Introducing The Canadian Problem Gambling Index

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Problem

What’s the big problem with gambling?

The Canadian Problem Gambling Index Final Report You The Canadian Problem Gambling Index Final Report will get the 25 bonus spins only if you deposit £25 or more. The maximum Bonus available to you will be the equivalent to 100% of your deposit amount and no more than £50. Problem Gambling: The Issues, the Options (“Gambling Problems: The Numbers” continued).The average amount each Canadian spent on gambling in 2009 was $515, compared to $130 in 1992. The average amount each Ontarian spent in 2009 was $455, compared to $105 in 1992. 2.It is estimated that 1.2%. Of Ontarians are affected. Introducing The Canadian Problem Gambling Index, poker suit lamp, meropa casino entertainment, opaque casino dice. Validation of the Canadian Problem Gambling Index (CPGI) (Ferris, Wynne, & Single, 1999). Compared to the SOGS, the CPGI is more theory based, designed specifically for Canadian communities, and better able to discriminate between problem gambler types in general population.

For most people, gambling is an exciting and relaxing way to win a little money or to socialize with friends. For others, gambling becomes a force that tears their family, financial, work and social lives apart.

When is gambling a problem?

If the thrill of the big win becomes more important than spending time with loved ones, if gambling affects work performance, or if debts grow and grow and it seems like they’ll never disappear, gambling may be a problem.

This site is designed to provide you with information about what problem gambling is and to recognize the signs and symptoms that there may be a problem.

What can I do?

Get informed and learn

First of all, learn about gambling and the differences between gambling and problem gambling.

Ask Yourself

Introducing The Canadian Problem Gambling Index 2020

The

Take one of the tests that are included and see if you or someone you care about might have a problem

Index

Get Help

Beating a gambling problem can be a real challenge but the odds are in your favour! Learn how to gamble safely and how to help someone you care about to set limits with their gambling.

What is Problem Gambling

Problem gambling is any type of gambling that compromises, disrupts, or damages your personal, family, working and social relationships, as well as your financial situation. It also has a great effect on the individual’s physical, emotional and mental health.

The Canadian Problem Gambling Index, in its final report, defines problem gambling as:

Problem gambling is gambling behaviour that creates negative consequences for the gambler, others in his or her social network, or for the community.

Problem gambling has a serious impact on not only the gambler, but also on family members, friends and co-workers.

Gamblers Anonymous define problem gambling as:

Any betting or wagering, for self or other whether for money or not, no matter how slight or insignificant, where the outcome is uncertain or depend upon chance or skill constitutes gambling.

GamblingProblem Gambling
  • Risking something of value
  • One realizes that something is at risk
  • Understanding that when the bet is done, is irreversible
  • Outcome is determine by chance
  • A pattern of gambling behaviour which compromises, disrupts and damages family, personal and/or professional life
  • Spends money in a way that is harmful to the person
  • May also harm people around them including family, spouse, friends

Problem Gambling Facts

Problem Gambling Facts:

  • 95% of the population have been involved in gambling are healthy when it comes to gambling however,
  • 5% of the population are problem gamblers, and
  • 1% of the 5% are compulsive/pathological gamblers.

Many people gamble without any problem. The vast majority of people gamble without doing any harm to themselves or others. According to the CAMH (Centre for Addiction and Mental Health) 1995 study, 84% of Ontario adults gamble at least once within a year, and 1/3 of the population (33%) has participated in at least 3 different forms of gambling in 1994 (Ferrir 1996).

Nevertheless, there is a sector of the population that will develop a “gambling problem” and problem gambling has a great deal in common with other addictions. Among gamblers, up to 50% have substance abuse problems. Nine to eighteen per cent of substance abusers will develop gambling problems. Substance abuse is higher among younger gamblers.

Gambling Phases

Progression of gambling phases

  • Early Stage Dependence (Winning Phase)
    In this stage, the financial rewards or the internal escape received as a result of gambling behaviour provide sufficient motivation for the behaviour to continue
  • Middle Stage (Losing Phase)
    Losses begin to stack up, gamblers start “chasing” their losses, which cause the gambling behaviour to become more out of control.
  • Late Stage (Desperation Phase)
    In this final stage, the gambler becomes overwhelmed. There are extreme emotional, financial and family/relationship problems. Criminal behaviour may occur, with possible legal consequences.

Types of Gamblers

  1. Professional: skilled, able to control
  2. Anti-Social or Criminal: use gambling to cheat, involved In illegal activities
  3. Casual Social: recreation and excitement, for win, loss is the cost of entertainment
  4. Serious Social: gambling main form of recreation but it is second to family and vacation
  5. Relief/Escape: to find relief from anxiety, depression, anger
  6. Compulsive: consists of four features: it is at the end of the continuum):
    • Progression: can’t stop, bets go up; will continue gambling as long as they have funds. Time spent gambling increases.
    • Intolerance of losing, when losses occur, the compulsive gambler “chases” the lost money. Losses are concealed from family members and lying becomes a major element in the gambling cycle.
    • Preocupation: Thoughts about gambling become a constant obsession.
    • Disregard for consequences: During this final stage, forgery, thefts and embezzlement are common. Despite these illegal acts, the gambler intends to score a big win and set everything right.

Some people gamble because they think they can “beat the system” or because they feel lucky.

The laws of probability will ensure that if you do “get ahead”, you’ll eventually erase those gains if you keep playing.

Introducing The Canadian Problem Gambling Index 2019

Gambling Facts and Myths:

There are many myths about winning when you gamble. According to the Responsible Gambling Council , these are some of the more frequent ones:

Introducing The Canadian Problem Gambling Index Pdf

Myth: I can see a pattern in the way the machine is paying out

Fact: Games are based on random event

Myth: If I keep playing, I will eventually win, get my money back

Fact: The longer one plays, the more one will lose

Myth: One has a better chance of winning at a slot machine by the entrance

Fact: Casinos have no control over the outcomes of the player’s game

Myth: I have discovered a winning system

Fact: People who have an inflated belief in their skills are a greater risk of developing a gambling problem. They undervalue the impact of the things they can not control.

Myths and Misconceptions

  • Gamblers have flamboyant, carefree personalities. (Some are, but others are quiet, introverted, and serious minded)
  • Gamblers enjoy risks in all areas of their lives. (Some are big risk takers, others are conservative in personal habits and work)
  • If you don’t gamble daily, you’re not a problem or compulsive gambler
  • You can be addicted to an activity. (ambling can change one’s mood by affecting the biochemistry of the brain much the same way as alcohol or drugs)
  • Gamblers are thieves and criminals. (Not true, but some gamblers may resort to criminal behaviour in desperation)
  • A compulsive gambler will bet on anything. (Problem gamblers generally have preferences and are not tempted by every type of gambling)
  • All compulsive gamblers want to lose. (are addicted to the act of gambling –they would rather lose than be out of the action)
  • Compulsive gamblers are week-willed, otherwise they would simply stop.

This article is available in: HTMLjgi:

Journal Information
Journal ID (publisher-id): jgi
ISSN: 1910-7595
Publisher: Centre for Addiction and Mental Health
Article Information
Copyright © 1999-2006 The Centre for Addiction and Mental Health
Received Day: 29 Month: 02 Year: 2004
Publication date: April 2006
Publisher Id: jgi.2006.16.19
DOI: 10.4309/jgi.2006.16.19
Gambling and problem gambling in a sample of university students
Affiliation: Alberta Gaming Research Institute, University of Lethbridge, Lethbridge, Alberta, Canada. E-mail: robert.williams@uleth.ca
Affiliation: Department of Mathematics & Computer Science, University of Lethbridge
Affiliation: Department of Sociology, University of Lethbridge
Affiliation: School of Health Sciences, University of Lethbridge

For correspondence: Dr. Robert Williams, Alberta Gaming Research Institute, 102 Anderson Hall, University of Lethbridge, Lethbridge, Alberta, Canada, T1K 3M4. E-mail: Robert.williams@uleth.ca
All URLs were available at the time of submission. Accepted: April 18, 2005. This article was peer reviewed.
Dr. Robert Williams, PhD, School of Health Sciences & Alberta Gaming Research Institute, University of Lethbridge, Lethbridge, Alberta T1K 3M4 Canada. Phone: 403-382-7128, fax: 403-329-2668, e-mail: robert.williams@uleth.ca
Competing interests: None declared.
Contributors: RW was responsible for the research design, data analysis and writing the manuscript, DC, R. Wood, and NN all contributed to the research design and data collection.
Ethical approval: The University of Lethbridge Human Subjects Research Committee provided approval for this project in 2002 as part of the approval for the project “Impact of statistical knowledge on the gambling behaviour of university students.”
Funding: Funding for this project was provided by the Alberta Gaming Research Institute.
Robert Williams, PhD, CPsych. is a professor in the School of Health Sciences, University of Lethbridge, and also the Lethbridge Coordinator for the Alberta Gaming Research Institute.
Dennis Connolly, PhD is an associate professor in the Department of Mathematics & Computer Science at the University of Lethbridge.
Robert Wood, PhD is an assistant professor in the Department of Sociology at the University of Lethbridge.
Nadine Nowatzki, MA is a research associate in the School of Health Sciences at the University of Lethbridge.
Abstract

University students from southern Alberta (n = 585) were administered a questionnaire to assess their gambling behaviour. Seventy-two percent reported gambling in the past 6 months, with the most common types being lotteries and instant win tickets (44%) and games of skill against other people (34%). Most students who gambled spent very little time and money doing so (median time spent = 1.5 hrs; median amount of money spent = $0). While gambling is an innocuous activity for most, a significant minority of students are heavy gamblers who experience adverse consequences from it. Seven and one-half percent of students were classified as problem or pathological gamblers, a rate significantly higher than in the general Alberta adult population. The characteristics that best differentiated problem gamblers from non-problem gamblers were more positive attitudes toward gambling, ethnicity (41% of Asian gamblers were problem gamblers), university major (kinesiology, education, management), superior ability to calculate gambling odds, and older age.

Introduction

The impact of the extensive availability, advertising, and sanctioning of legalized gambling is of concern in the fields of public health and addictions. Among adults, the prevalence of disordered gambling has increased significantly from 1977 to 1993 (Shaffer, Hall, & VanderBilt, 1997). It was estimated in a 2001 meta-analysis that 4.0% of adults in North America met criteria for either problem or pathological gambling in the past year (Shaffer & Hall, 2001).

Of even greater concern is the impact of gambling on the current generation of youth, as they are the first to have been raised in an environment of extensive legalized and government-sanctioned gambling. Indeed, there appears to be reason for concern. Several surveys have found the prevalence rates of gambling to be highest in young adults. Young adults typically have the highest rates of involvement in most risky behaviours (substance use, reckless driving, unsafe sex, etc.) (e.g., Douglas et al., 1997). Gambling appears no different. The lifetime rates of gambling in college and university students typically range from 70% to 94%, with males consistently having higher rates than females (Adebayo, 1998; Devlin & Peppard, 1996; Engwall, Hunter, & Steinberg, 2002; Kang & Hsu, 2001; Ladouceur, Dube, & Bujold, 1994; Lesieur et al., 1991; Oster & Knapp, 1998). A recent nationally representative study of college students in the United States (LaBrie, Shaffer, LaPlante, & Wechsler, 2003) found a lower prevalence, but this study was limited by low response rates and a lack of questions about all forms of gambling.

National studies have consistently found that the rates of problem gambling also peak in the age group 18 to 24 (Gerstein et al. (1999) in the United States, Productivity Commission (1999) in Australia, and Rönnberg et al. (1999) in Sweden). Similarly, the meta-analysis of all North American prevalence studies found that the 19 study samples of college students had higher overall lifetime rates of problem and pathological gambling (16.4%) than either adolescents (11.8%) or adults (6.1%) (Shaffer & Hall, 2001).

While many studies have documented that college and university students have the highest prevalence rates of gambling and problem gambling, much less is known about the nature of gambling in this group. Specifically, little is known about the amount of time and money spent on gambling, the types of gambling being played, and the characteristics differentiating nongamblers from gamblers and gamblers from problem gamblers. The above topics form the basis for the present study.

Method

The sample consisted of students from the University of Lethbridge, in Lethbridge, Alberta, Canada. Alberta has one of the widest arrays of gaming entertainment options available to its citizenry of any jurisdiction in North America (Wynne, 2000), and the city of Lethbridge has all of these options available. The University of Lethbridge is a primarily undergraduate institution with a student body mostly from western Canada. Students were recruited from 10 different introductory courses in statistics, history, and sociology between September 2001 and April 2003. A 30-minute gambling questionnaire was administered at the beginning of each course. Students were told that the questionnaire was designed to assess their general gambling knowledge, attitudes, and behaviour and that completion of the questionnaire was optional. The questionnaire collected and assessed

  • 1. demographic information concerning age, gender, race/ethnicity, current university major, and current university year;
  • 2. attitude toward gambling as measured by the Gambling Attitudes Scale (see below);
  • 3. knowledge of gambling and problem gambling as measured by the Gambling Knowledge Scale;
  • 4. gambling fallacies as measured by the Gambling Fallacies Scale;
  • 5. knowledge and ability to calculate gambling odds as assessed by the Gambling Odds Scale;
  • 6. gambling behaviour, i.e., type of gambling engaged in, time spent gambling, and amount of money spent gambling in the past 6 months;
  • 7. problem gambling as measured by the nine-item Canadian Problem Gambling Index (CPGI) (Ferris & Wynne, 2001).

The Gambling Attitudes Scale is a three-item scale that measures people's belief about the morality of gambling and its harm versus benefit. It has good 1-month test-retest reliability as well as excellent concurrent and predictive validity. This scale was developed along with the Gambling Knowledge Scale, the Gambling Fallacies Scale, and the Gambling Odds Scale to study gambling in adult populations (Williams, 2003).

The Gambling Knowledge Scale is a 10-item scale assessing whether people are aware of the legalities of gambling, the different forms of gambling, the prevalence of problem gambling, the risk factors for developing problem gambling, where to get help for problem gambling, etc. It has very good test-retest reliability as well as internal consistency (Williams, 2003).

The Gambling Fallacies Scale is a 10-item scale measuring awareness of and resistance to common gambling fallacies (e.g., “to win at gambling you need to think positively”). It has very good 1-month test-retest reliability, good internal consistency, and very good concurrent and predictive validity (Williams, 2003).

The Gambling Odds Scale is a 10-item scale with excellent 1-month test-retest reliability, internal consistency, and concurrent and predictive validity (Williams, 2003).

Results
Sample

Over 95% of the students completed the questionnaire. The final sample consisted of 585 students. Their average age was 21.7 (3.7 SD), and 61% were female. Racial/ethnic background was 81% European-Canadian, 8% Asian-Canadian, 4% Aboriginal, 4% other, 2% African-Canadian, and 1% Hispanic-Canadian. Thirty-four percent were management majors, 26% were science majors, 21% were social science majors, 9% were humanities majors, 5% were kinesiology/physical education majors, and 4% were education majors. Forty percent of students were in their first year, 22% in second year, 25% in third year, and 12% in fourth year. This is a very representative sample of the general student body with the exception of university year, where the sample contained a greater portion of first-year students.

Gambling behaviour

As seen in Table 1, 72.1% of the sample reported gambling in the past 6 months. The most common types of gambling engaged in were lotteries and instant win tickets (44%), followed by games of skill against other people (34%), video lottery terminals (VLTs) or slot machines (29%), and casino table games (26%). The average number of different types of gambling engaged in was 1.7 (median = 1; mode = 0).

Table 1 also reports the average total time spent on different gambling activities in the past 6 months (reported frequency multiplied by the average time spent per occasion). The average time spent was 33.7 total hours (1.5 hours median) for all types of gambling combined. Seven percent of students spent 40 hours or more gambling. The types of gambling that students spent the most time at were games of skill against other people (17.3 hours), casino table games (15.3 hours), the stock market (8.7 hours), and VLTs or slot machines (7.3 hours). In all cases, the averages are much higher than the medians due to a small percentage of gamblers with very high involvement in the activity. Median and modal time spent was zero for each activity.

The average total amount of money reported lost on all types of gambling in the past 6 months was $25.93 ($0 median). Eleven percent of students reported losing more than $100, and 1% reporting losing more than $1000. The types of gambling that students spent the most money on were VLTs or slot machines ($5.23), the stock market ($4.87), casino table games ($4.84), and lotteries or instant win tickets ($4.33). In all cases the median amount of money spent was zero. The average losses are low partly because they are offset by small numbers of people reporting significant winnings on these activities.

Problem gambling

Using the CPGI, 1.4% of the total sample met criteria for severe problem gambling (CPGI 8+; roughly equivalent to pathological gambling) and another 6.2% met criteria for moderate-risk gambling (CPGI 3–7; equivalent to problem gambling). A further 16.9% were low-risk gamblers (CPGI 1–2), 47.4% were non-problem gamblers (CPGI 0), and 27.9% were nongamblers.

Characteristics differentiating gamblers from nongamblers

A direct logistic regression investigated characteristics differentiating the gamblers from the nongamblers. Eight predictor variables were used: age, sex, ethnicity, university major, university year, attitudes toward gambling, number of gambling fallacies, and skill at calculating gambling odds. The 12 cases with missing values for age and the 7 cases with missing values for university year were imputed using linear trend at point. To reduce the impact of outliers, students older than 27 were recoded as age 27. There were 352 gamblers and 142 nongamblers available for the analysis.

A test of the full model with all eight predictors against a constant-only model was statistically reliable (χ2 (19, N = 494) = 104.4, p < .0001), indicating that the eight predictors, as a set, reliably distinguished between gamblers and nongamblers. The variance accounted for was modest, with Nagelkerke R squared = .27. Overall prediction success was 75.5%. Table 2 shows regression coefficients, Wald statistics, and odds ratios for each of the eight predictors. According to the Wald criterion, only three variables reliably predicted gambling: more positive attitudes toward gambling (z = 47.5, p < .001), university major (z = 10.5, p < .05), and superior ability to calculate gambling odds (z = 4.7, p < .05). The percentage of students who were gamblers as a function of university major was as follows: kinesiology/physical education (82%), management (82%), education (74%), social science (72%), science (66%), and humanities (56%).

Characteristics differentiating problem gamblers from non-problem gamblers

A direct logistic regression investigated characteristics differentiating problem and pathological gamblers from gamblers who had not experienced any adverse consequences. Eight predictor variables were used: age, sex, ethnicity, university major, university year, attitudes toward gambling, number of gambling fallacies, and skill at calculating gambling odds. The 12 cases with missing values for age and the 7 cases with missing values for university year were imputed using linear trend at point. To reduce the impact of outliers, the students older than 27 were recoded as age 27.

A test of the full model with all eight predictors against a constant-only model was statistically reliable (χ2 (18, N = 352) = 79.9, p < .001), indicating that the eight predictors, as a set, reliably distinguished between problem gamblers and non-problem gamblers. The variance accounted for was moderate, with Nagelkerke R squared = .40. Overall prediction success was 91.2%. Table 3 shows regression coefficients, Wald statistics, and odds ratios for each of the eight predictors. According to the Wald criterion, five variables reliably predicted problem gambling: more positive attitudes toward gambling (z = 23.7, p < .001), ethnicity (41% of Asian gamblers were problem gamblers) (z = 15.4, p < .01), university major (18% of kinesiology majors, 18% of education majors, and 14% of management majors were problem gamblers) (z = 14.6, p < .05), superior ability to calculate gambling odds (z = 6.2, p < .05), and older age (z = 4.1, p < .05).

Discussion

Gambling is a common activity among university students, with 72% having done so in the past 6 months. The most common types of gambling were lotteries and instant win tickets, followed by games of skill against other people. However, most students who gambled indicated that they spent very little time and money doing so. The types of gambling that occupied the most time were games of skill against other people and casino table games. The types of gambling associated with the greatest spending were VLTs and slot machines, the stock market, and casino table games. Consistent with prior research, it would appear that for most students gambling is a fairly innocuous activity, done primarily for entertainment purposes (Neighbors, Lostutter, Cronce, & Larimer, 2002; Kang & Hsu, 2001).

The overall percentage of gamblers in the present study is slightly lower than that found in most other studies. This between-jurisdiction difference potentially reflects a variety of different factors, including (1) the number and type of easily available gambling opportunities, (2) the demographics of the gambling population, (3) the nature of local gambling legislation and its impact upon gambling behaviour, and (4) the respective cultural and ethnic composition of the groups of university students being surveyed. With respect to this last factor, the University of Lethbridge is situated in a region with lower rates of gambling compared to the rest of the province (Smith & Wynne, 2002, 2004). A significant minority of the student body and the population of southern Alberta are members of the Latter Day Saints, a religious group that strongly proscribes gambling behaviour.

The preferred forms of gambling in the present study are consistent with what has been found previously. The most popular gambling activity for college and university students as well as adults appears to be lotteries (Engwall et al., 2002; Kang & Hsu, 2001; Ladouceur et al., 1994). The five most common gambling activities in the studies mentioned above were lotteries, casinos, playing cards, slot/poker machines, and skill games, but these did vary somewhat in order of preference between studies. It is more difficult to make comparisons to other studies regarding time and money spent, as extant studies on these issues address mostly casino gambling (e.g., Bailey et al., 1997; Kang & Hsu, 2001). Nonetheless, consistent with the present research, it does not appear that a great deal of time and money is being lost to gambling.

While gambling is innocuous for most, it is apparent that a significant minority of students are heavy gamblers who experience adverse consequences from it. Seven and one-half percent of students were classified as problem or pathological gamblers. Similar to prior research, the rate of problem/pathological gambling in university students is higher than in the general population. Despite being in a region with less gambling, University of Lethbridge students have a rate of problem/pathological gambling 2.3% higher than the 5.2% rate for Albertan adults (Smith & Wynne, 2002). The rates of problem/pathological gambling in the present study are lower than reported in other studies of college and university students. The reasons are undoubtedly the same reasons that the rate of gambling is somewhat lower. The other difference is that most other studies have used the South Oaks Gambling Screen (Lesieur & Blume, 1987) or variations thereof, while this is the only study that has used the newly created CPGI.

There has been very little prior research concerning variables that discriminate between college/university gamblers and nongamblers or problem gamblers and non-problem gamblers. In the present study, having a more positive attitude toward gambling was the best predictor of both being a gambler and being a problem gambler. This is not an unexpected finding, although it is interesting that people experiencing problems still maintain a more positive attitude than people not experiencing problems.

The higher rates of gambling and problem gambling for kinesiology and management majors is an interesting finding that has not been reported in previous research. However, what have been previously reported are higher rates of problem gambling in student athletes, presumably due to a greater propensity for risk taking (Engwall et al., 2002; Rockey, Beason, & Gilbert, 2002). It is not unreasonable to anticipate that a significant portion of students pursuing a kinesiology/physical education degree are also student athletes. Risk taking might also characterize people interested in business management degrees. Alternatively, the relationship between gambling and business management interests may be due to a common interest in making money.

The relationship between superior ability to calculate gambling odds and both gambling and problem gambling is a puzzling one. It is possible that mathematically skilled individuals feel they possess the necessary competence to gamble relatively successfully. However, one would think that more mathematically knowledgeable students would also be more cognizant of the negative mathematical expectation for most forms of gambling. The link between older age and problem gambling could be because it takes some time for gambling to develop into a problem. Alternatively, older students may have either higher incomes or higher debt loads, which might create a greater predilection to gamble. The link between Asian heritage and problem gambling is something that has been previously found in the literature (Lesieur et al., 1991), as well as in general population surveys.

References
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Bailey, E.A.. Burroughs, S.W.. Dabit, J.S.. Hambrick, R.S.. Theriot, P.B.. ( 1997). The lure of casino gambling and its potential impact on college students in Mississippi. College Student Affairs Journal, 17, 81-91.
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Ferris, J.. Wynne, H.. ( 2001). The Canadian Problem Gambling Index: Final report. Submitted for the Canadian Centre on Substance Abuse. February 19, 2001.
Gerstein, D.. Volberg, R.A.. Murphy, S.. Toce, M.. Hoffmann, J.. Palmer, A.. , et al. ( 1999). Gambling impact and behavior study: Report to the National Gambling Impact Study Commission. Chicago: National Opinion Research Center at the University of Chicago.
Kang, S.K.. Hsu, C.H.C.. ( 2001). University students' perceptions on legalized gambling and their casino gaming behaviors. The Consortium Journal, 5, 5–16.
LaBrie, R.A.. Shaffer, H.J.. LaPlante, D.A.. Wechsler, H.. ( 2003). Correlates of college student gambling in the United States. Journal of American College Health, 52, 53-63.
Ladouceur, R.. Dube, D.. Bujold, A.. ( 1994). Prevalence of pathological gambling and related problems among college students in the Quebec metropolitan area. Canadian Journal of Psychiatry, 39, 289-293.
Lesieur, H.R.. Blume, S.B.. ( 1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 1184-1188.
Lesieur, H.R.. Cross, J.. Frank, M.. Welch, M.. White, C.M.. Rubenstein, G.. , et al. ( 1991). Gambling and pathological gambling among university students. Addictive Behaviors, 16, 517-527.
Neighbors, C.. Lostutter, T.W.. Cronce, J.M.. Larimer, M.E.. ( 2002). Exploring college student gambling motivation. Journal of Gambling Studies, 18, 361-371.
Neighbors, C.. Lostutter, T.W.. Larimer, M.E.. Takushi, R.Y.. ( 2002). Measuring gambling outcomes among college students. Journal of Gambling Studies, 18, 339-360.
Oster, S.L.. Knapp, T.J.. ( 1998). Underage and pathological gambling by college students: Emerging problem on campus?Psychology and Education, 38, 15-19.
Productivity Commission ( 1999). Australia's gambling industries, Report No. 10. Canberra: AusInfo.
Rockey, D.L.. Beason, K.R.. Gilbert, J.D.. ( 2002). Gambling by college athletes: An association between problem gambling and athletes. Electronic Journal of Gambling Issues, Issue 7, December 2002. Retrieved August 21, 2005, from http://www.camh.net/egambling/issue7/research/college_gambling.html
Rönnberg, S.. Volberg, R.A.. Abbott, M.W.. Moore, W.L.. Andren, A.. Munck, I.. , et al. ( 1999). Gambling and problem gambling in Sweden. Report No. 2 of the National Institute of Public Health Series on Gambling. Stockholm: National Institute of Public Health.
Shaffer, H.J.. Hall, M.N.. ( 2001). Updating and refining meta-analytic prevalence estimates of disordered gambling behavior in the United States and Canada. Canadian Journal of Public Health, 92 (3), 168-172.
Shaffer, H.J.. Hall, M.N.. VanderBilt, J.. ( 1997). Estimating the prevalence of disordered gambling behavior in the United States and Canada: A research synthesis. American Journal of Public Health, 89, 1369-1376.
Smith, G.. Wynne, H.. ( 2002). Measuring gambling and problem gambling in Alberta using the Canadian Problem Gambling Index. Edmonton, AB: Alberta Gaming Research Institute.
Smith, G.. Wynne, H.. ( 2004). VLT gambling in Alberta: A preliminary analysis. Edmonton, AB: Alberta Gaming Research Institute. Retrieved August 21, 2005, from http://www.uofaweb.ualberta.ca/abgaminginstitute/pdfs/VLT_Gambling_Alberta.pdf
Williams, R.J.. ( 2003). Reliability and validity of four scales to assess gambling attitudes, gambling knowledge, gambling fallacies and ability to calculate gambling odds. Unpublished technical report. Lethbridge, Alberta. Available from author.
Wynne, H.J.. ( 2000). Gambling on the edge in Alberta. eGambling: The Electronic Journal of Gambling Issues, Issue 1, March 2000. Retrieved August 19, 2005, from http://www.camh.net/egambling/issue1/policy/
Tables
Table 2

Logistic regression of characteristics differentiating gamblers from nongamblers


Table 3

Logistic regression of characteristics differentiating problem gamblers from non-problem gamblers


Article Categories:
  • research

Keywords: gambling, problem gambling, university, students.