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Faculty Research and Publications

Williams, Robert

2006-04

Gambling and problem gambling in asample of university students
Williams, Robert J.
Centre for Addiction and Mental HealthWilliams, R. J., Connolly, D., Wood, R.T., & Nowatzki, N. (2006). Gambling and problem
gambling in a sample of university students. Journal of Gambling Issues, 16.
http://hdl.handle.net/10133/377
Downloaded from University of Lethbridge Research Repository, OPUS

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PDF version of: This Article (122 KB) | This Issue (1 MB)

Gambling and problem gambling in a sample ofuniversity students
Robert J. Williams, Alberta Gaming Research Institute,
University of Lethbridge, Lethbridge, Alberta, Canada.
E-mail: robert.williams@uleth.ca
Dennis Connolly, Department of Mathematics &Computer Science, University of Lethbridge
Robert T. Wood, Department of Sociology, University of
Lethbridge
Nadine Nowatzki, School of Health Sciences, Universityof Lethbridge

Abstract
University students from southern Alberta (n = 585) wereadministered a questionnaire to assess their gambling behaviour.
Seventy-two percent reported gambling in the past 6 months, withthe 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 nonproblem 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. Key words:
gambling, problem gambling, university, students

Introduction
The impact of the extensive availability, advertising, andfile://C:\jgi16\issue16\jgi_16_williams.html

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sanctioning of legalized gambling is of concern in the fields ofpublic health and addictions. Among adults, the prevalence ofdisordered 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 foreither 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 anenvironment 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, recklessdriving, unsafe sex, etc.) (e.g., Douglas et al., 1997). Gambling
appears no different. The lifetime rates of gambling in college anduniversity students typically range from 70% to 94%, with malesconsistently 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 thisstudy 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) inAustralia, and Rönnberg et al. (1999) in Sweden). Similarly, themeta-analysis of all North American prevalence studies found thatthe 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 andproblem gambling, much less is known about the nature ofgambling in this group. Specifically, little is known about theamount of time and money spent on gambling, the types ofgambling being played, and the characteristics differentiating
nongamblers from gamblers and gamblers from problem gamblers.
The above topics form the basis for the present study.

MethodThe sample consisted of students from the University of
Lethbridge, in Lethbridge, Alberta, Canada. Alberta has one of the

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widest arrays of gaming entertainment options available to its
citizenry of any jurisdiction in North America (Wynne, 2000), andthe city of Lethbridge has all of these options available. The
University of Lethbridge is a primarily undergraduate institution witha student body mostly from western Canada. Students wererecruited from 10 different introductory courses in statistics, history,
and sociology between September 2001 and April 2003. A 30minute gambling questionnaire was administered at the beginningof each course. Students were told that the questionnaire wasdesigned 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 universityyear;
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 asassessed by the Gambling Odds Scale;
6. gambling behaviour, i.e., type of gambling engaged in, timespent gambling, and amount of money spent gambling in thepast 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 measurespeople'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 wasdeveloped along with the Gambling Knowledge Scale, theGambling 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

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

Results
SampleOver 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. Thirtyfour 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% wereeducation 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 bodywith the exception of university year, where the sample contained agreater portion of first-year students.
Gambling behaviour
As seen in Table 1, 72.1% of the sample reported gambling in thepast 6 months. The most common types of gambling engaged inwere lotteries and instant win tickets (44%), followed by games ofskill 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).

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Table 1
Gambling behaviour in the past 6 months
Percentage
of students
involved

Averagetime
spent

Averagemoney spent72

33.7 h–$25.9344

7.0 h(76 SD)–$4.33(34.4 SD)34

17.3 h(104 SD)+$0.39(29.5 SD)

VLTs or slot
machines

29

7.3 h(87 SD)–$5.23(31.9 SD)

Casino table
games

26

15.3 h(113 SD)–$4.84(39.1 SD)

Sports betting

17

7.1 h
(86 SD)–$1.88(29.4 SD)

Bingo

8

3.8 h
(63 SD)–$2.54(23.1 SD)

Horse racing

7

2.2 h
(38 SD)–$1.21(21.7 SD)

Stock market

7

8.7 h
(93 SD)–$4.87(41.1 SD)

Other

1

0.4 h
(8 SD)–$1.72(22.4 SD)Any gambling
Lottery or
instant win
tickets
Skill games
against others

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 averagetime spent was 33.7 total hours (1.5 hours median) for all types ofgambling combined. Seven percent of students spent 40 hours ormore gambling. The types of gambling that students spent the mosttime 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 aremuch higher than the medians due to a small percentage ofgamblers with very high involvement in the activity. Median andmodal time spent was zero for each activity.
The average total amount of money reported lost on all types ofgambling in the past 6 months was $25.93 ($0 median). Elevenpercent of students reported losing more than $100, and 1%
reporting losing more than $1000. The types of gambling thatstudents spent the most money on were VLTs or slot machines($5.23), the stock market ($4.87), casino table games ($4.84), andlotteries or instant win tickets ($4.33). In all cases the medianamount of money spent was zero. The average losses are low

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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 pathologicalgambling) 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 nonproblem 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, andskill 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 impactof 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 constantonly 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 varianceaccounted 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 ofuniversity major was as follows: kinesiology/physical education(82%), management (82%), education (74%), social science (72%),
science (66%), and humanities (56%).

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Table 2
Logistic regression of characteristics differentiating gamblers
from nongamblers
Variable
Age
Ethnicity (European= reference)

Regression
coefficients (B)
.03

Asian

0.2

Odds
ratios
1.0

–

6.7

–

–

10.5*

–

–
–.46
.51
.03
.20
4.30

7.3
3.3
47.5**
0.2
4.7*
3.2

–
0.6
1.7
1.0
1.2
70.1

Wald statistics

Aboriginal
Other
Major (science =reference)Management
Social science
Humanities
Kinesiology
Education
University year
GenderGambling attitudes
Gambling fallacies
Gambling math skill
CONSTANT*p < .05; **p < .01
Characteristics differentiating problem gamblers from nonproblem 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 withmissing values for age and the 7 cases with missing values foruniversity year were imputed using linear trend at point. To reducethe impact of outliers, the students older than 27 were recoded asage 27.
A test of the full model with all eight predictors against a constant-

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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, andodds ratios for each of the eight predictors. According to the Wald
criterion, five variables reliably predicted problem gambling: morepositive 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% ofeducation majors, and 14% of management majors were problem
gamblers) (z = 14.6, p < .05), superior ability to calculate gamblingodds (z = 6.2, p < .05), and older age (z = 4.1, p < .05).
Table 3
Logistic regression of characteristics differentiating problem
gamblers from non-problem gamblers
Variable
Age
Ethnicity (European =reference)
Asian

Regression
coefficients (B)
.19

Wald
statistics
4.1*

Odds
ratios
1.2–

15.4**

–

–

14.6*

–

–
–.86
.56
–.01
.33
–7.60

6.8
3.0
23.7**
0
6.2*
0.1

–
0.4
1.7
1.0
1.4
0.001

Aboriginal
Other
Major (science =reference)Management
Social science
Humanities
Kinesiology
Education
University year
GenderGambling attitudes
Gambling fallacies
Gambling math skill
CONSTANT*p < .05; **p < .01

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Discussion
Gambling is a common activity among university students, with
72% having done so in the past 6 months. The most common typesof gambling were lotteries and instant win tickets, followed bygames 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 gamesof skill against other people and casino table games. The types ofgambling associated with the greatest spending were VLTs andslot 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 forentertainment 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 betweenjurisdiction 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 culturaland 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 gamblingcompared to the rest of the province (Smith & Wynne, 2002, 2004).
A significant minority of the student body and the population ofsouthern 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 moredifficult to make comparisons to other studies regarding time andmoney 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 notappear that a great deal of time and money is being lost togambling.
While gambling is innocuous for most, it is apparent that asignificant minority of students are heavy gamblers who experience
adverse consequences from it. Seven and one-half percent of

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students were classified as problem or pathological gamblers.
Similar to prior research, the rate of problem/pathological gamblingin 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 thepresent 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 thebest predictor of both being a gambler and being a problem
gambler. This is not an unexpected finding, although it is interestingthat 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 beenreported in previous research. However, what have beenpreviously 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 notunreasonable 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 inbusiness management degrees. Alternatively, the relationshipbetween gambling and business management interests may be
due to a common interest in making money.
The relationship between superior ability to calculate gamblingodds 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 orhigher debt loads, which might create a greater predilection togamble. The link between Asian heritage and problem gambling issomething that has been previously found in the literature (Lesieur
et al., 1991), as well as in general population surveys.

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References
Adebayo, B. (1998).
Luck of the dice: Gambling attitudes of a sample ofcommunity college students. College Student Journal, 32,
255–257.
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 oncollege students in Mississippi. College Student Affairs
Journal, 17, 81–91.
Devlin, A.S., & Peppard, D.M. (1996).
Casino use by college students. Psychological Reports, 78,
899–906.
Douglas, K.A., Collins, J.L., Warren, C., Kann, L., Gold, R.,
Clayton, S., et al. (1997).
Results from the 1995 National College Health Risk Behavior
Survey. Journal of American College Health, 46, 55–66.
Engwall, D., Hunter, R., & Steinberg, M. (2002).
Gambling and other risk behaviors on university campuses.
Retrieved February 19, 2004, fromhttp://www.responsiblegambling.org/articles/gambling_and_other _risk_behaviors_on_university.pdf
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 andtheir casino gaming behaviors. The Consortium Journal, 5, 5–
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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

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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.
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Oster, S.L., & Knapp, T.J. (1998).
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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 August21, 2005, fromhttp://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).
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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 ofdisordered gambling behavior in the United States andCanada. Canadian Journal of Public Health, 92 (3), 168–172.
Shaffer, H.J., Hall, M.N., & VanderBilt, J. (1997).

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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, fromhttp://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, fromhttp://www.camh.net/egambling/issue1/policy/
For correspondence: Dr. Robert Williams
Alberta Gaming Research Institute, 102 Anderson Hall, Universityof Lethbridge, Lethbridge, Alberta, Canada, T1K 3M4. E-mail:Robert.williams@uleth.ca
Submitted: February 29, 2004. All URLs were available at the timeof 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 allcontributed 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

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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.
Dennis Connolly, PhD is an associate professor in the Departmentof 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.

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