | 0.2 |
Once a week (%) | 3.7 | 3.2 | 2.2 | 12.7 | 3.3 | 10.0 | 1.3 | 15.0 | 0.7 | 0.4 |
2–3 times a week (%) | 1.7 | 1.2 | 1.8 | 4.7 | 1.8 | 9.4 | 1.5 | 17.0 | 0.2 | 0.2 |
4 or more times a week (%) | 0.2 | 1.1 | 0.4 | 2.3 | 1.3 | 13.4 | 1.3 | 29.1 | 0.0 | 0.4 |
Total | 458 | 4,773 | 453 | 4,728 | 455 | 4,695 | 452 | 4,689 | 451 | 4,638 |
(b) |
Not at all in the past 12 months (%) | 60.8 | 88.0 | 62.4 | 79.3 | 32.1 | 45.4 | 66.4 | 71.6 | 96.0 | 96.8 |
Less than once a month (%) | 19.6 | 5.8 | 20.9 | 8.5 | 38.3 | 23.6 | 24.7 | 21.5 | 2.4 | 1.1 |
Once a month (%) | 9.0 | 2.4 | 7.6 | 2.9 | 10.2 | 8.3 | 4.4 | 3.5 | 0.4 | 0.5 |
2–3 times a month (%) | 5.1 | 1.3 | 4.2 | 1.6 | 10.0 | 6.7 | 2.2 | 1.4 | 0.2 | 0.3 |
Once a week (%) | 2.4 | 1.4 | 2.4 | 2.2 | 4.4 | 7.7 | 1.3 | 1.0 | 0.4 | 0.5 |
2–3 times a week (%) | 2.0 | 0.6 | 1.3 | 2.1 | 4.0 | 5.5 | 0.2 | 0.6 | 0.0 | 0.1 |
4 or more times a week (%) | 1.1 | 0.6 | 1.1 | 3.4 | 1.1 | 2.9 | 0.7 | 0.5 | 0.4 | 0.7 |
Total | 454 | 4,665 | 449 | 4,636 | 452 | 4,659 | 450 | 4,647 | 452 | 4,664 |
UR students (M = 3.82, SD = 1.89) did not engage in a significantly different number of gambling activities compared to general population respondents (M = 3.92, SD = 1.95), F(1,5169) = 1.82, p = 0.18.
Alcohol and Drug Use While Gambling
UR students reported drinking alcohol while gambling significantly less often than general population respondents,
b = −0.28,
SE = 0.10, Wald = 7.34,
p = 0.007. No significant differences were observed between UR students and general population respondents in terms of recreational drug use while gambling (Table
3).
Table 3
Percentage of UR students and general population respondents who use alcohol and drugs whilst gambling
Never (%) | 24.0 | 27.7 | 89.8 | 91.6 |
Rarely (%) | 18.9 | 22.3 | 5.8 | 2.9 |
Sometimes (%) | 23.8 | 23.6 | 3.1 | 3.4 |
Often (%) | 19.1 | 17.2 | 0.7 | 1.4 |
Always (%) | 14.2 | 9.3 | 0.7 | 0.7 |
Total | 450 | 4,669 | 452 | 4,675 |
Proportion of Online Betting
When controlling for demographic factors, UR students used the Internet for a significantly higher proportion of their gambling compared to general population respondents for the following gambling activities: sports betting online via computer (b = 10.61, SE = 2.61, t(1897) = 4.06, p < 0.001), sports betting online via mobile phone (b = 12.80, SE = 4.56, t(550) = 2.81, p = 0.005) and horse and dog racing online via computer (b = 12.52, SE = 2.40, t(2461) = 5.22, p < 0.001).
UR students and general population respondents did not differ significantly in the percentage of their gambling for the following gambling activities: lottery tickets online, sports betting via land-based agencies, sports betting via telephone, sports betting via interactive television, horse and dog racing via land-based agencies, horse and dog racing via mobile phone, horse and dog racing via telephone, bingo online, games of skill online and poker online. There was a large amount of variance in the responses given to these questions, which may explain some of the non-significant results.
Time Spent Betting Online
No significant differences were found in terms of the length of gambling sessions for bingo, games of skill, poker and Internet casinos. Once again, power was reduced given the large variance in responses.
How Internet Gamblers Use the Internet
UR students were significantly more likely to report having never gambled on the Internet (77.0 %) than respondents from the general population (29.5 %). The results from a multinomial logistic regression, using those who had never gambled on the Internet as a reference group, indicated that UR students were significantly less likely than general population respondents to gamble on the Internet either at home (b = 1.29, SE = 0.14, Wald = 82.48, p < 0.001) or at work (b = 2.13, SE = 0.74, Wald = 8.28, p = 0.004) (Table
4). UR students tended to gamble via the Internet significantly later in the day compared to general population respondents, b = 0.13, SE = 0.04, t(3432) = 3.02,
p = 0.003 (Table
5). UR students and general population respondents did not differ significantly in terms of the year that they started to use the Internet for gambling purposes. While there was a four-year difference (2008 vs. 2004), the result was not significant when controlling demographic factors.
Table 4
Percentage of each category of respondent who primarily uses the Internet in each location to bet online
Home | 19.9 | 49.1 | 66.6 |
Work | 0.4 | 0.4 | 2.5 |
Away from home and work | 2.6 | 2.6 | 1.4 |
I have never gambled on the Internet | 77.0 | 47.8 | 29.5 |
Table 5
Percentage of each category of respondent who bets online in each time period
6 am–12 pm | 1.9 | 5.8 | 10.3 |
12 pm–6 pm | 21.0 | 35.0 | 58.8 |
6 pm–midnight | 66.7 | 53.3 | 29.0 |
Midnight–6 am | 10.5 | 5.8 | 1.9 |
Problem Gambling
There was a significantly higher proportion of UR students (32.1 %) in the low-risk gambler condition, compared to 25.0 % of general population respondents, b = −0.41, SE = 0.16, Wald = 6.18, p = 0.013. Similarly, there was a significantly lower proportion of UR students (8.8 %) in the possible problem gambling category compared to general population respondents (17.0 %), b = 1.46, SE = 0.22, Wald = 45.12, p < 0.001.
An error in the survey design meant that ‘sports betting’ was not an available option for participants to nominate this form as the primary cause of their gambling problems. A significantly higher proportion of general population respondents (32.9 %) attributed their problem gambling to betting on horse and dog races, compared to just 3.5 % of UR students, b = 2.41, SE = 0.47, Wald = 26.18, p < 0.001. Similarly, a higher proportion of general population respondents (24.8 %) nominated EGMs compared to 18.3 % of UR students, b = 0.69, SE = 0.26, Wald = 6.81, p = 0.009, while only 0.6 % of general population respondents indicated their problems were caused by betting on games of skill against other people compared to 4.2 % of UR students, b = −2.92, SE = 0.82, Wald = 12.62, p < 0.001.
In terms of mediums for gambling, 24.8 % of general population respondents stated the Internet via computers was the primary cause of their gambling problems compared to 16.2 % of UR students, b = 0.62, SE = 0.26, Wald = 5.66, p = 0.017. Furthermore, 35.3 % of general population respondents indicated land-based gambling was the causal mode compared to 24.6 % of UR students, b = 0.74, SE = 0.23, Wald = 10.53, p = 0.001.
A significantly lower proportion of UR students (2.1 %) reported having sought help for gambling problems, compared to 16.5 % of general population respondents, b = 2.39, SE = 0.61, Wald = 15.64, p < 0.001.
The Role of the Internet in Problem Gambling
A significantly higher proportion of general population respondents (19.3 %) stated that they have had problems due to their gambling, compared to 11.5 % of UR students, b = 1.12, SE = 0.34, Wald = 11.24, p = 0.001. A higher proportion of general population respondents (62.7 %) stated that their gambling problems emerged after they first gambled online, compared to 30.8 % of UR students, but this was not significant when controlling for age, marital status, income and gender. There were no significant differences between UR students and general population respondents in terms of the effect of online gambling on their eating or sleeping patterns. Similarly, UR students and general population respondents did not differ significantly in the perceived impact of using credit cards or electronic bank transfers on their spending.
Gambling Knowledge and Beliefs Score
UR students (M = 6.33, SD = 2.13) had significantly lower scores than general population respondents (M = 7.36, SD = 1.75) on the Gambling Knowledge and Beliefs scale, b = −0.73, SE = 0.11, t(4546) = 6.77, p < 0.001.
Gambling Attitude Scale Summary Score
UR students (M = −0.61, SD = 1.42) had significantly less positive attitudes towards gambling than general population respondents (M = 0.47, SD = 1.63) based on their Gambling Attitude scale summary score, b = −0.56, SE = 0.10, t(4637) = 5.79, p = 0.001.
University-Recruited Students Compared to General Population Students
UR students differed from GP students in proportions of gender (43.4 % male from UR compared to 76.7 % male from GP students, χ
2 (1,
N = 693) = 69.19,
p < 0.001, ϕ = −0.32) and marital status (χ
2 (4,
N = 692) = 11.55,
p < 0.001, ϕ = 0.13) (Table
1). GP students (M = 24.19,
SD = 7.69) were significantly older than UR students (M = 21.10,
SD = 5.97),
t(376.01) = 5.35,
p < 0.001,
d = 0.45. No significant differences were observed in overall income,
p = 0.029.
As for the preceding analyses, all of these factors were controlled in all analyses between UR students and GP students. Overall, the pattern of results between UR students and GP students is much the same as for UR students and general population respondents and only the results that differ are reported below.
Gambling Activities
UR students bought lottery tickets significantly less frequently compared to GP students, b = −0.61, SE = 0.19, Wald = 10.04, p = 0.002. Furthermore, UR students played EGMs significantly less frequently compared to GP students, b = −0.37, SE = 0.16, Wald = 5.15, p = 0.023. The difference between UR students and GP students in terms of gambling via table games at a casino was non-significant.
Proportion of Betting Online
The differences between UR students and GP students in terms of the percentage of sports betting done via computer or sports betting via mobile phone were non-significant.
How Internet Gamblers Use the Internet
The difference between UR students and GP students in terms of proportion of Internet gambling carried out at work was non-significant, p = 0.79.
Problem Gambling
The difference between the proportions of UR and GP students claiming to have suffered gambling-related problems was non-significant, p = 0.27. Similarly, the difference between the proportions of UR and GP students who indicated that their gambling problems started after first gambling online was non-significant, p = 0.20. There was also no significant difference in the proportions of UR and GP students in terms of sleep disruptions due to online gambling, p = 0.42. There was no significant difference in the proportion of UR and GP students who attributed their problem gambling to EGMs, p = 0.65, nor was there a significant difference in terms of games of skill against other people, p = 0.20. There was also no significant difference in the proportion of UR and GP students who indicated that their problem gambling was related to using the internet via a computer, p = 0.54, nor was there a significant difference for land-based gambling, p = 0.06. Apart from these, the pattern of results for UR students compared to GP students was the same as those reported for UR students compared to general population respondents.