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Gambling, Gambling Activities, and Problem Gambling
Article in Psychology of Addictive Behaviors · July 2009
DOI: 10.1037/a0014181 · Source: PubMed
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Gambling, Gambling Activities, and Problem Gambling
Thomas Holtgraves
Ball State University
This research examined similarities and differences between gambling activities, with a particular focus
on differences in gambling frequency and rates of problem gambling. The data were from population-
based surveys conducted in Canada between 2001 and 2005. Adult respondents completed various
versions of the Canadian Problem Gambling Index (CPGI), including the Problem Gambling Severity
Index (PGSI). A factor analysis of the frequency with which different gambling activities were played
documented the existence of two clear underlying factors. One factor was comprised of Internet gambling
and betting on sports and horse races, and the other factor was comprised of lotteries, raffles, slots/Video
Lottery Terminals (VLTs), and bingo. Factor one respondents were largely men; factor two respondents
were more likely to be women and scored significantly lower on a measure of problem gambling.
Additional analyses indicated that (1) frequency of play was significantly and positively related to
problem gambling scores for all activities except raffles, (2) the relationship between problem gambling
scores and frequency of play was particularly pronounced for slots/VLTs, (3) problem gambling scores
were associated with playing a larger number of games, and (4) Internet and sports gambling had the
highest conversion rates (proportion who have tried an activity who frequently play that activity).
Keywords: problem gambling, individual differences, gambling activities
People can and do gamble on virtually anything. Currently, the
most popular gambling activities are poker, sports betting, various
types of lotteries, bingo, parimutual wagering on (horse and dog)
races, casino games such as black jack and craps, slots, and a
variety of electronic gambling machines (e.g., video poker). Day
trading stocks on the Internet is a more recent addition to this list,
although one whose popularity may have already peaked.
The gambling activities in this (partial) list vary across many
dimensions. Some activities, such as poker involve a degree of
skill; others, such as lotteries, are purely random, chance events.
Some activities, such as poker and craps, are relatively social and
involve a degree of interaction that is sometimes intense and
focused; others, such as slots, are more solitary activities and are
generally pursued as such. The speed of play varies as well, from
craps and blackjack where the outcome is immediate, to weekly
lottery drawings or wagering on sporting events where the out-
come is more delayed. Moreover, gambling allows one to present
certain identities, and a large part of that identity is the game or the
games that one chooses to play (e.g., Holtgraves, 1988).
It would be surprising if these differences between gambling
activities were unimportant, yet research on gambling has often
overlooked them (but see Kessler et al., 2008; Wong & So, 2003).
For example, problem gamblers are often treated as a homogeneous
group, and the different pathways (e.g., different gambling activities)
through which one might become a problem gambler are ignored
(Blaszczynski & Nower, 2002). This is unfortunate, because dif-
ferent gambling activities may vary in terms of the type of person
they attract, as well as the role they play in the development of
pathological gambling. Hence, it is possible that different types of
people will engage in different gambling activities with different
subsequent effects.
Gambling and Individual Differences
Are there differences between people who prefer different gam-
bling activities? Research addressing this issue has been relatively
sparse. However, there has been some research examining differ-
ences between problem gamblers and nonproblem gamblers. There
were early mixed results reported for the traits of sensation seeking
(Anderson & Brown, 1984; Kuley & Jacobs, 1988) and locus of
control (Cameron & Myers, 1966; Ladouceur & Mayrand, 1984).
More recently, however, several studies have converged on showing
that problem gamblers tend to score higher on a cluster of traits
associated with the dimensions of impulsiveness and negative emo-
tionality (Bagby et al., 2007; Slutske, Caspi, Moffitt, & Poulton,
2005). It is possible, however, that this overall profile obscures
some important differences based on preferred gambling activities.
For example, it has been argued that problem gamblers can be
classified into subgroups based on their approach to arousal: a
subgroup that uses gambling as a means of augmenting arousal and
a subgroup that uses gambling as a means of reducing arousal
(Blaszczynski & Nower, 2002). Gambling activities clearly vary in
this regard; some are simple and solitary (e.g., slots) and promote
dissociative states that can serve to reduce arousal. Others are more
complex and social (e.g., craps) and can serve to augment arousal.
In one of the few attempts to examine differences in personality
traits for players of different games, Slowo (1998) found that
people who prefer to play the more exciting casino games were
This research was supported by a grant from the Ontario Problem
Gambling Research Centre. The statistical assistance of James Jones is
gratefully acknowledged.
Correspondence concerning this article should be addressed to Thomas
Holtgraves, Department of Psychological Science, Ball State University,
Muncie, IN 47306. E-mail: 00t0holtgrav@bsu.edu
Psychology of Addictive Behaviors © 2009 American Psychological Association
2009, Vol. 23, No. 2, 295–302 0893-164X/09/$12.00 DOI: 10.1037/a0014181
295

relatively higher on extraversion traits such as activity and excite-
ment. In contrast, poker machine players were significantly higher
on anxiety. Hence, the problem gambling trait of impulsiveness
was more evident in one subset of gamblers (those preferring
fast-paced casino games), and the trait of negative emotionality
was more evident in a different subset (those who preferred poker
machines).
More recent research has documented the existence of other
differences between people who prefer different gambling activi-
ties. For example, Petry (2003) asked participants who were seek-
ing admission to a state-run gambling treatment center to indicate
their most problematic form of gambling. Five major groups
emerged (sports, horse/dog racing, cards, slots, and lottery), and
these groups differed in several ways. First, there were clear
gender differences, with sports and horse/dog racing being almost
exclusively men and slot players twice as likely to be women.
Second, these groups differed in terms of gambling frequency
(lottery players gambled the most frequently and card players the
least) and amount of money gambled (lottery players the least and
horse/dog race gamblers the most). Finally, there were differences
in terms of substance abuse (substance abuse, especially alcohol,
was more common among sports betters) and psychiatric variables
(sports and card gamblers had fewer problems than the other
groups).
Differences Between Gambling Activities
Rather than focusing on differences between people who play
different games, it is possible to focus on differences between the
games themselves. One manifestation of this approach is the
argument that participation in some gambling activities is more
likely to result in problem gambling than participation in other
gambling activities. It has been argued, for example, that Elec-
tronic Gambling Machines (EGMs) are highly addictive (Produc-
tivity Commission, 1999). In this survey, conducted in Australia, it
was estimated that 22.6% of regular EGM gamblers had a signif-
icant gambling problem, a rate comparable to casino table games
(23.8%) but higher than racing (14.7%) and far higher than lotter-
ies (2.5%). It is very difficult to determine unambiguously the
addictive potential of a game, however. For example, high-
problem gambling rates for EGM players could be the result of
their playing other gambling activities. One alternative measure is
to compute the percentage of gamblers indicating that an activity
is their favorite (based on amount of money spent) who are
problem gamblers. With this measure, people who preferred play-
ing EGMs had the highest problem gambling rate (9.7%), followed
by racing (5.2%), casino gambling (3.5%), and lotteries (.3%).
Another measure is the weekly conversion rate, or percentage of
people who have played an activity who report playing that activ-
ity weekly. In the Productivity Commission report (1999), this rate
was 11.06% for EGMs, a rate lower than that for lotteries (48.5%)
but greater than that for casino table games (2.4%).1
And another
measure is the percentage of problem and nonproblem gamblers
who engage in any particular activity. Not surprisingly, problem
gamblers are more likely to play EGMs than are nonproblem
gamblers (Smith & Wynne, 2004; Wynne, 2002), although this
finding is true for most gambling activities. Still, relative to other
activities, EGMs have been rated as one of the most popular
weekly activities for problem gamblers but not for nonproblem
gamblers (Volberg, 1997; Wynne, 2002). Taken together, these
measures suggest a relatively high addictive potential for EGMs.2
Present Research
Prior research suggests that individuals who prefer, or at least
more frequently play, different gambling activities differ from one
another in some important ways. The purpose of the present
research was to explore these and other differences (and similar-
ities) in more detail. More specifically, in this research I pursued
the following two major issues. First, is there an underlying
structure for different gambling activities based on the frequency
with which they are played? In other words, do gambling activities
cluster together in any sort of meaningful way? For example, are
people more likely to play slot machines if they play the lottery
versus if they bet on sports? This type of analysis will be useful for
identifying similarities and differences between gambling activi-
ties, as well as the role played by these underlying dimensions in
the initiation and development of gambling and problem gambling.
Second, to what extent are different gambling activities associated
with different rates of problem gambling? This is obviously an im-
portant question, but one that is not amenable to a single, straightfor-
ward analysis. There are no completely unambiguous measures in this
regard. Accordingly, in this research I used a variety of different
analyses and searched for common patterns across these analyses.
First, I examined differences between gambling activities in terms of
their conversion rates and levels of problem gambling. Second, I
focused on differences between people in terms of their problem
gambling status, and whether these differences were associated with
preferences for certain gambling activities and with the number of
gambling activities that one played.
To examine these issues, I used a large, integrated data set
comprised of responses to gambling surveys conducted in several
Canadian provinces between 2001 and 2005. The use of this type
of population-based survey data is important because participants
in many studies in this area have been problem gamblers seeking
treatment (e.g., Petry, 2003). Hence, there is a clear need to
explore these differences with population-based data.
Method
Sample
The integrated data set for this study was made available by the
Ontario Problem Gambling Research Centre. This data set con-
sisted of responses to telephone surveys regarding gambling that
had been conducted in several different Canadian provinces be-
tween 2001 and 2005. Respondents were adults (18 or 19 years of
1
Conversion rates are particularly susceptible to the availability of an
activity and hence should be interpreted with caution.
2
There has been some dispute over these findings, however (e.g.,
Dowling, Smith, & Thomas, 2005). Most notably, Mizerski and colleagues
(Mizerski, Jolley, & Mizerski, 2002) have argued that the high percentage
of people who are heavy users of EGMs is no different from the distribu-
tion of heavy-use consumers for any consumer product (i.e., 80% of use is
accounted for by 20% of the users). This analysis, however, does not
involve different gambling activity comparisons, only EGM distributions
versus expected consumer behavioral distributions.
296 HOLTGRAVES

age or older) randomly selected with various constraints (e.g.,
stratified by region) in order to approximate the demographic
breakdown for that area. Some of the surveys weighted their
sample based on certain demographic considerations. However,
the assignment of weights was not consistent over these different
surveys and hence they were not used in the present analyses. The
sample size of the combined data set was 21,374. Of these respon-
dents, 12,299 had gambled at least once during the past 12 months
and hence were eligible for inclusion in the present analyses.
Information regarding the procedures used for each survey is
presented in Table 1. Included in this table are references and
URLs for each survey (all survey reports are available online).
Measures
Although each survey was created and conducted indepen-
dently, the survey protocol always consisted of a version of the
Canadian Problem Gambling Index (CPGI), a comprehensive set
of questions regarding participation in a variety of gambling ac-
tivities, as well as background questions, substance abuse issues
and a variety of additional demographic variables. There were
some differences between the surveys in terms of the content and
wording of the CPGI items. However, all analyses reported here
are based on identically worded questions (the specific wording for
all analyzed items is given in the Results section).
One important component of the CPGI is a scale designed to
assess problem gambling. This scale consists of nine items and is
referred to as the Problem Gambling Severity Index (PGSI). Each
of the surveys, with one exception, contained these nine items
worded in an identical manner. The one exception was the Na-
tional survey in which a dichotomous (rather than four-response)
format was used for two of the PGSI items. This survey was
excluded from all analyses that included the PGSI. The PGSI was
designed to measure a single, problem gambling construct in a
general population rather than in a clinical context (unlike its
Table 1
Surveys Included in Combined Data Set
Survey province: Alberta
Survey year: 2002
Sample size: 1,804
Reference: Smith & Wynne (2002)
Online report: https://dspace.ucalgary.ca/bitstream/1880/1626/1/
gambling_alberta_cpgi.pdf
Conducted by: The University of Alberta’s Population Research Lab
Stratified: By region
Sampling households: Random-digit dialing
Sampling within households: Adult with most recent birthday
Reported response rate: 63.6%
Margin of error (95% CI at maximum variance): 2.3%
Survey province: Ontario
Survey year: 2001
Sample size: 4,631
Reference: Wiebe, Single, & Falkowski-Ham (2001)
Online report: http://www.responsiblegambling.org/articles/
CPGI_report-Dec4.pdf
Conducted by: Viewpoints Research Inc.
Stratified: By region, gender, age
Reported response rate: 37%
Survey province: Ontario
Survey year: 2005
Sample size: 3,604
Reference: Wiebe, Mun, & Kauffman (2006)
Online report: http://www.gamblingresearch.org/download.sz/bib
.pdf?docid ϭ 7670
Conducted by: Hitachi Survey Research Centre in the Department of
Sociology at the University of Toronto at Mississauga
Reported response rates: (strict) 82.5% (optimal); 46.4%
Margin of error: Not reported
Survey Province: Manitoba
Survey year: 2001
Sample Size: 3,119
Reference: Patton, Dhaliwal, Pankratz, & Broszeit (2002)
Online report: http://www.afm.mb.ca/pdf/FinalGamblingReport_
Full_.pdf
Conducted by: Addictions Foundation of Manitoba
Reported response rate: 40.7%
Survey province: British Columbia
Survey year: 2002
Sample Size: 2,500
Reference: Ipsos-Reid & Gemini Research (2003)
Online report: http://www.bcresponsiblegambling.ca/responsible/
bcprobgambstudy.pdf
Conducted by: Ipsos-Reid
Margin of error (95% CI at maximum variance): 2%
Reported response rate: 27%
Survey province: Newfoundland
Survey year: 2005
Sample size: 2,596
Reference: Market Quest Research Group (2005)
Online report: http://www.health.gov.nl.ca/health/commhlth_old/
gambling_report_nov21.pdf
Conducted by: Market Quest Research Group
Stratified: By region, gender
Sampling within households: Adult (19 years and older) with most
recent birthday
Reported response rate: 25.4% (computed following Scenario C of the
Marketing Research and Intelligence Association [MRIA] of
Canada)
Survey province: National
Survey year: 2001
Sample size: 3,120
Reference: Ferris & Wynne (2001)
Online report: http://www.ccsa.ca/nr/rdonlyres/58bd1aa0–047a-41ec-
906e-87f8ff46c91b/0/ccsa0088052001.pdf
Conducted by: Institute for Social Research at York University
Reported response rate: Not reported
CI: Not reported
Note. CI ϭ confidence interval.
297GAMBLING, GAMBLING ACTIVITIES, AND PROBLEM GAMBLING

nearest competitor, the South Oaks Gambling Screen). The mea-
sure was theoretically derived and assesses problem gambling
behaviors (e.g., Chasing: “How often have you gone back another
day to try to win back the money you lost?) and the occurrence of
adverse consequences of gambling (e.g., Guilt: “How often have
you felt guilty about the way you gamble or what happens when
you gamble?”). For each of the nine items, respondents answer on
a four-alternative scale: 0 ϭ never; 1 ϭ sometimes; 2 ϭ most of the
time; 3 ϭ Almost always. Responses were summed and, following
convention, respondents were classified into gambling subtypes
based on their PGSI scores as follows: 0 ϭ nonproblem gambler;
1–2 ϭ low-risk gambler; 3–7 ϭ moderate-risk gambler; 8 and
over ϭ problem gambler.
Although the PGSI was developed independently, there is some
item overlap with the SOGS. Research on the PGSI indicates that
it has adequate internal consistency and test–retest reliability (Fer-
ris & Wynne, 2001), and that it assesses a single, underlying
problem gambling construct that is correlated with various gam-
bling behaviors (Holtgraves, in press).
Results
Unless otherwise noted, all reported analyses were based on
participants who indicated that they had gambled at least once in
the past 12 months. All analyses were performed with SPSS
(version 15). The Ns varied over analyses, because not all ques-
tions were included in all surveys, and because analyses differed in
their inclusion criteria.
Factor Analysis of Gambling Frequency
The first issue concerned the possibility that there is an under-
lying structure to the frequency with which gambling activities are
played. A principal components analysis (PCA) of the frequency
(0 ϭ never; 1 ϭ less than once a month; 2 ϭ at least once a month;
3 ϭ at least once a week; 4 ϭ daily) with which respondents
reported engaging in eight different gambling activities (lottery,
horse race betting, Internet gambling, bingo, raffles, sport select,
slots/VLTs, bookie)3
was conducted on the sample of respondents
with nonmissing data on these eight activities (N ϭ 10,685).
Because responses to the PGSI constituted ordinal data, a poly-
choric correlation matrix was first constructed and used as matrix
input for the factor analysis (Gilley & Uhlig, 1993; Joreskog &
Moustaki, 2000). In this analysis, the PCA yielded two factors with
eigenvalues greater than one (3.28 and 1.35). Together, these two
factors accounted for 57.9% of the variance.
The factor structure (varimax rotation) that emerged was very
clear (see Table 2), with horse racing, sport select, Internet, and
bookie wagering all loading highly on the first factor and lottery,
bingo, slots/VLTs, and raffles all loading highly on the second
factor. All loadings were greater than .61 on the primary factor and
less than .3 on the secondary factor. This structure suggests that
certain people prefer to play certain types of games, one group that
prefers to play lottery, bingo, slots, and raffles, and a second group
that prefers gambling on the Internet, horse races, and sports.4
I conducted exploratory analyses of the characteristics of re-
spondents based on within which factor their favorite gambling
activity fell. Favorite gambling activity was defined as the gam-
bling activity played more frequently than any other gambling
activity. The majority of the factor one group were men (78%), and
the majority of the factor two group were women (52%). In
addition, mean scores on the PGSI were significantly higher for the
factor one group (M ϭ .867) than for the factor two group (M ϭ
.397), F(1, 4084) ϭ 15.83, p Ͻ .001.5
Importantly, this difference
occurred for both men (1.12 vs. .46) and women (.61 vs. .34),
demonstrating that the problem gambling difference between fac-
tors is not a function of greater male participation in factor one and
female participation in factor two; the Factor ϫ Gender interaction
was not significant, F(1, 4084) ϭ 2.82, p Ͼ .09.
Differences Between Gambling Activities
Conversion rates. Conversion rates provide an estimate of the
likelihood that individuals who try a particular gambling activity
will come to frequently engage in that activity. Hence, conversion
rates are estimated by computing the ratio of frequent players to
players who have ever played an activity. The conversion rates
(weekly or more) for eight activities were computed and are
presented in Table 3. The numbers in this table refer to the
percentage of players who report having ever played an activity
who currently play that activity at least weekly. For example,
11.95 % of people who have played some form of lotto in the past
12 months play lotteries weekly or more frequently. In contrast,
the conversion rate for raffles is 1.87. The activities clearly differ
in their conversion rates. Most notably, there is a group of three
activities that have conversion rates significantly greater than the
other activities: bookie, sport select, and Internet. This means that
people who have tried these three activities are far more likely to
be frequent players than people who have tried other activities.
Problem gambling differences. This analysis was concerned
with whether there were differences between gambling activities in
terms of problem gambling rates. For example, what percentage of
people for whom activity X is their favorite gambling activity are
3
Casino gambling was not included in this list because items that
assessed it were not included in some surveys, and because surveys that did
include such items assessed different aspects of casino gambling.
4
Factor analyses were conducted separately for men, and the results
were identical to the overall analysis. It was not possible to conduct a
separate analysis for women because the distribution of gambling frequen-
cies prevented the generation of a polychoric correlation matrix.
5
The sample size is reduced because of missing responses on PGSI
items and because of the exclusion of respondents with multiple favorite
activities.
Table 2
Factor Structure (Loadings) for Frequency of Engaging in
Different Gambling Activities
Activity Factor 1 Factor 2
Sport select .755 .220
Internet .725 .044
Bookie .880 .133
Horse racing .704 .267
Lottery .162 .775
Raffle .112 .606
Bingo .057 .736
Slots/VLT .296 .700
298 HOLTGRAVES

classified as nonproblem gamblers, low-risk gamblers, moderate-
risk gamblers, and problem gamblers? First, the distribution of
gambling subtypes as a function of favorite gambling activity (as
defined earlier) was computed and the results are presented in
Table 4. A ␹2
analysis indicated that the distribution of gambling
subtypes varied over gambling activities, ␹2
(21, N ϭ4088) ϭ
334.59, p Ͻ .0001. As can be seen in Table 4, rates of nonprob-
lematic gambling were relatively high (Ͼ78%) for lotteries, raf-
fles, and horse race betting; moderate for bingo and slots/VLTs;
and low (Ͻ64%) for sport select, Internet and bookies, with
nonproblem gambling 50% or less for the latter two activities.
A one-way analysis of variance (ANOVA) was then conducted
with favorite activity as the independent variable and PGSI score
as the dependent measure. Consistent with the ␹2
test, PGSI scores
varied significantly as a function of favorite activity, F(7,80.78) ϭ
22.25, p Ͻ .001.6
These results can be seen in the far right column
in Table 4. Post hoc tests using the Dunnett’s C procedure indi-
cated that people for whom playing raffles was the favorite activity
had significantly lower PGSI scores than participants whose fa-
vorite activity was playing slots, lotto, bingo, or sport select. PGSI
scores were elevated for those who favored the Internet and
bookies but the differences were not significant at p Ͻ .05.
Two parallel analyses were then conducted to examine problem
gambling as a function of frequent participation (weekly or more)
in a gambling activity (regardless of whether the activity was a
person’s favorite). As can be seen in Table 5, the results generally
paralleled the findings for favorite activity. A ␹2
analysis indicated
that the distribution of gambling subtypes varied over gambling
activities, ␹2
(21, N ϭ933) ϭ 101.28, p Ͻ .0001. Again, problem
gambling rates were lowest for raffles and highest for gambling
with bookies. There were some differences, however, between the
results of this analysis and those that classified participants based
on their favorite activity. Most notable was the relatively high
problem gambling rate for people who play slots/VLTs on a
frequent basis. Fully 66% of those engaging in this activity on at
least a weekly basis are low risk or greater. This suggests that
frequent slot/VLT play is accompanied by the more frequent
playing of another gambling activity (which is then the favorite).
An ANOVA on PGSI scores (see far right column in Table 5) was
consistent with this pattern and significant, F(7, 84.86) ϭ 3.62,
p Ͻ .01.7
PGSI scores were quite elevated (and significantly
different from other participants, with the exception of frequent
Internet and bookie gambling) for people who frequently played
slots/VLTs. PGSI scores were elevated for frequent Internet and
bookie gamblers, but not significantly greater than other participants.
Differences Between People
Several issues regarding gambling differences as a function of
gambling subtype were examined. First, do problem gamblers
focus and play only one activity as some have previously sug-
gested (e.g., Breen & Zimmerman, 2002; Petry, 2003)? Or are they
more likely to play multiple games (Kessler et al., 2008)? A
composite gambling measure was created representing the number
of the eight different gambling activities one had engaged in
during the past 12 months. This measure was analyzed as a
function of gambling subtype. There was a significant effect of
gambling type for this measure, F(3, 2900) ϭ 75.94, p Ͻ .001, and
follow-up post-hoc tests indicated that all means were significantly
different (p Ͻ .05) from one another, with the exception of the
difference between moderate risk and problem gamblers. As can
be seen in figure 1, there is an increase in the number of gambling
activities that individuals engage in as problem gambling severity
increases. In short, it does not appear to be the case that problem
gamblers concentrate on a single game (at least relative to less
problematic gamblers).
One potential limitation with the prior analyses is that partici-
pants often play multiple games, thereby making it difficult to
isolate the relationship between individual games and problem
gambling. To help overcome this, a multiple regression analysis
was conducted in which total PGSI score was treated as the
criterion variable and frequency of play for each gambling activity
served as the predictor variables. All variables were entered si-
6
The variances differed over gambling groups (and the size of each
group differed widely), and Levene’s test for the equality of variances was
significant (p Ͻ .01). Because of this violation of the homogeneity of
variance assumption, Welch’s F was computed for the omnibus test and
Dunnett’s C for all post hoc comparisons.
7
The sample size is reduced because of missing responses on PGSI
items and because of the exclusion of respondents with multiple favorite
activities.
Table 3
Conversion Rates for Eight Gambling Activities
Activity (n)
Conversion rate
(weekly or greater)a
95% Confidence
interval
Lotto (4,845) 11.95 11.08–12.89
Raffles (9,030) 1.87 1.60–2.17
Bingo (1,939) 17.48 15.85–19.23
Slots/VLT (3,372) 2.76 2.26–3.37
Horse Race (913) 5.15 3.90–6.78
Sport select (849) 23.20 20.49–26.16
Bookie (53) 30.19 19.52–43.54
Internet (149) 20.81 15.07–28.02
Note. a
Values represent the percentage of people who have played an
activity who play that activity weekly or more.
Table 4
Distribution of Gambling Subtypes and Mean Problem
Gambling Severity Index (PGSI Scores) for Each Favored
Gambling Activity
Activity (n)
Gambling subtype, %
PGSINonproblem Low risk
Moderate
risk Problem
Lottery (1,221) 81 12 6 1 .54b
Raffle (1,915) 93 6 1 Ͻ1 .13a
Horses (71) 78 10 11 1 .77ab
Bingo (214) 67 22 10 1 .87b
Slots/VLT (469) 74 16 8 2 .85b
Sport select (160) 64 27 6 3 .92b
Internet (32) 50 34 12 3 1.56ab
Bookie (6) 17 50 17 17 3.33ab
Note. Means without a superscript in common are significantly different
at p Ͻ .05 using Dunnett’s C.

multaneously, thereby assessing the effects of each game control-
ling for the effects of all other games. The results are reported in
Table 6. The overall model was significant, R ϭ .335, F(8,
7404) ϭ 117.17, p Ͻ .001. Each gambling activity, with the
exception of playing raffles, contributed positively and signifi-
cantly to the problem gambling scores. The size of the beta weights
was relatively modest and ranged between .18 and .05. Consistent
with the analysis of frequently played games, frequency of playing
slots/VLTs was the activity most highly associated with problem
gambling. Importantly, in this case the slots–problem gambling
relationship held when the effects of other gambling activities
were controlled.
Discussion
Limitations of the present research should be noted at the outset.
First, the response rates for some of the surveys were relatively
low, although generally comparable to rates typically reported for
gambling surveys (Gemini Research, 1994; Shaffer, Hall, &
Vander Bilt, 1997). Second, the surveys differed in various ways
(although not the specific items that were analyzed in this report)
and were conducted for different reasons. Moreover, the surveys
were conducted in different locales that varied in terms of gam-
bling availability. Third, consistent with other research (e.g.,
Kessler et al., 2008), the occurrence of problem gambling was
relatively infrequent, and breaking down problem gambling as a
function of favored gambling activity reduced the sample even
more, thus resulting in some data instability. Within the context of
these limitations, the present research generated several important
findings.
First, even though gambling activities vary on a range of di-
mensions, there do appear to be some underlying similarities, so
much so that two clearly defined groups were identified in this
research. One group is comprised of Internet gambling and betting
on sports and horse races. The other group is comprised of slots/
VLTs, raffles, lotteries, and bingo. These activities vary on a
number of dimensions, but the group two activities generally tend
to be low-wager activities (i.e., the amount that can be wagered on
any single outcome is relatively low), relative to group one, which
typically allows for much higher wagers, and this may partially
account for the high problem gambling score for the former group
relative to the latter. Even though problem gambling scores were
lower for group two, there was one activity within this group—
slots/VLT— that was associated with higher rates of problem
gambling (as will be discussed subsequently). Note also that the
gender difference (i.e., more women in group two and men in
group one) is consistent with other reports of gender differences in
gambling preferences (e.g., Petry, 2003).
Second, consistent with the difference in problem gambling
scores for the two gambling groups, there were differences be-
tween gambling activities in terms of their conversion rates. Spe-
cifically, conversion rates for Internet gambling and sports betting
(both sport select and betting with bookies) were far higher than
they were for the other activities. The percentage of people who
tried gambling on the Internet or sports betting and continued to
gamble frequently on the Internet or sports was very high. There is
no doubt, however, that conversion rates are influenced by gam-
bling availability. It is not possible to gamble frequently on an
activity if that activity is not available or is difficult to access. But
to a certain extent that is just the point. Increased availability will
Table 5
Distribution of Gambling Subtypes and Mean (SD) Problem
Gambling Severity Index (PGSI) Scores for Gambling Activities
That Are Played Frequently (at Least Weekly)
Activity (n)
Gambling subtype, %
PGSINonproblem
Low
risk
Moderate
risk Problem
Lottery (287) 69 18 9 4 1.0b
(2.44)
Raffle (114) 77 13 7 3 .82b
(2.53)
Horses (30) 63 23 10 3 1.0b
(2.17)
Bingo (258) 63 22 12 3 1.19b
(2.56)
Slots/VLT (68) 34 20 28 18 4.50a
(6.44)
Sport Select (141) 54 30 12 4 1.36b
(2.56)
Internet (24) 42 38 17 4 1.96ab
(4.44)
Bookie (8) 0 63 13 25 4.75ab
(5.8)
Note. Means without a superscript in common are significantly different
at p Ͻ .05 using Dunnett’s C. PGSI SDs are presented in parentheses.
PROBLEMMODERATEAT RISKNON-PROBLEM
GAMBLER
Meanofnumberofdifferentgames
2.50
2.00
1.50
Figure 1. Number of different games played as a function of problem
gambling subtype.
Table 6
Multiple Regression Analysis: Problem Gambling Severity Index
(PGSI) Scores Predicted by Frequency of Gambling Activity
Activity r B t
Lottery .152‫ء‬
.08 7.00‫ء‬
Raffle Ϫ.004 Ϫ.03 Ϫ3.02‫ء‬
Horses .128‫ء‬
.05 4.06‫ء‬
Bingo .161‫ء‬
.11 9.53‫ء‬
Slots/VLT .240‫ء‬
.18 15.82‫ء‬
Sport select .147‫ء‬
.08 7.26‫ء‬
Internet .107‫ء‬
.07 6.30‫ء‬
Bookie .141‫ء‬
.10 8.43‫ء‬
‫ء‬
p Ͻ .01.
300 HOLTGRAVES

result in increased gambling. For example, nothing is more avail-
able than the Internet; a player doesn’t even need to leave home.
And although the difference was not significant in the present
study, other researchers have reported elevated problem gambling
scores (on the SOGS) for people who gamble on the Internet (Ladd
& Petry, 2002).
Third, the relationship between problem gambling (as assessed
with the PGSI) and gambling activity varied as well. The major
difference was this. People who play raffles more frequently than
other games had significantly lower problem gambling scores than
people who preferred other games. This occurred when problem
gambling was treated as a categorical variable and when it was
treated as a continuous variable. And when problem gambling was
treated as a continuous variable, every gambling activity, except
raffles, contributed positively, independently, and significantly, to
problem gambling scores. In other words, more frequent playing of
any game was associated with increased problem gambling scores.
Fourth, some of the analyses suggest that frequent slots/VLTs
play is associated with increased problem gambling. Almost two-
thirds of the people who played slots/VLT on a weekly basis were
low risk or greater. And in the multiple regression analysis, slots/
VLTs had the largest beta weight of all gambling activities. These
data are consistent with the general findings of the Productivity
Commission (1999) regarding the enhanced addictive potential of
video gambling. Note that in the present research the slots/VLT
category is broader than the video gambling category considered
by the Productivity Commission (1999). Note also that the rela-
tionship between problem gambling and slots/VLTs occurred for
the frequency measures but not the favorite activity measure.
Problem gambling (low risk or greater) is heightened for people
who play slots/VLTs frequently (independent of whether it is their
most frequently played activity). And of course this is consistent
with the finding that problem gambling scores were positively
correlated with the number of different activities played.
Still, there must also be something about slots/VLTs that is
contributing to problem gambling, because for no other activity
was there such a large discrepancy in problem gambling rates
between the favored and frequently played analyses. Likely vari-
ables in this regard include playing speeds and payout interval
(Griffiths, 1993, 1999). The payout interval for slots is very short,
almost instantaneous; for raffles and lotteries it is much longer.
Even for casino games such as craps and blackjack the payout
interval tends to be longer because of the presence of other people.
Short intervals facilitate chasing, one of the defining features (if
not the most defining feature) of problem gambling. Quick pay-
outs, combined with fast playing speeds, may facilitate behavior
characteristic of problem gambling. Subsequent research attempt-
ing to identify those features of gambling activities that play
substantial roles in problem gambling is warranted.
The present research documented the existence of differing rates
of frequent gambling (conversion rates) and problem gambling for
different gambling activities. As with all correlational research,
however, the causal direction is unknown. People are not randomly
assigned to play different gambling activities. As a result, there
may be certain types of people inclined to participate in certain
activities, and it is that inclination that is critical rather than
anything about the activities themselves. But that seems too simple
as well. Gambling activities differ in their affordances, in what
they provide for those who choose to play them. Some activities
(bingo) offer a chance to socialize, other activities (craps) a chance
for intense and focused excitement. And people choose to play and
to continue to play those activities that mesh well with their
personalities (in addition to the activity’s availability, affordabil-
ity, and so on). So it is largely a Person ϫ Situation interaction that
will account for these patterns, an interaction that has yet to be
investigated in any systematic way.
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Received May 21, 2008
Revision received September 15, 2008
Accepted September 17, 2008 Ⅲ
302 HOLTGRAVES
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