RESEARCH QUESTION
What are the differences between internet gamblers and non-internet gamblers?
PURPOSE
To examine differences, particularly related to gender and problem gambling behaviours, between internet gamblers and non-internet gamblers in a student sample.
PARTICIPANTS
Four hundred seventy three undergraduate students (213 males, 260 females, mean age 22 years) at a United Kingdom East Midlands university.
HYPOTHESES
Males would be more likely than females to be internet gamblers. Internet gamblers would be more likely than non-internet gamblers to be problem gamblers. Males would be more likely than females to be problem internet gamblers.
PROCEDURE
Approximately 2000 email requests were sent to students in a variety of undergraduate courses inviting them to participate in an online survey of student gambling behaviour. Each email included a hyperlink to the online survey.
MAIN OUTCOME MEASURESGeneral gambling behaviour. Questions were asked regarding the frequency of gambling activities, the amount of time and money spent on gambling activities, and the types of gambling activities engaged in. The South Oaks Gambling Screen (SOGS), a 20-item self-report measure used to assess gambling symptoms over a person’s lifetime, was used to identify problem gamblers. Internet gambling behaviour. Questions were asked regarding the frequency of online gambling activities, the amount of time and money spent on online gambling activities, and the types of online gambling activities engaged in. Questions were also asked regarding payments used for online gambling (i.e., credit card, debit card, e-wallet) and overall trustworthiness on the internet. Additional questions regarding factors involved in the initial decision to gamble (i.e., role of family/friends/advertising/etc) were asked. Demographic. Questions were asked regarding age, gender, ethnicity, etc.
KEY RESULTSGender differences. Male gamblers were significantly more likely to have gambled on the internet (50%) than females (8%). Problem gamblers were more likely to be male (81%) than female (19%). Males spent significantly more money on gambling per week than females (average dollars/time spent were not reported). Internet gambling. Internet gamblers were more likely to gamble more than once a week (60%) than non-internet gamblers (20%). Internet gamblers spent significantly more money on gambling per week than non-internet gamblers. Problem gambling. Problem gamblers were more likely to have gambled on the internet (77%) than not (23%). Problem gamblers gambled more often and spent more money per week on gambling than non-problem gamblers.
LIMITATIONS
The sample was self-selecting and may not be representative of either gamblers or internet gamblers. Similarly, the sample consisted of only students and may not generalize to other populations. The data were self-report and may not necessarily be accurate recollections of gambling behaviours. There was a low response rate (approximately 25%), which also limits the generalizability of the results.
CONCLUSIONS
All three hypotheses were confirmed: males were more likely than females to be internet gamblers, internet gamblers were more likely than non-internet gamblers to be problem gamblers, and males were more likely than females to be problem internet gamblers. The internet does seem to have an effect on both situational (i.e., accessibility and convenience) and structural (i.e., fast-action and continuous) characteristics of many gambling activities which may make internet gambling more addictive than other forms of gambling. More research to explore characteristics of internet gamblers and the relationship between internet and non-internet gambling is warranted.
