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Cognitive distortions as a problemgambling risk factor in InternetgamblingTerri-Lynn MacKay a & David C. Hodgins aa Addictive Behaviours Laboratory, Department of Psychology ,University of Calgary , CanadaPublished online: 19 Jan 2012.

To cite this article: Terri-Lynn MacKay & David C. Hodgins (2012) Cognitive distortions as a problemgambling risk factor in Internet gambling, International Gambling Studies, 12:2, 163-175, DOI:10.1080/14459795.2011.648652

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Cognitive distortions as a problem gambling risk factor in Internetgambling

Terri-Lynn MacKay* and David C. Hodgins

Addictive Behaviours Laboratory, Department of Psychology, University of Calgary, Canada

(Received 23 May 2011; final version received 7 December 2011)

The purpose of this study was to examine the role of cognitive distortions in Internetgambling. The primary objectives were to determine whether cognitive distortionspredict Internet gambling and investigate whether distorted gambling-relatedcognitions are associated with problem gambling severity among online gamblers.Three hundred and seventy four undergraduate participants (143 online gamblers, 172males) completed an online questionnaire looking at demographics, play-relatedvariables (duration, frequency and expenditures of play) and cognitive distortions.Variables were entered into a logistic regression model to predict online gambling.Three variables made independent contributions to predicting Internet gambling: malegender, higher frequency of play, and cognitive distortions. A hierarchical linearregression analysis with Internet gamblers revealed that cognitive distortionsaccounted for a proportion of the variance in problem gambling severity beyondvariance accounted for by demographic variables and level of gambling involvement.Results suggest that cognitive distortions are a risk factor in online gambling.

Keywords: Internet gambling; problem gambling; cognition; irrational beliefs;erroneous beliefs

Introduction

Over the past 20 years, gambling availability has increased dramatically and the level of

commercial global growth is qualitatively different from what has been seen in the past.

The omnipresence of contemporary gambling is being influenced by technology and the

most recent advancement is for gamblers to access games via the Internet. There has been

a dearth of empirical studies into the sequelae of Internet gambling despite extant literature

showing that online gamblers are more likely to report disordered gambling behaviour

when compared to gamblers that have never placed a wager on the Internet (Griffiths &

Barnes, 2008; Griffiths, Wardle, Orford, Sproston, & Erens, 2009; Ladd & Petry, 2002;

Olason et al., 2011; Petry, 2006; Petry & Weinstock, 2007; Potenza et al., 2011; Wood &

Williams, 2009).

Cognitive factors have been implicated as contributing to problem gambling and could

play a role in influencing the higher rates of disordered gambling observed in online

players. The different types of cognitive distortions that may exert an influence on

gambling behaviour have been categorized by: perceived skill, superstitions or ability to

control outcomes, selective recall, flawed perceptions of randomness and attributional

distortions (Blaszczynski & Nower, 2007; Hodgins & Holub, 2007). Research has shown

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*Corresponding author. Email: tlmackay@ucalgary.ca

International Gambling Studies

Vol. 12, No. 2, August 2012, 163–175

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that 70% of vocalizations during gambling situations contain erroneous beliefs (Gaboury

& Ladouceur, 1989) and irrational cognitions account for up to 75% of thoughts while

gambling (Delfabbro & Winefield, 2000). When examining a variety of gambling-related

cognitive heuristics, researchers have found that pathological gamblers have a higher

percentage of verbalizations related to luck and skill when compared to recreational

gamblers (Baboushkin, Hardoon, Derevensky, & Gupta, 2001). Previous research has

demonstrated that problem gamblers have an inflated perception of their own skill

(Toneatto, Blitz-Miller, Calderwood, Dragonetti, & Tsanos, 1997) and endorse more

irrational beliefs about gambling when compared to social gamblers (Joukhador,

MacCallum, & Blaszczynski, 2003).

Research has shown that certain types of gambling activities are more likely to

encourage misperceptions about control and foster erroneous beliefs. Lund (2011) found

that game preference moderated the relationship between irrational thinking and gambling

frequency, with a stronger effect for such formats as Internet gambling. There are many

aspects of online gambling that could perpetuate distorted cognitions. Parke and Griffiths

(2006) have described how structural characteristics of the gambling medium could

decrease the salience of losses and lead players to have distorted perceptions about

winning. Cole, Barratt, and Griffiths (2011) found that gamblers placed higher bets and

made riskier bets in the online environment than in a casino environment. They noted that

gamblers vocalized less salience in using virtual roulette chips online when compared to

gambling with actual chips in a casino. They also noted a higher event frequency in online

games (i.e. games were faster) that could have contributed to the Internet gamblers placing

higher wagers. Internet software providers increase player engagement by creating real

life gaming environments (e.g. pkr.com, partycasino.com), which could increase a

gambler’s belief in their ability to control outcomes when playing simulated games. For

example, a player may feel a stronger sense of mastery when gambling on the Internet

versus in a casino environment or tournament play because unlike the casino, there are less

external stimuli for distraction and the player has command over the pace and timing of

play. Experience and ease with the online medium could lead players to believe that they

have more control than they actually do. It has been shown that familiarity with the

gambling modality perpetuates mistaken confidence in ability (Cantinotti, Ladouceur, &

Jacques, 2004).

Many sites offer the individual the opportunity to learn and practise games prior to

wagering money. Success and increased familiarity with practice sites could lead players

to a distorted sense of control. Demonstration modes have been shown to offer higher odds

than the actual games (Sevigny, Cloutier, Pelletier, & Ladouceur, 2005) which could give

players a false perception of being adept because they would be more likely to experience

wins in the demonstration mode than on the actual game. Experimental research has

shown that gamblers who have experienced a win have higher levels of erroneous beliefs

when compared to losing players (Monaghan, Blaszczynski, & Nower, 2008). Research

has also demonstrated that near wins and frequent wins contribute to irrational thinking in

gamblers (Ladouceur, Gaboury, Dumont, & Rochette, 1988) and are associated with

problem gambling (Turner, Zangeneh, & Littman-Sharpe, 2006). Advertising for online

sites could potentially serve to inflate a player’s sense of control by insinuating that

average players will experience monetary success. Research has shown that advertising

can trigger the urge to gamble (Grant & Kim, 2001). Advertising for Internet gambling

sites may be particularly persuasive in jurisdictions where offline gambling venues are

prohibited from advertising and consumers are unaccustomed to promotional gambling

messages. Moreover, advertising of demonstration sites may not be subject to the same

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restrictions as real-money sites, and messages about a player’s chances for success could

be misleading.

While no studies have specifically addressed the role of distorted cognitions with

Internet gamblers, Cotte and Latour (2009) conducted a qualitative investigation to

explore differences between those individuals who gamble online and those who gamble

in casinos. Although the results may not be generalizable due to the small sample size,

their findings warrant consideration. They concluded that online gamblers perceive that

they have greater control over their environment, their finances and the outcome of the

games when compared to casino gamblers or themselves in a casino environment. The

authors described that this sense of control often leads to playing for longer periods of

time. The authors noted, ‘There exists among online gamblers a dangerous illusion of

control over outcomes, monetary spending and time commitment’ (p. 751). They found

that online gamblers were more inclined to play purely for the challenge and monetary

incentive than casino gamblers who described various motivations for play (e.g. social

aspect). This finding is an important consideration because research has shown that

distorted cognitions can moderate the relationship between risky gambling practices and

spending, and risky gambling practices and tolerance (Miller & Currie, 2008). If Internet

gamblers are engaging in risky practices (such as spending more time and money

gambling) then distorted cognitions could be influencing the relationship with problem

gambling severity. It may be the case that problem gambling rates are higher among

Internet gamblers because those individuals differ in significant ways from individuals

who would opt to only gamble in land-based establishments. Shaffer and colleagues have

argued that the Internet is not inherently addictive, contending that it is the interplay of the

individual with the activity that determines level of involvement (Shaffer, 1996; Shaffer,

Hall, & Vander Bilt, 2000).

Only a handful of studies to date have directly compared samples of Internet and non-

Internet gamblers to examine variables differentiating the two populations. Germane to the

current investigation is research showing that Internet gamblers are more likely to be male

(Griffiths & Barnes, 2008; Griffiths, Wardle, et al., 2009; Wong, 2010), younger (Ladd &

Petry, 2002; Griffiths, Wardle, et al., 2009) and to gamble at a greater frequency (Griffiths

& Barnes, 2008; Wood & Williams, 2009) when compared to non-Internet gamblers.

A small number of investigations have reported factors associated with problem gambling

severity in Internet gambling. Research has shown that online problem gambling is

associated with gambling on multiple activities (Lloyd et al., 2010; Wood & Williams,

2009), higher frequency or longer play duration (Griffiths, Parke, Wood, & Rigbye, 2009;

Hopley & Nicki, 2010; McBride, & Derevensky, 2009), higher expenditures (McBride &

Derevensky, 2009; Wood & Williams, 2009) and a greater number of gambling fallacies

(Wood & Williams, 2009).

The primary objectives of this study were to determine whether cognitive distortions

predict Internet gambling involvement and to investigate whether distorted gambling-

related cognitions are associated with problem gambling severity among online gamblers.

It was hypothesized that cognitive distortions would independently predict online from

offline gamblers and would account for a proportion of variance in problem gambling

severity. Gender and age were included as covariates because they have been shown to be

associated with Internet gambling (Griffiths & Barnes, 2008; Griffiths, Wardle, et al.,

2009; Ladd & Petry, 2002; Wong, 2010). Similarly, the level of involvement in gambling

on all formats (frequency, duration, expenditures) was included as covaraites because

previous research has shown that heavily involved gamblers are more likely to wager

online (Griffiths & Barnes, 2008; Wood & Williams, 2009). The Gambler’s Beliefs

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Questionnaire (Steenbergh, Meyers, May, & Whelan, 2002) was chosen as the measure of

cognitive distortions because it is a commonly used measure of gambling-related

distortions grounded in theory, reviewed by experts and validated with empirical evidence.

Method

Participants

Three hundred and seventy four undergraduate participants (143 online gamblers, 172

males) were recruited from the Research Participation System (RPS) of a large Canadian

university. The sample size exceeded the recommended rule of thumb for stepwise logistic

regression of at least 10 cases per independent variable (Harrell, Lee, Califf, Pryor, &

Rosati, 1984). The mean age of participants was 21 years old (range ¼ 18–52; SD ¼ 4.0)

with an average of three years of post-secondary education. The sample included students

from 11 different faculties. Respondents had to speak English, be at least 18 years of age

and have gambled at least once in the past month to participate. Internet gamblers (defined

as betting money online at least once in the past month) were over sampled to yield similar

cell sample sizes for logistic regression analysis. The decision to use an undergraduate

sample comes from the higher prevalence rates of Internet gambling among college and

university students (Griffiths & Barnes, 2008; Petry & Weinstock, 2007) than those found

in general population surveys (Wood & Williams, 2009).

Procedure and measures

The Conjoint Faculties Research Ethics Board granted ethical approval for this study.

Participants were recruited to take part in an online study presented as ‘gambling among

university students’. When participants signed up in the RPS they were given a link to the

online survey tool, Survey Monkey, and a password to access the survey. It was decided

that an online survey would be most advantageous for recruiting and testing a large

number of participants to ensure a sufficient sample size for the number of variables.

Research has shown that online surveys are comparable in validity to traditional data

collection methods (Gosling, Vazire, Srivastava, & John, 2004).

Each type of gambling activity was queried separately. Types of gambling behaviour

in land-based forms included: video lottery terminals (VLTs), slots, casino poker, casino

table games (e.g. blackjack, roulette), poker with friends, instant win, raffles, sports select

(parlay sports betting), sports pools, bingo, games of skill (e.g. pool, darts) and video

games. Types of gambling behaviour queried for online gambling included: casino games,

poker against the machine, poker against other players, sportsbooks, bingo, skill games

(e.g. solitaire, dominos, suduko, treasure hunts), betting exchanges, backgammon and

online role-playing games for money.1 Information was gathered about demographics

(gender, age), gambling behaviour (frequency, duration, expenditures) and gambling

initiation (i.e. in an online or land-based format). Participants also completed the

following two measures:

Problem Gambling Severity Index (PGSI)

The PGSI is a subsection of the Canadian Problem Gambling Index (Ferris & Wynne,

2001) designed to measure the severity of gambling problems. The PGSI asks participants

to rate how much statements apply to them within the past 12 months on a four-point scale

(never, sometimes, most of the time, almost always). Scores on the PGSI are continuous

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and are used to classify gambling behaviour as non-problem (score of 0), low-risk (score

of 1–2), moderate-risk (score of 3–7) or problem gambling (score of 8–27). The PGSI has

a Cronbach’s alpha reliability coefficient of .84, an internal reliability of .72, and a three-

to four-week test-retest reliability of .78 (Ferris & Wynne, 2001). The PGSI was designed

for use with non-clinical populations, is comparable to DSMmeasures (Abbott & Volberg,

2006), and useful for looking at gradations of severity.

Gamblers’ Beliefs Questionnaire (GBQ; Steenbergh et al., 2002)

The GBQ was designed as a self-report measure of gambling related cognitive distortions.

It consists of 21 questions rated on a seven-point Likert scale where respondents rate items

from ‘strongly agree’ to ‘strongly disagree’. Higher scores indicate more gambling related

cognitive distortions with the total scores ranging from 21 to 147. The instrument is

comprised of two closely related constructs of luck/perseverance and illusion of control.

The test was originally validated against the Massachusetts Gambling Screen DSM–IV

Questionnaire (MAGS DSM–IV; Shaffer, LaBrie, Scanlan, & Cummings, 1994) and the

South Oaks Gambling Screen (SOGS; Lesieur & Blume, 1987). The scale has an internal

consistency of .92 and a two-week test retest reliability of .77 (Steenbergh et al., 2002).

Data manipulation

Participants were removed if they completed the survey more than once (n ¼ 5), did not

complete at least 10% of the survey (i.e. only answered demographic information and did

not answer any gambling-related questions; n ¼ 10), or did not report any gambling

behaviours (n ¼ 11). When possible, scale scores were pro-rated when 10% or less of the

responses were missing. The age variable had missing values so missing participant ages

were estimated with a regression substitution. The expenditures variable had outliers

in the positive (n ¼ 7) and negative (n ¼ 1) direction so outliers above two standard

deviations were replaced with the next highest value þ 1 or 2 1 (Field, 2009; Tabachnick

& Fidell, 2007).

Results

As predicted, Internet gamblers had higher problem severity and were more likely to be

problem gamblers with a mean PGSI score of 3.52 (SD ¼ 3.39) compared to 1.56

(SD ¼ 2.38) for non-Internet gamblers, t(371) ¼ 26.55, p , .001. Less than 15% of

Internet gamblers were classified as non-problem gamblers by the PGSI, with 35.7%

classified as low risk, 40.6% as moderate risk and 10.5% classified as problem gamblers.

For non-Internet gamblers, 46.1% were classified as non-problem gamblers, 34.3% as low-

risk, 14.3% as moderate risk and 5.2% as problem gamblers, x 2(3, N ¼ 373) ¼ 51.72,

p , .001. Similarly, Internet gamblers were more likely to report gambling problems by

answering in the affirmative to the PGSI item: ‘Have you felt you ever had a problem with

gambling’, x 2(3, N ¼ 373) ¼ 9.66, p , .05. Seventeen percent of Internet gamblers

positively endorsed this statement compared to 7% of those that did not gamble online.

The following variables were entered into a logistic regression equation to predict

Internet gambling: gender, age, frequency of play, average duration of play, expenditures,

PGSI and GBQ. All assumptions of logistic regression analyses were tested including ratio

of cases to variables, linearity of the logit, multicollinearity and outliers (Field, 2009). In

accordance with conventional rules for identifying multicollinearity, all tolerance values

International Gambling Studies 167

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were less than 0.1 and no Variance Inflation Factors values were greater than 10 (Myers,

1990). Having wagered on the Internet was coded one (1) in the binary dependent variable.

The final model was significant (x 2(7) ¼ 92.34, p , .001), correctly predicting 71% of

gamblers (49% of Internet gamblers and 86% of non-Internet gamblers) indicating that the

final model was superior to the 61% predicted from the constant only model (100% non-

Internet gamblers). Significant variables in the final model included gender (with males

being more likely to gamble online, OR ¼ 0.55, 95% CI ¼ 0.32–0.94, p , .05),

frequency of play (online gamblers having a higher frequency of play, OR ¼ 1.20, 95%

CI ¼ 1.11–1.30, p , .001) and GBQ (online gamblers having more distortions,

OR ¼ 1.02, 95% CI ¼ 1.01–1.04, p , .001). The regression analysis yielded an effect

size of .31 (Nagelkerke R 2). The results of the logistic regression are summarised in

Table 1.

Online gamblers were more likely to report cognitive distortions with an average score

of 73.98 (SD ¼ 23.34) on the GBQ compared to 54.61 (SD ¼ 23.68) for non-Internet

gamblers, t(366) ¼ 7.67, p , .001. Online gamblers were more likely to gamble on a

greater variety of activities (M ¼ 6.08, SD ¼ 2.00) compared to gamblers who had not

wagered on the Internet (M ¼ 3.54, SD ¼ 1.63), t(371) ¼ 212.82, p , .001. Internet

gamblers were also more likely to wager on land-based activities (M ¼ 4.43, SD ¼ 1.71)

when compared to non-Internet gamblers (M ¼ 3.54, SD ¼ 1.63), t(371) ¼ 24.94,

p , .001.

An additional multiple regression analysis was conducted with Internet gamblers to

determine which variables would contribute to problem gambling severity level among

this group. Demographic variables were entered in the first step (model 1), including

gender and age. Play variables were entered into the second step (model 2), including

frequency of play, average duration of play, and expenditures. The GBQ was entered into

the third step (model 3). As shown in Table 2, the first model in the multiple regression

analysis just approached significance, F(2, 139) ¼ 3.15, p ¼ .05 (AdjR 2 ¼ .03). Models

2, F(5, 139) ¼ 7.30, p , .001, (AdjR 2 ¼ .19) and 3, F(6, 139) ¼ 13.06, p , .001,

(AdjR 2 ¼ .34) were significant. In the final model, frequency of play and GBQ were

significant independent predictors of problem gambling severity.

In order to control for problem gambling status, an additional analysis was conducted

to determine whether there were differences between Internet problem gamblers and non-

Internet problem gamblers. There were no significant differences between these two

groups in terms of gender x 2(1, N ¼ 27) ¼ 0.68, p . .05, PGSI score, t(25) ¼ 1.81,

Table 1. Logistic regression analysis for predicting Internet gamblers from non-Internet gamblers.

Variable B(SE) Wald Odds Ratio (95% CI)

Gender 20.60 (0.28) 4.75 0.55* (0.32–0.94)Age 20.08 (0.04) 3.13 0.93 (0.85–1.01)Frequency of play 0.18 (0.04) 22.45 1.20*** (1.11–1.30)Average duration of play 20.00 (0.00) .06 1.00 (0.99–1.01)Expenditures 0.00 (0.00) .21 1.00 (1.00–1.00)PGSI 0.02 (0.06) .08 1.02 (0.91–1.13)GBQ 0.02 (0.01) 12.75 1.02*** (1.01–1.04)Constant 21.31(0.98) 1.79 .27

Note: R 2 ¼ .23 (Cox & Snell), .31 (Nagelkerke). Model x 2(7) ¼ 92.34, p , .001. PGSI ¼ Problem GamblingSeverity Index; GBQ ¼ Gamblers’ Beliefs Questionnaire.

*p , .05, ***p , .001.

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p . .05, gambling frequency, t(25) ¼ 1.23, p . .05, or cognitive distortions,

t(25) ¼ 1.53, p . .05.

Discussion

The present study was designed to investigate differences between Internet and non-

Internet gamblers on cognitive distortions and to examine the influence of cognitions on

problem gambling severity among online gamblers. Consistent with previous research,

Internet gamblers were more likely to report disordered gambling behaviour when

compared to non-Internet gamblers. It is difficult to make direct comparisons and contrasts

to problem gambling rates in the existing Internet gambling literature because previous

studies have used the SOGS (Griffiths & Barnes, 2008; Ladd & Petry, 2002; Petry, 2006;

Petry & Weinstock, 2007), DSM criteria (Griffiths, Wardle, et al., 2009), adolescent

samples (Olason et al., 2011; Potenza et al., 2011) or random digit dialling of general

population samples (Wood & Williams, 2009). However, as with the aforementioned

studies, it remains substantiated that Internet gamblers endorse more problematic

behaviours when compared to non-Internet gamblers. The problem gambling rate among

Internet gamblers measured by the PGSI was 10.5% compared to 5.2% for non-Internet

gamblers.

Factors associated with both level of gambling involvement and the individual exerted

an influence to predict whether someone had gambled on the Internet. Significant

contributing variables to a prediction of online gambling included gender, frequency of

play and cognitive distortions. Similarly, higher frequency of activity and more distorted

gambling beliefs were associated with problem severity among online gamblers in the

final regression model. The hierarchical regression analysis revealed that distortions

accounted for a proportion of the variance in problem severity beyond the variance

accounted for by demographic variables and level of involvement in gambling activities.

Table 2. Hierarchical multiple regression analysis for variables predicting problem gamblingseverity among Internet gamblers.

Variable B SE B ß

Step 1Gender 21.27 0.57 2 .19*Age 20.15 0.12 2 .10

Step 2Gender 20.22 0.56 2 .03Age 20.18 0.12 2 .12Frequency of play 0.24 0.05 .41***Average duration of play 0.01 0.01 .10Expenditures 0.00 0.00 2 .02

Step 3Gender 0.22 0.51 .03Age 20.11 0.10 2 .07Frequency of play 0.16 0.05 .28**Average duration of play 0.00 0.01 .04Expenditures 20.00 0.00 2 .05GBQ 0.07 0.01 .45***

Note: AdjR 2 ¼ .03 for step 1; DAdjR 2 ¼ .16 for step 2 ( ps , .001); DAdjR 2 ¼ .16 for step 3 ( ps , .001).GBQ ¼ Gamblers’ Beliefs Questionnaire.

*p , .05, **p , .01, ***p , .001

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The degree of cognitive distortions significantly differentiated participants who had

gambled online with those that had not wagered on the Internet. Online gamblers had a

mean score of 74 on the GBQ compared to 55 for non-Internet gamblers. As a point of

comparison, research has shown that pathological gamblers (MAGS DSM-IV) score 76

compared to 54 for non-pathological gamblers on the GBQ. Similarly, on the SOGS,

pathological gamblers have been shown to score 71 compared to 53 for non-problem

gamblers (Steenbergh et al., 2002). On average, Internet gamblers are scoring similar to

pathological gamblers on measures of disordered gambling.

The implication in these findings is not one of causality. There are a number of

plausible reasons as to why Internet gamblers would have higher levels of cognitive

distortions. It could be the case that individuals who have a belief in a superior gambling

proficiency are more heavily involved and persevere in the inaccurate belief that they can

control outcomes. Regular gamblers have been shown to believe that they possess above

average skills in slot machine play when compared to non-regular players (Griffiths,

1990). When compared to offline gamblers, online gamblers in the current study were

more likely to endorse a belief in a superior skill set (e.g. my knowledge and skill in

gambling contribute to the likelihood that I will make money; my gambling wins are

evidence that I possess skill and knowledge related to gambling; I have more skills and

knowledge related to gambling than most people who gamble). As a speculative

observation, Internet gamblers may have unrealistic expectancies about future winnings

because of their assumptions of control in the gambling environment.

Research has shown that Internet gamblers endorse playing to enhance their ability

(Hopley & Nicki, 2010) and gamblers with a preference for games that contain elements of

skill have a greater illusion of control over outcomes (Myrseth, Brunborg, & Eidem,

2010). The belief in a superior skill base in the absence of an actual proficiency is a factor

of consideration. Internet gamblers may believe that they are ameliorating game play

through practice, despite their repeated losses. As such, these individuals may view

persistence in the face of financial failure as fortitude and sacrifice to hone skills.

Gambling sites may perpetuate this belief with messages implying that players can

increase their skill level with ongoing practice and investment. Although some individuals

may increase their skill through practice, it is not a likely outcome for the average gambler

and could come at the cost of monetary losses. If cognitions moderate the relationship

between risky practices and gambling intensity as research has shown (Miller & Currie,

2008), then the relative intractable nature of beliefs among Internet gamblers could serve

to exacerbate problem gambling vulnerability.

An additional explanation is that gamblers who are already experiencing problems are

more likely to gamble on multiple formats, to gamble online and to have more cognitive

distortions. Similarly, young men may be more likely to experience problems and to

gamble on the Internet. The compound nature of these risk factors could explain higher

levels of problem gambling severity. An alternative explanation is that individuals who are

attracted to Internet gambling are more likely to have certain personality characteristics

(e.g. are competitive in a controlled way) that allows them to be successful or to believe

that they are successful.

It may be that online gamblers enjoy moving away from the socialization aspect of the

game because it allows them to focus more intently on the game. One of the major

concerns put forth by the American Psychological Association’s Advisory Committee on

Internet Gambling was the potential for exacerbation of gambling problems due to the

solitary nature of Internet gambling (American Psychological Association, 2001). The

asocial nature of Internet gambling is a valid concern. This shift from a social to a solitary

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activity is particularly detrimental to those susceptible to problem gambling. Research has

demonstrated that problem gamblers are more likely to play in isolation and to report that

at the height of their addiction, they did the majority of their gambling alone (e.g. Griffiths,

1995). Shead, Hodgins, and Scharf (2008) found that poker players who favoured

gambling in a casino or online had more gambling problems when compare to those that

play with family or friends. In their study, poker players who reported gambling

predominantly for the skill factor spent a higher proportion of time playing online

compared to players who preferred it for the socialization aspect. Similarly, Cotte and

Latour (2009) highlighted that online gamblers described being attracted to the

competitive nature of play, and the feeling of power and control in the environment.

The finding that online gamblers were more likely to be male and engage in a wide

variety of gambling formats supports previous research (Griffiths & Barnes, 2008; Wood

& Williams, 2009). Griffiths, Parke, et al. (2009) showed that problem gambling in a

student sample of online poker players was associated with increased frequency of play

and longer durations of play. Wood and Williams (2009) found that the single most

significant predictor of Internet gambling for both Canadian and international samples was

the number of gambling formats in which participants engaged. They also found that

Internet gamblers had a greater frequency of activity when gambling. LaBrie and Shaffer

(2011) found that frequency of play was a behavioural marker of problematic gambling

patterns for online sports bettors. Similarly, in the current study, frequency of play in

various formats was related to problem gambling severity. It is not surprising that Internet

gamblers have a higher frequency of play by the nature of having more formats from

which to choose. More noteworthy was that they also engage in more land-based activities

when compared to non-Internet gamblers.

Some limitations of the research should be noted. One limitation in this study is the

potential generalizability of the findings. Although an undergraduate student sample

provides benefits (convenience, less treatment related confounds, higher levels of Internet

gambling involvement, shorter gambling histories), there are also associated drawbacks.

Undergraduates tend to be younger than the general population and previous research has

shown the average age of Canadian Internet gamblers to be 36 years old (Wood &

Williams, 2009). Undergraduates may have a current lower average income compared to a

general population sample of Internet gamblers. An income discrepancy could affect the

results in terms of expenditures and negative consequences. A second point of

consideration is given to online data collection. While the general limitations to online

data collection are acknowledged (validity in responding, sampling bias, representative-

ness of the sample) it was decided that, on balance, the benefits (time, cost, larger sample

size, familiarity with the medium) outweighed the drawbacks. It is important to note that

that online gamblers also gamble in land-based establishments. A logical comparison

group for non-Internet gamblers would be Internet-only gamblers, but such a group does

not naturally occur. The lack of a ‘pure’ Internet gambling sample is an endemic part of

the research area. Wood and Williams (2007) found that none of the Internet gamblers in

their national Canadian sample gambled exclusively online. The current study used the

same type of comparison group as similar studies in the extant literature (e.g. Griffiths &

Barnes, 2008).

Implications and future directions

Theoretical models of gambling behaviour highlight the complex and interactional nature

of cognitions with biological, social and psychological factors (e.g. Sharpe, 2002).

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The precise mechanism by which cognitive distortions develop, persist and change has yet

to be established because of the multifactorial nature of gambling involvement. Thus, it is

unclear in this sample whether cognitive distortions existed before or after Internet

gambling first commenced. It could be speculated that people who choose to gamble on the

Internet were already experiencing problems or gambling at a level that could be

indicative of developing problems. The Internet may be a particularly attractive option for

gamblers who have an initial propensity towards distorting beliefs, or could serve to

engender or perpetuate distortions around control in the Internet gambling environment.

It is conceivable that there are unique cognitive characteristics in individuals who find

appeal in online gambling. Longitudinal research is needed to further delineate the

direction of this relationship. Future research examining actual online gambling behaviour

in conjunction with cognitive factors would be advantageous for understanding the

validity of the self-report data obtained in this sample.

The results of this study have implications for better understanding individual factors

that could interact with the online medium. From a responsible gambling perspective, the

results of this study point to the need for regulators to ensure sites are not engaging in

practices that will inflate or overstate the average player’s chances of success. Of

particular consideration are how vulnerable groups such as minors and pathological

gamblers could be more susceptible to irrational beliefs through exposure to online

gambling. From a treatment perspective, cognitive restructuring might be a particularly

vital therapeutic component for Internet gamblers. As we have already witnessed, new

forms of technology have the capacity to considerably change the way people gamble.

Presently, only a handful of researchers study Internet gambling despite the growing

multi-billion-dollar industry. As gambling changes and evolves, investigators need to

advance research in conjunction with industry and technology. In a recent review into the

literature on Internet gambling, Shaffer, Peller, LaPlante, Nelson, and LaBrie (2010)

argued that there is a substantial need for empirical scientific research in the area. It is

hoped that this paper will contribute to the small but growing body of research aimed at

understanding some of the unique factors influencing online gambling.

Acknowledgements

This research was funded by the Social Sciences and Humanities Research Council of Canada, theAlberta Heritage Foundation for Medical Research Endowment Fund and the Alberta GamingResearch Institute.

Note

1. It was stated explicitly in the questionnaire that this activity referred to betting money in an onlinegame.

Notes on contributors

Terri-Lynn MacKay, MA, is a doctoral candidate in the Clinical Psychology Program at theUniversity of Calgary in the Addictive Behaviours Laboratory. She received her Masters degree inBehavioural Neuroscience from the University of Manitoba. Her research and clinical interestsare aimed at understanding the psychological factors that lead to addictive behaviours and themotivational factors that influence change. Her current research focuses on Internet gambling, with aspecific focus on cognitive variables and sociological impacts.

David C. Hodgins, PhD, is a professor in the Program in Clinical Psychology in the Department ofPsychology, University of Calgary. His research interests focus relapse and recovery from substance

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abuse and gambling disorders. He has a particular interest in concurrent mental health disorders andbrief motivational treatment. Dr Hodgins teaches in the clinical psychology programme and has anactive cadre of graduate students. He maintains a private practice in addition to providingconsultation to a number of organizations internationally.

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