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Psychology of Addictive Behaviors

Measuring Cognitive Distortions in PathologicalGambling: Review and Meta-AnalysesAdam S. Goodie and Erica E. Fortune

Online First Publication, February 25, 2013. doi: 10.1037/a0031892

CITATION

Goodie, A. S., & Fortune, E. E. (2013, February 25). Measuring Cognitive Distortions in

Pathological Gambling: Review and Meta-Analyses. Psychology of Addictive Behaviors.

Advance online publication. doi: 10.1037/a0031892

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Measuring Cognitive Distortions in Pathological Gambling:Review and Meta-Analyses

Adam S. Goodie and Erica E. FortuneUniversity of Georgia

There is broad agreement that cognitive distortions are an integral component of the development,

maintenance, and treatment of pathological gambling. There is no authoritative catalog of the

distortions that are observed more frequently in pathological gamblers than in others, but several

instruments have been successfully developed that measure various distortions of interest, which are

reviewed. All of the prominent instruments include measures of the illusion of control (perceiving

more personal control over events than is warranted), and almost all include measures of gamblers

fallacy (the belief that after a string of one event, such as a coin landing heads, an alternative event,

such as the coin landing tails, becomes more likely). Beyond these two errors, there is scant

consensus on relevant errors, and a wide variety has been studied. Meta-analyses were conducted on

differences between PGs and non-PGs in scores on six published instruments that were developed

to measure distortions in gamblers. All instruments reveal large effects using Hedges g statistic,

suggesting that the impact of distortions on PG is robust. Several subscales, assigned diverse namesby scale authors, can be viewed as reflecting common distortions. Those judged to assess gamblers

fallacy show evidence of more robust effects sizes than those that assess illusion of control. It is

recommended that future research focus more specifically on the impact of particular distortions on

gambling disorders.

Keywords:gambling, cognitive distortion, heuristic, addiction, meta-analysis

It has been widely argued that cognitive distortions play an

important role in the development and maintenance of pathological

gambling (PG; e.g., Jacobsen, Knudsen, Krogh, Pallesen, &

Molde, 2007; Ladouceur, 2004; Xian et al., 2008). Despite the fact

that distortions are not a diagnostic criterion for PG, the correction

of gambling-related distortions has been a primary avenue for the

clinical treatment of PG (e.g., Jimnez-Murcia et al., 2007; Ladou-

ceur, Sylvain, Letarte, Giroux, & Jacques, 1998; for recent re-

views, see Fortune & Goodie, 2012, and Gooding & Tarrier,

2009), as well as understanding the development and maintenance

of PG. Importantly, although PG-related distortions are derived

largely from the literature of heuristics and biases that plague

humankind in general, and not just those who experience gambling

problems (Tversky & Kahneman, 1974), these errors are posited to

occur with greater severity or in situations of greater importance

among those with PG. Blaszczynski and Nower (2002) identified

the distortions as part of the learning pathway to PG, and an

extensive series of studies, related in Walker (1992), suggests that

irrational cognitions play a central role in the maintenance ofdisordered gambling.

Yet the field lacks consensus on how to accurately identify and

measure gambling-related distortions. There is not a single error or

process that defines cognitive distortions. Rather, the term most

often refers to a loosely defined class of errors, including a small

number of well-characterized errors that are identified often (most

notably, the illusion of control and gamblers fallacy) and a larger

number that are each identified occasionally.

A preponderance of research spanning roughly the decade of the

1990s used the thinking-aloud method (e.g., Gadboury & Ladou-

ceur, 1989) to elicit irrational verbalizations in a free-response

format, rather than targeting particular errors, revealing higher

frequencies and proportions of irrational beliefs among patholog-

ical gamblers (PGs) than among other populations (Baboushkin,

Hardoon, Derevensky, & Gupta, 2001; Griffiths, 1994; Hardoon,

Baboushkin, Derevensky, & Gupta, 2001). More recent ap-

proaches to distortions have developed through psychometrically

validated surveys, with items expressing distortions, and responses

reflecting degrees of endorsement of those distortions.

Survey-based research now provides sufficient evidence to sup-port at least provisional analyses of the magnitude of the relation-

ship between cognitive distortions and gambling severity, and to

begin to evaluate which distortions are of greatest promise for

research and intervention targeting. The current paper has two

goals: (a) to aggregate and statistically evaluate associations be-

tween PG and cognitive distortions in general, and (b) to begin to

disentangle the potential roles of particular distortions. It is hoped

that this will help both researchers and clinicians to focus future

work on particular distortions; their roles in development, main-

tenance, and treatment of PG; and particular measurement instru-

ments.

Adam S. Goodie and Erica E. Fortune, Department of Psychology,

University of Georgia.

Preparation of this article was supported by grants from the National

Center for Responsible Gaming.

Correspondence concerning this article should be addressed to Adam S.

Goodie, Department of Psychology, University of Georgia, 125 Baldwin

Street, Athens, GA 30602-3013. E-mail: [email protected]

Psychology of Addictive Behaviors 2013 American Psychological Association2013, Vol. 27, No. 1, 000 0893-164X/13/$12.00 DOI: 10.1037/a0031892

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Cognitive Distortions Implicated in PG

Among the distortions that have been implicated as contributing

to PG, several derive from the well-known heuristics defined by

Kahneman and Tversky (1974), especially availability and repre-

sentativeness. Availability refers to a process of deeming an event

as more likely if similar events are easier to recall from memory.

Representativeness refers to a process of judging an event likely to

be drawn from a particular class to the extent that it resembles (or

is representative of) a typical member of that class. Distortions

rooted in availability include illusory correlations, inherent mem-

ory bias, and the availability of others wins (for a review, see

Fortune & Goodie, 2012). Applied to gambling, illusory correla-

tions exist where gamblers erroneously perceive a relationship

between unrelated events, such as the idea that their personal luck

is an influential factor in their gambling outcomes (Petry, 2004).

Coincidental co-occurrences of superstitious beliefs and behaviors

with winning are more likely to be recalled, and thus to be more

available in memory, due to a memory bias for wins (Wagenaar,

1988). Furthermore, the availability of others wins, such as by

seeing and hearing the effects of winning pulls of nearby slotmachines, can influence gamblers to increase the subjective like-

lihood of winning (Griffiths, 1994).

Distortions derived from the representativeness heuristic include

the gamblers fallacy, overconfidence, and trends in number pick-

ing. The gamblers fallacy occurs when individuals believe that

even short strings of random events must correspond with their

perception of what constitutes randomness, leading to beliefs that

particular outcomes are due (Tversky & Kahneman, 1971).

Overconfidence is a tendency to display degrees of confidence that

are unwarranted by actual ability (Koriat, Lichtenstein, & Fis-

chhoff, 1980), and correlates positively with PG (Goodie, 2005;

Lakey, Goodie, Lance, Stinchfield, & Winters, 2007; Walker,

1992). The belief that random processes should result in typicalpatterns over short runs is also apparent when looking at trends in

number picking (e.g., choosing lottery numbers), in which gam-

blers avoid picking duplicate numbers and prefer number strings

that do not contain neighboring digits (Haigh, 1997; Holtgraves &

Skeel, 1992; Rogers & Webley, 2001).

Other important gambling-related distortions are not clearly

related to established heuristics, such as the illusion of control,

near-miss effects, self-serving bias, and the concept of impaired

control. The illusion of control describes an individuals belief in

his or her probability of personal success that is unjustifiably high

(Langer, 1975), and comes into sharper relief when considered in

the context of a gamblers ability to recognize situations in which

control is or is not applicable (Goodie, 2003, 2005). The near-miss

effect occurs in the context of outcomes that constitute losses butare similar to potential winning outcomesfor example, a lottery

number that differs from the winning number only slightly. Near-

miss outcomes have been found to enhance future gambling re-

sponses (Clark, Lawrence, Astley-Jones, & Gray, 2009), particu-

larly among PGs (Chase & Clark, 2010). Self-serving bias refers

the tendency to attribute wins to skill or other internal causes and

losses to external causes. Impaired control (distinct from the illu-

sion of control) is a gamblers belief that he or she cannot control

his or her own problematic gambling behaviors. It may be argued

that impaired control is not a true cognitive distortion, either on the

grounds that it reflects a motivational or behavioral state rather

than a cognitive state, or on the grounds that it is not an irrational

belief if the individual truly is unable to control his or her gam-

bling.

Instruments for Measuring Cognitive Distortions in PG

A Google Scholar and PsycINFO search for pathological gam-bling, in conjunction with search terms including cognitive

distortions, irrational beliefs, erroneous, biases, or heuris-

tics, yielded the following instruments that have been used to

measure distortions in PG. In order to compile the most complete

index of data investigating the connection between pathological

gambling and cognitive distortions, the corresponding authors for

all of the included instruments were contacted and asked for any

relevant citations or unpublished data that utilized their instru-

ments. Original and supporting studies are summarized in Table 1.

The Information Biases Scale (IBS)

The IBS (Jefferson & Nicki, 2003) assesses various distortions

by having individuals determine the applicability of each of 25

items on a 7-point Likert scale. The measure has good internal

reliability ( .92) and correlates with both the South Oaks

Gambling Screen (SOGS; Lesieur & Blume, 1987; r .48) and

the National Opinion Research Center DSMIV Screen for Gam-

bling Problems (NODS; Gerstein, Volberg, Harwood, & Chris-

tiansen, 1999; r .38). The SOGS was developed as a research

screen and does not reflect a diagnostic symptom count. The

NODS is a symptom-based screen. The IBS was developed to be

administered to video lottery terminal (VLT) gamblers; hence, no

comparative data are available from a nongambling sample. Al-

though the IBS is a single-factor questionnaire, the authors note

that the scale taps into several different distortions, including the

illusion of control (e.g., I would rather use a VLT that I amfamiliar with than one that I have never used before), the gam-

blers fallacy (e.g., The longer a VLT has gone without paying

out a large sum of money, the more likely are the chances that it

will pay out in the very near future), illusory correlations (e.g., I

know some VLT users who are just plain lucky), and the avail-

ability heuristic (e.g., Hearing about other people winning on

VLTs encourages me to keep on playing). Scores on the IBS

decreased among moderate-risk VLT players (using the Canadian

Problem Gambling Index [CPGI]; Ferris & Wynne, 2001) with

prevention interventions, but not in a control group (Doiron &

Nicki, 2007).

Gambling Beliefs Questionnaire (GBQ-1)Two different scales share a single acronym, GBQ, despite

having been developed independently and with differing items.

These will be termed GBQ-1 and GBQ-2, which reflects the

chronological order in which they were first published.

The GBQ-1 (Steenbergh, Meyers, May, & Whelan, 2002) is a

21-item questionnaire with two subscales reflecting two categories

of gambling beliefs: Luck/Perseverance (13 items) and the Illusion

of Control (8 items), with all items measured on a 7-point Likert

scale. The Illusion of Control items focus on perceived knowledge

and skill related to gambling, and the Luck/Perseverance items

focus on specific beliefs and strategies that might be utilized while

2 GOODIE AND FORTUNE

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Tab

le1

StudiesThatHaveRelatedSelf-Rep

ortMeasuresofCognitiveDistortionsto

DegreeofGamblingSeverity

Gambling

measure

Gambling

classification

Scores

Effect

sizea

Correlationswith

gambling

measure

Study

Year

N

Sample

Group

Mean

SD

IBS Je

fferson&Nicki

2003

96

Community:VLT

players

SOGS

None

Entiresam

ple

149

.05

47.32

r

0.4

8

Jefferson,Doiron,

Nicki

,&MacLean

2004

Sam.

1

228

Undergraduates

SOGS&NO

DS

None

Entiresam

ple

97.2

6

25.06

SOGS:r

0.77;

Pastyear

NODS:

r

.26;and

Lifetime

NODS:

r

.29

Sam.

2

30

Undergraduates

SOGS&NO

DS

None:Experimental

(n

15)

Experimental(pre)

92.8

8

19.88

NR

Experimental(post)

55.2

5

20.28

1.87

None:Control

(n

15)

Control(p

re)

102

.73

32.09

NR

Control(p

ost)

92.7

3

34.82

0.3

Brisley

UD

Nonproblem

(n

10)

Time1

49

Community:VLT

players

CPGI

Lowrisk(n

9)

Entiresam

pleat

Time1

102

27.9

r

0.4

1

Time2

36

Moderaterisk

(n

15)

Entiresam

pleat

Time2

101

.43

29.54

0.02

Problemgambler

(n

14)

Otteson

UD

40

Community:VLT

players

CPGI

Problemgamblers

Treatment(pre)

114

.8

20

NR

Treatment(post)

102

.55

19.12

0.63

Education

al(pre)

109

.25

26.63

Education

al(post)

109

.75

30.34

0.02

Doiron&Nicki

2007

40

Community:VLT

players

CPGI

Moderaterisk

experimental

(n

20)

Experimental(pre)

94.6

5

27.14

NR

Experimental(post)

68.6

28.05

0.94

Moderaterisk

control(n

20)

Control(p

re)

99.4

26.1

Control(p

ost)

101

.25

26.87

0.07

Nickietal.

2008

40

Community:VLT

players

PGSI

Problem(n

20)

Problem

4.4

2

1.11

r

0.7

7

Nonproblem

(n

20)

Nonproblem

2.6

7

0.85

1.77

Gallagheretal.

inpress

54

Community:VLT

players

2-week

Problem(n

27)

Problem

110

.23

32.26

NR

PGSI

Nonproblem

(n

27)

Nonproblem

94

25.08

0.56

(tablecontinues)

3MEASURING COGNITIVE DISTORTIONS IN PG

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Tab

le1(continued)

Gambling

measure

Gambling

classification

Scores

Effect

sizea

Correlationswith

gambling

measure

Study

Year

N

Sample

Group

Mean

SD

Hayes

UD

40

Community:VLT

players

CPGI

Problemgamblers

Experimental

123

.93

17.92

NR

Control

117

.43

29.14

GBQ-1

Steenberghetal.

2002

371

Undergraduates

(n

192)

SOGS

Nonproblem

(n

317)

Nonproblem

52.5

22.21

Community

(n

179)

Problem(n

22)

Problem

60.7

3

18.82

0.37

NR

Pathological

(n

32)

Pathological

70.9

4

21.5

0.49

Brisley

UD

Nonproblem

(n

10)

Time1

49

Community:VLT

players

CPGI

Lowrisk(n

9)

Entiresam

pleat

Time1

103

.79

21.12

r

0.5

4

Time2

36

Moderaterisk

(n

15)

Entiresam

pleat

Time2

102

.23

20.87

0.07

Problemgambler

(n

14)

MacKillopetal.

2006

104

Undergraduates

SOGS

Nonproblem

(n

41)

Nonproblem

46.3

9

19.88

PPGsLP

subscale:r

0.3

8

Problem(n

40)

Problem

60.6

0

22.32

0.67

PPGsIOC

subscale:r

.34

Probable

pathological

(n

23)

Probable

pathological

89.3

0

21.18

1.31

PGsLPsubscale:

r

0.38

PGsIOC

subscale:r

0.1

3

Doiron&Nickib

2007

40

Community:VLT

players

CPGI

Moderaterisk

experimental

(n

20)

Experimental(pre)

116

.25

18.01

NR

Experimental(post)

128

.65

16.39

0.72

Moderaterisk

control(n

20)

Control(p

re)

106

.9

17.6

Control(p

ost)

104

.85

15.46

0.12

Mattsonetal.

2008

393

Undergraduates

SOGS

Nonproblem

(n

297)

Entiresam

ple:IOC

subscale

16.9

8

8.66

LPsubscale:r

0.2

9

Problem(n

83)

Entiresam

ple:LP

subscale

20.7

8

9.82

IOCsubscale:

r

0.22

Probable

pathological

(n

13)

(tablecontinues)


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gambling. Several of the Luck/Perseverance items reflect the gam-

blers fallacy (e.g., If I continue to gamble, it will eventually pay

off and I will make money). The GBQ-1 shows good testretest

reliability (r .77), as well as strong convergent validity with

pathological gambling measures. Internal reliability indices such

as Cronbachs alpha were not reported in the original development,

but Mattson, MacKillop, Castelda, Anderson, and Donovick(2008) reported values of 0.93 for the scale as a whole, 0.89 for

Illusion of Control, and 0.94 for Luck/Perseverance. GBQ-1 total

score and subscale scores were significantly higher among PGs

than among nonpathological gamblers (NPGs), as identified by

both the Massachusetts Gambling Screen DMS-IV Questionnaire

(Shaffer, LaBrie, Scanlan, & Cummings, 1994) and the SOGS.

However, the GBQ-1 did not differentiate between PGs (those

scoring 5 or higher) and problem gamblers (those scoring a 3 or 4)

on the SOGS. Demonstrating good discriminant validity, the re-

sults could not be explained by demand characteristics, as GBQ-1

scores did not correlate with a measure of social desirability.

Scores on the GBQ-1 decrease with improved cognition in VLT

players at moderate risk (using CPGI) who receive prevention

interventions, and correlate positively with gambling pathology

scores (MacKillop, Anderson, Castelda, Mattson, & Donovick,

2006, and Mattson et al., 2008, both using SOGS; Mitrovic &

Brown, 2009, using CPGI), but this result is not reflected in a

control group (Doiron & Nicki, 2007). Similarly, Goodie et al.

(2010) found both correlations and between-groups differences on

the GBQ-1 using the Structured Clinical Interview for PG (Grant,

Steinberg, Kim, Rounsaville, & Potenza, 2004).

Perceived Personal Luck Scale (PPLS)

The PPLS (Wohl, Young, & Hart, 2005) consists of 10 items

from the GBQ-1 (Steenbergh et al., 2002), reconfigured with a

5-point Likert format, that represent a skill orientation towardgambling. The PPLS has good internal consistency ( .88;

replicated by Wohl, Young, & Hart, 2007, .90). Both low- and

high-risk undergraduate problem gamblers, identified by the Prob-

lem Gambling Severity Index (PGSI; a subscale of the CPGI),

have greater perceived personal luck than nonproblem gamblers

(Young, Wohl, Matheson, Baumann, & Anisman, 2008). Further-

more, individuals who prefer games with a combination of skill

and chance also have greater perceived personal luck than those

who prefer pure chance games (Wohl et al., 2005).

Gambling Belief Questionnaire (GBQ-2)

The GBQ-2 (Joukhador, MacCallum, & Blaszczynski, 2003) is

a 65-item questionnaire scored on a 5-point Likert scale ranging

from 0 to 4. The questionnaire includes 12 subscales: Illusion of

Control (9 items), Erroneous Beliefs of Winning (4 items), En-

trapment/Gamblers Fallacy (12 items), Superstition (8 items), Im-

paired Control (5 items), Near Miss (3 items), Memory Bias (3

items), Biased Evaluation (equivalent to self-serving bias; 7

items), Positive State (3 items), Relief (5 items), Money Equals a

Solution to Problems (4 items), and Denial (2 items).

The overall scale exhibits good internal reliability ( .97,

although subscale reliability indices were not reported) and shows

group differences based upon gambling severity. Problem gam-

blers, whom Joukhador et al. (2003) stringently defined as those

scoring a 10 or higher on the SOGS, had significantly higher

scores than social gamblers on all of the subscales except Denial.

Follow-up studies utilized a revised 48-item version (i.e., Moodie,

2007), which lacks published validation, as well as a briefer

24-item version (Moodie, 2008). The 24-item version uses a five-

factor model, with six items apiece for the categories of Coping,

Personal Illusory Control, and General Illusory Control, and threeitems apiece for Winning Expectancy and Rational Beliefs. This

abbreviated version correlates significantly with the SOGS (r

0.58) and showed good internal reliability ( .89).

The Gambling Related Cognitions Scale (GRCS)

The GRCS (Raylu & Oei, 2004) is a 23-item questionnaire,

assessed on a 7-point Likert scale, which contains five subscales.

Each focuses on a particular cognition, including the Illusion of

Control (4 items), Predictive Control (equivalent to the gamblers

fallacy; 6 items), Interpretative Bias (4 items), Gambling-Related

Expectancies (4 items), and Perceived Inability to Stop Gambling

(impaired control; 5 items). The Illusion of Control subscale fo-cuses on superstitious beliefs, and the Predictive Control subscale

focuses on probability errors such as the gamblers fallacy. The

Interpretive Bias subscale items are based upon self-serving

bias (e.g., Relating my losses to bad luck and bad circumstances

makes me continue gambling). Gambling-Related Expectancies

focus on expected benefits from gambling (e.g., Gambling makes

me happier), and the Perceived Inability to Quit reflects respon-

dents confidence in their ability to control their gambling (e.g.,

Im not strong to enough to stop gambling).

The GRCS shows good internal reliability, with overall scale

.93 and subscale values ranging from 0.77 to 0.91. It shows

satisfactory concurrent validity with measures of depression and

anxiety, gambling motivation, and SOGS scores, as well as good

discriminant validity. The GRCS total score correlates with SOGS

scores (r 0.43), and GRCS subscale scores accounted for 27%

of SOGS score variance in a regression analysis. Correlations

among children, mothers and fathers were also significant (Oei &

Raylu, 2004). The authors tested two discriminant functions, one

with the subscales as predictors and one with the total score as a

predictor, which accurately classified participants into two groups

(SOGS scores of 0 vs. SOGS scores of 4 or higher), 86% of the

time for the first function and 85% of the time for the second

function. Individuals with SOGS scores of 4 or greater had sig-

nificantly higher scores on the GRCS total score and on all of the

five subscales than those with SOGS scores of 0.

The GRSC has been validated by means of correlations with

pathology measures (Oei & Raylu, 2004; Oei, Lin, & Raylu, 2007,both using SOGS), and by group average differences between

pathology-defined groups (Emond & Marmurek, 2010, using the

PGSI). Uniquely among the instruments reviewed here, the GRCS

has been tested cross-culturally (Oei, Lin, & Raylu, 2008). Chinese

and Caucasian participants had similar GRCS subscale scores,

except for the Illusion of Control and the Perceived Inability to

Stop Gambling subscales, in which Chinese participants scored

higher. Oei et al. (2008) suggest that these differences may be a

result of unique cultural norms, posing a challenge to researchers

who attempt to apply Western-developed PG treatment programs

to Chinese populations.


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Gambling Beliefs and Attitudes Survey (GABS)

The GABS (Breen & Zuckerman, 1999) is a 35-item question-

naire scored on a 4-point Likert scale that is not divided into

subscales but rather is intended to capture a wide range of

cognitive biases, irrational beliefs, and positively valued attitudes

to gambling (Breen & Zuckerman, 1999, p. 1102). Several items

clearly represent common biases, including the gamblers fallacy

(e.g., If I have not won any of my bets for a while, I am probably

due for a big win) and the illusion of control (e.g., No matter

what the game is, there are betting strategies that will help you

win), but the GABS total score is taken as a whole to represent a

gambling affinity. The GABS was developed solely for use with

gamblers and should be administered only to individuals who

understand gambling-related concepts and vernacular. In prelimi-

nary studies, the GABS demonstrated good internal reliability with

both student ( .93) and treatment-seeking PG ( .90)

samples. GABS scores also showed a strong positive relationship

with SOGS scores for the primary sample of male college students

who gambled (r 0.38), although neither SOGS scores nor GABS

scores were significantly different when comparing groups ofparticipants categorized as chasers and nonchasers.

Scores on the GABS have been found to be higher among PGs

than various nonclinical populations (Strong, Breen, & Lejuez,

2004, and Tochkov, 2009, both using SOGS). Additionally, GABS

scores have been found to correlate with pathology measures in

studies that measure pathology using the SOGS (Neighbors, Los-

tutter, Larimer, & Takuski, 2002; Strong, Breen, et al., 2004).

Revised 10- and 15-item versions of the GABS have also been

found to correlate with PGSI scores (Callan, Ellard, Shead, &

Hodgins, 2008, Studies 1 and 2; Strong, Daughters, Lejuez, &

Breen, 2004).

Video Gaming Device Inventory (VGDI)

The 45 yesno items of the VGDI (Pike, 2002) assess partici-

pants endorsement of gambling-related beliefs and behaviors in

two subscales. The Interest subscale (24 items) focuses on feelings

and beliefs that precede or accompany gambling, and the Effects

subscale (21 items) focuses on repercussions of gambling. Several

items in the Effects subscale are variations of the Diagnostic and

Statistical Manual of Mental Disorders (4th ed.; DSMIV; Amer-

ican Psychiatric Association, 1994) criteria for pathological gam-

bling. For example, two items are I have tried in the past to stop

playing video gaming devices and I have wished I could stop

playing video gaming devices, reflecting the DSMIVcriterion of

inability to reduce gambling activity despite repeated attempts to

do so. The Interest subscale incorporates erroneous cognitions

such as the gamblers fallacy (e.g., I have thought that a string of

bad luck was a clue that I was close to winning) and the illusion

of control (e.g., I have thought that certain ways that I have

played increased my chances of winning with video gaming de-

vices). The subscales showed high internal reliability, with

ranging from 0.90 to 0.93 for the two scales in two samples.

During validation, the VGDI correctly classified 93% of partici-

pants as PG or NPG, with the NPG group showing better categor-

ical accuracy than the PG group (95.5% vs. 80.5%). The VGDI is

intended for use in the identification of problem video gamblers,

and it measures erroneous cognitions in gamblers as a secondary

focus.

Additional Measures of Cognitive Distortions

Three additional measures of distortions, although not suited for

the meta-analytic procedures discussed here, merit mention. Xian

et al. (2008) studied monozygotic and dizygotic twins in an inter-

view format, determining that key genetic and environmental

factors did not significantly moderate the positive relationship

between gambling severity and distortions. The presence of dis-

tortions was determined using a 12-question interview, focusing on

elements of control, beliefs about luck, and superstitions. All 12

items loaded onto a single factor, and the mean number of distor-

tions for four levels of gambling severity (NODS low risk, at risk,

problem gamblers, and PGs) all significantly differed from each

other. For example, low risk gamblers displayedM 0.83 distor-

tions, whereas PGs displayed M 2.82.

Joukhador, Blaszczynski, and MacCallum (2004) examined su-

perstitious beliefs among PGs and NPGs who use electronic gam-

ing machines, such as feeling and acting upon hunches or partic-

ipating in gambling rituals (adapted from Toneatto, 1999). PGs

believed in gambling-related superstitions (M 9.6) significantly

more than NPGs (M 2.2).Finally, the Belief in Good Luck scale (BIGL; Darke & Freed-

man, 1997; updated by Maltby, Day, Gill, Colley, & Wood, 2008),

although not created for use with gambling populations, is a useful

measure of luck-related distortions in pathological gamblers. The

scale consists of 12 items on a 6-point Likert scale, with the total

score representing the extent to which individuals believe in luck.

The scale has previously been used (Wohl & Enzle, 2002) to

investigate perceptions of luck in gambling situations in a sample

of university students, although gambling pathology was not a

variable of interest. Chiu and Storm (2010) found that PGs (mea-

sured using the CPGI) showed greater levels of perceived luck

than the moderate-risk gamblers, low-risk gamblers, and NPGs.

Meta-Analyses of Established Measures

In order to evaluate the effectiveness of the instruments as a

group, and potentially to compare their relative discriminant va-

lidity, meta-analyses were conducted of all identifiable findings on

whether groups with higher levels of gambling-related pathology

reliably exhibit higher levels of distortions. The studies listed in

Table 1 formed the initial basis for the meta-analyses. Studies were

excluded that reported scores only for the entire sample, not

distinguishing between groups of higher and lower pathology. For

studies with more than two levels of pathology (e.g., nongamblers,

low-risk gamblers, and high-risk gamblers), effect sizes used for

meta-analysis purposes included the means for the lowest and

highest severity groups so as to avoid multiple comparisons using

the same data. The analysis consisted of a comparison of standard-

ized mean difference (Hedges g statistic). Because chi-square

tests of heterogeneity were generally significant, a random effects

model was used. As can be seen in Table 2, all measures of

distortions were significantly related to gambling severity, with

absolute effect sizes ranging from 0.77 to 2.50, which are consid-

ered large effects.

All of the included instruments reveal sharply greater distortions

among PGs than among NPGs. The IBS exhibits an admirable

absolute effect size and 95% confidence interval (CI), but its

prospects for widespread use are limited by the fact that it is

intended only for those who participate in VLT gambling. Infre-


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quent use in research studies is currently a limiting factor of the

PPLS, GBQ-2, GRCS, and GABS. And, although the PPLS and

GBQ-2 are associated with the largest point estimate effect sizes(2.50 and 1.92, respectively), they also have the largest 95% CIs.

This results in their having relatively low CI lower bounds, mean-

ing the possibility of being associated with relatively small effects,

despite having the two greatest effect size point estimates. This

suggests that the current literature provides relatively little confi-

dence in the true magnitude of the relationships between PPLS and

GBQ-2 and gambling severity. In comparison, despite the small

numbers of studies utilizing the GRCS and the GABS, both of

these measures demonstrate robust estimated effect sizes with

relatively narrow 95% CIs, suggesting their psychometric proper-

ties are relatively well characterized in the existing literature.

Overall, GBQ-1 has been investigated in the most extensive num-

ber of studies, with a large collective N coming from a largediversity of laboratories, a large effect size, and a narrow 95% CI.

Several of the instruments include subscales reflecting particular

distortions, and although the subscales are labeled by their authors

with diverse names, four common distortions can be identified

among the subscales of multiple scales: illusion of control, gam-

blers fallacy, self-serving bias and impaired control. The PPLS

does not contain subscales, but the entire scale is included as a

reflection of illusion of control. This categorization of subscales is

depicted in Table 3, which reports Cohen effect sizes in all

studies reporting subscale scores. Studies are indicated in which

data were reported dimensionally as correlations, which have been

transformed to effect size measures. Table 3 indicates the names

assigned by scale authors to the repeated subscales, where theassigned name differs from that of the category as a whole, as

well as names and observed effect sizes of subscales that are

unique to a single scale.

Robust effect sizes are reported for all subscales except Denial

in the GBQ-2, and so it would not be appropriate to rule out any

of the other distortions for research and treatment. However, it is

notable that between the two most frequently studied distortions,

Illusion of Control and Gamblers Fallacy, Gamblers Fallacy is

consistently associated with a greater effect size, often consider-

ably greater. This is observed within subjects and within studies, as

well as across diverse implementations of the distortions.

Conclusions

Meta-analyses were conducted on the six diverse measures of

distortions for which adequate empirical testing was available. All

six had significant discriminant validity, being differentially asso-

ciated with gambling pathology scores. All were associated with

large effect sizes, and there was a considerable range of confidence

interval sizes observed. The widely held belief that distortions play

an important role in PG draws support from this pattern of results,

and the robustness of this role is supported by strong effects that

are seen across a broad diversity of conceptions and operational-

izations of distortions, from including only one or two dimensions

(PPLS, GBQ-1), to explicitly including a full range of specific

distortions (IBS, GBQ-2, GRCS), to including a broad range of

items within a single dimension of gambling affinity (GABS).

The prominent effect sizes associated with the gamblers fallacy

is of particular interest. The gamblers fallacy was not discoveredor named in reference to pathological gambling, and although

gambling situations have been used to identify and exemplify the

fallacy (e.g., Kahneman & Tversky, 1972), it is not thought to be

limited to PG or to gambling situations. Under the gamblers

fallacy, a gambler who has lost would incorrectly expect to be

more likely to win in the future. (The implications of this error for

chasing losses are obvious.) Gamblers fallacy reflects a belief in

negative serial dependencyas one potential event occurs, it is

thought to become less likely in the future. As such, it is the

opposite of the hot hand phenomenon (Gilovich, Vallone, &

Tversky, 1985), in which one believes an outcome that has pre-

vailed recently is hot, that is, that the outcome more likely to

prevail again in the future. An example of the hot-hand phenom-enon is believing that a basketball player who has recently made

several shots is more likely than he would otherwise be to make

shots in the near future. The hot-hand phenomenon is reflected in

the current literature in the GBQ-2, as the Erroneous Beliefs of

Winning subscale refers to the belief that the individual is on a

winning streak or can win at gambling, for example, When I

begin winning I keep going because I know that I will be on a roll.

This subscale was associated with a notably large effect size

among the broad set of distortions studied by Joukhador et al.

(2003). It is interesting, but not contradictory, that gambling se-

verity is associated with errors of both positive and negative serial

Table 2

Meta-Analysis Statistics for Cognitive Distortion Measures

Measure Number of studies Total N 2 of heterogeneity Hedges g 95% CI

IBS 7 409 26.82 0.77 0.33, 1.21GBQ-1 5 896 15.51 1.02 0.67, 1.38

PPLS 3 190 61.95

2.50 0.26, 4.73GBQ-2 2 899 3.40 1.92 0.66, 3.17GRCS 4 862 5.96 1.90 1.52, 2.27GABS 3 1458 2.85 0.97 0.80, 1.13

Note. A meta-analysis was not conducted for GABS-R because the studies using the measure did not report subgroup means needed in order to calculateeffect sizes. For all other measures, if studies reported means for multiple subgroups (i.e., nongamblers, low-risk gamblers, and high-risk gamblers), effectsizes used for meta-analysis purposes only included the means for the lowest and highest pathology groups (i.e., nongamblers vs. high risk gamblers) asto avoid multiple comparisons using the same data. GABS Gambling Attitudes and Beliefs Scale; GBQ-1 Gamblers Beliefs Questionnaire; GBQ-2 Gambling Beliefs Questionnaire; GRCS Gambling Related Cognitions Scale; IBS Information Biases Scale; PPLS Perceived Personal Luck Scale. p .01. p .001.


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dependency. These are both important errors in gambling settings

with no serial dependency, that is, where each gambling event is

independent of those that precede it.

Gamblers fallacy and the illusion of control have received

particular attention among the possible cognitive distortions. Does

this imply that these fallacies are most prominent in degree of

importance, reliability, and so forth? To the contrary, rather, itappears to be either a coincidence or an accident of researchers

predilections. It has not been demonstrated that gamblers fallacy

and illusion of control are superior predictors of generalized dis-

tortions or of gambling pathology, or stronger bases for therapy,

compared with other particular distortions. Clearly, these distor-

tions are strongly associated with PG. The comparison presented

here between the two suggests that gamblers fallacy is the stron-

ger candidate for further focusing of treatment and prevention

interventions, and associated research.

It appears that the broad relationship between cognitive distor-

tions and PG is sufficiently robust that a wide variety of potential

instruments may capture the effect. At present, the relevance of

gamblers fallacy and illusion of control are most strongly sup-ported. Nevertheless, instruments based on a broad array of puta-

tive distortions remain generally preferable. These may feasibly be

brief in scope, exemplified by the 23-item GRCS, or more exten-

sive, like the 65-item GBQ-2. A broad-based instrument permits

researchers and clinicians to work from a larger menu of potential

errors. It also permits further empirical investigation of which

errors are most strongly associated with gambling severity, and

which may be less strongly related, and the correction of which

errors is more associated with reduction in gambling severity.

The selection of instruments for measuring PG carries signifi-

cant importance in this area. Most studies rely primarily on the

SOGS, which is intended as a screen and not a diagnostic instru-

ment, and is more useful in research than in clinical settings. It is,to some extent, reassuring that a number of the studies combined

SOGS with diagnostic instruments or other instruments that reflect

the diagnostic symptom structure of PG and are more suited to

clinical settings, and that the results using diagnostic instruments

were broadly consistent with those from studies that used SOGS.

Future research will be best served to continue the trend of utiliz-

ing DSMcriteria-based measures of gambling severity.

The literature of distortions has reached a point where particular

distortions may be examined on their own, without a broader focus

on distortions at large. Future research should capitalize on this

opportunity, refining both theoretical understanding of the impact

of cognitive distortions on gambling problems and clinical ap-

proaches based on correcting those distortions.

References

American Psychiatric Association. (1994). Diagnostic and statistical man-

ual of mental disorders (4th ed.). Washington, DC: Author.

Baboushkin, H. R., Hardoon, K. K., Derevensky, J. L., & Gupta, R. (2001).

Underlying cognitions in gambling behavior among university students.

Journal of Applied Social Psychology, 31, 14091430. doi:10.1111/j

.1559-1816.2001.tb02680.x

Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and

pathological gambling. Addiction, 97, 487499. doi:10.1046/j.1360-

0443.2002.00015.x

Breen, R. B., & Zuckerman, M. (1999). Chasing in gambling behavior:

Personality and cognitive determinants. Personality and Individual Dif-

ferences, 27, 10971111. doi:10.1016/S0191-8869(99)00052-5

Callan, M. J., Ellard, J. H., Shead, N. W., & Hodgins, D. C. (2008).

Gambling as a search for justice: Examining the role of personal relative

deprivation in gambling urges and gambling behavior. Personality and

Social Psychology Bulletin, 34, 15141529. doi:10.1177/

0146167208322956

Chase, H. W., & Clark, L. (2010). Gambling severity predicts midbrain

response to near-miss outcomes. The Journal of Neuroscience, 30,

6180 6187. doi:10.1523/JNEUROSCI.5758-09.2010

Chiu, J., & Storm, L. (2010). Personality, perceived luck and gambling

attitudes as predictors of gambling involvement. Journal of Gambling

Studies, 26, 205227. doi:10.1007/s10899-009-9160-x

Clark, L., Lawrence, A. J., Astley-Jones, F., & Gray, N. (2009). Gambling

near-misses enhance motivation to gamble and recruit win-related brain

circuitry.Neuron, 61, 481490. doi:10.1016/j.neuron.2008.12.031

Darke, P. R., & Freedman, J. L. (1997). The belief in good luck scale.

Journal of Research in Personality, 31,486511. doi:10.1006/jrpe.1997

.2197

Doiron, J. P., & Nicki, R. M. (2007). Prevention of pathological gambling:

A randomized controlled trial.Cognitive Behaviour Therapy, 36,7484.doi:10.1080/16506070601092966

Emond, M. S., & Marmurek, H. H. C. (2010). Gambling related cognitions

mediate the association between thinking style and problem gambling

severity. Journal of Gambling Studies, 26, 257267. doi:10.1007/

s10899-009-9164-6

Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index:

Final report. Ottawa, Ontario, Canada: Canadian Centre on Substance

Abuse.

Fortune, E. E., & Goodie, A. S. (2012). Cognitive distortions as a compo-

nent and treatment focus of pathological gambling: A review. Psychol-

ogy of Addictive Behaviors, 26, 298 310. doi:10.1037/a0026422

Gadboury, A., & Ladouceur, R. (1989). Erroneous perceptions and gam-

bling. Journal of Social Behavior & Personality, 4, 411420.

Gerstein, D. R., Volberg, R. A., Harwood, R., & Christiansen, E. M.

(1999). Gambling impact and behavior study: Report to the national

gambling impact study commission. Chicago, IL: National Opinion

Research Center, University of Chicago.

Gilovich, T., Vallone, R., & Tversky, A. (1985). The hot hand in basket-

ball: On the misperception of random sequences. Cognitive Psychology,

17,295314. doi:10.1016/0010-0285(85)90010-6

Goodie, A. S. (2003). The effects of control on betting: Paradoxical betting

on items of high confidence with low value. Journal of Experimental

Psychology: Learning, Memory, and Cognition, 29, 598610. doi:

10.1037/0278-7393.29.4.598

Goodie, A. S. (2005). The role of perceived control and overconfidence in

pathological gambling. Journal of Gambling Studies, 21, 481502. doi:

10.1007/s10899-005-5559-1

Goodie, A. S., MacKillop, J., Miller, J. D., Campbell, W. K., Lance, C. E.,

Fortune, E. E., . . . Meisel, M. K. (2010). Motivational pathways topathological gambling. Unpublished manuscript, University of Georgia.

Gooding, P., & Tarrier, N. (2009). A systematic review and meta-analysis

of cognitive-behavioural interventions to reduce problem gambling

Hedging our bets? Behaviour Research and Therapy, 47, 592607.

doi:10.1016/j.brat.2009.04.002

Grant, J. E., Steinberg, M. A., Kim, S. W., Rounsaville, B. J., & Potenza,

M. N. (2004). Preliminary validity and reliability testing of a structured

clinical interview for pathological gambling. Psychiatry Research, 128,

7988. doi:10.1016/j.psychres.2004.05.006

Griffiths, M. D. (1994). The role of cognitive bias and skill in fruit machine

gambling. British Journal of Psychology, 85, 351369. doi:10.1111/j

.2044-8295.1994.tb02529.x


8/12/2019 2013-06064-001

14/15

Haigh, J. (1997). The statistics of the national lottery. Journal of the Royal

Statistical Society, Series A, Statistics in Society, 160, 187206. doi:

10.1111/1467-985X.00056

Hardoon, K. K., Baboushkin, H. R., Derevensky, J. L., & Gupta, R. (2001).

Underlying cognitions in the selection of lottery tickets. Journal of

Clinical Psychology, 57, 749 763. doi:10.1002/jclp.1047

Holtgraves, T., & Skeel, J. (1992). Cognitive biases in playing the lottery:

Estimating the odds and choosing the numbers. Journal of Applied

Social Psychology, 22, 934952. doi:10.1111/j.1559-1816.1992

.tb00935.x

Jacobsen, L. H., Knudsen, A. K., Krogh, E., Pallesen, S., & Molde, H.

(2007). An overview of cognitive mechanisms in pathological gambling.

Nordic Psychology, 59, 347361. doi:10.1027/1901-2276.59.4.347

Jefferson, S., Doiron, J., Nicki, R., & MacLean, A. (2004). Further psy-

chometric development of the Informational Biases Scale: An instru-

ment designed to assess gambling cognitive distortions in video lottery

terminal players. Gambling Research: Journal of the National Associ-

ation for Gambling Studies (Australia), 16, 2839.

Jefferson, S., & Nicki, R. (2003). A new instrument to measure cognitive

distortions in video lottery terminal users: The informational biases scale

(IBS). Journal of gambling studies, 19, 387403. doi:10.1023/A:

1026327926024Joukhador, J., Blaszczynski, A., & MacCallum, F. (2004). Superstitious

beliefs in gambling among problem and non-problem gamblers: Prelim-

inary data. Journal of Gambling Studies, 20, 171180. doi:10.1023/B:

JOGS.0000022308.27774.2b

Joukhador, J., MacCallum, F., & Blaszczynski, A. (2003). Differences in

cognitive distortions between problem and social gamblers. Psycholog-

ical Reports, 92, 12031214.

Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment

of representativeness. Cognitive Psychology, 3, 430 454. doi:10.1016/

0010-0285(72)90016-3

Koriat, A., Lichtenstein, S., & Fischhoff, B. (1980). Reasons for confi-

dence.Journal of Experimental Psychology: Human Learning and Mem-

ory, 6, 107118. doi:10.1037/0278-7393.6.2.107

Ladouceur, R. (2004). Perceptions among pathological and nonpathologi-

cal gamblers. Addictive Behaviors, 29, 555565. doi:10.1016/j.addbeh.2003.08.025

Ladouceur, R., Sylvain, C., Letarte, H., Giroux, I., & Jacques, C. (1998).

Cognitive treatment of pathological gamblers. Behaviour Research and

Therapy, 36, 11111119. doi:10.1016/S0005-7967(98)00086-2

Lakey, C. E., Goodie, A. S., Lance, C. E., Stinchfield, R., & Winters, K. C.

(2007). Examining DSM-IV criteria in gambling pathology: Psychomet-

ric properties and evidence from cognitive biases. Journal of Gambling

Studies, 23, 479498. doi:10.1007/s10899-007-9063-7

Langer, E. J. (1975). The illusion of control. Journal of Personality and

Social Psychology, 32, 311328. doi:10.1037/0022-3514.32.2.311

Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen

(SOGS): A new instrument for the identification of problem gamblers.

The American Journal of Psychiatry, 144, 11841188.

MacKillop, J., Anderson, E. J., Castelda, B. A., Mattson, R. E., & Don-

ovick, P. J. (2006). Convergent validity of measures of cognitive dis-

tortions, impulsivity, and time perspective with pathological gambling.

Psychology of Addictive Behaviors, 20, 7579. doi:10.1037/0893-164X

.20.1.75

Maltby, J., Day, L., Gill, P., Colley, A., & Wood, A. M. (2008). Beliefs

around luck: Confirming the empirical conceptualization of beliefs

around luck and the development of the Darke and Freedman

beliefs around luck scale. Personality and Individual Differences, 45,

655660. doi:10.1016/j.paid.2008.07.010

Mattson, R. E., MacKillop, J., Castelda, B. A., Anderson, E. J., & Don-

ovick, P. J. (2008). The factor structure of gambling-related cognitions

in an undergraduate university sample. Journal of Psychopathology and

Behavioral Assessment, 30,229234. doi:10.1007/s10862-007-9063-z

Mitrovic, D. V., & Brown, J. (2009). Poker mania and problem gambling:

A study of distorted cognitions, motivation and alexithymia. Journal of

Gambling Studies, 25, 489 502. doi:10.1007/s10899-009-9140-1

Moodie, C. (2007). An exploratory investigation into the erroneous cog-

nitions of pathological and social fruit machine gamblers. Journal of

Gambling Issues, 19, 3150. doi:10.4309/jgi.2007.19.9

Moodie, C. (2008). Student gambling, erroneous cognitions, and awareness

of treatment in Scotland. Journal of Gambling Issues, 21, 3055. doi:

10.4309/jgi.2008.21.5

Neighbors, C., Lostutter, T. W., Larimer, M. E., & Takuski, R. Y. (2002).

Measuring gambling outcomes among college students.Journal of Gam-

bling Studies, 18, 339 360. doi:10.1023/A:1021013132430

Oei, T. P., Lin, J., & Raylu, N. (2007). Validation of the Chinese version

of the Gambling Related Cognitions Scale (GRCS-C). Journal of Gam-

bling Studies, 23, 309 322. doi:10.1007/s10899-006-9040-6

Oei, T. P., Lin, J., & Raylu, N. (2008). The relationship between gambling

cognitions, psychological states, and gambling: A cross-cultural study of

Chinese and Caucasians in Australia. Journal of Cross-Cultural Psy-

chology, 39, 147161. doi:10.1177/0022022107312587

Petry, M. P. (2004). Pathological gambling: Etiology, comorbidity, and

treatment. Washington, DC: American Psychological Association.

Pike, C. K. (2002). Measuring video gambling: Instrument developmentand validation. Research on Social Work Practice, 12, 389407. doi:

10.1177/1049731502012003004

Raylu, N., & Oei, T. P. S. (2004). The gambling related cognition scale

(GRCS): Development, confirmatory factor validation and psychometric

properties. Addiction, 99, 757769. doi:10.1111/j.1360-0443.2004

.00753.x

Rogers, P., & Webley, P. (2001). It could be us!: Cognitive and social

psychological factors in UK national lottery play. Applied Psychology:

An International Review, 50,181199. doi:10.1111/1464-0597.00053

Shaffer, H. J., LaBrie, R., Scanlan, K. M., & Cummings, T. N. (1994).

Pathological gambling among adolescents: Massachusetts Gambling

Screen (MAGS). Journal of Gambling Studies, 10, 339362. doi:

10.1007/BF02104901

Steenbergh, T. A., Meyers, A. W., May, R. K., & Whelan, J. P. (2002).

Development and validation of the Gamblers Beliefs Questionnaire.

Psychology of Addictive Behaviors, 16, 143149. doi:10.1037/0893-

164X.16.2.143

Strong, D. R., Breen, R. B., & Lejuez, C. W. (2004). Using item response

theory to examine gambling attitudes and beliefs. Personality and Indi-

vidual Differences, 36, 15151529. doi:10.1016/j.paid.2003.06.001

Strong, D. R., Daughters, S. B., Lejuez, C. W., & Breen, R. B. (2004).

Using the Rasch model to develop a revised gambling attitudes and

beliefs scale (GABS) for use with male college student gamblers. Sub-

stance Use & Misuse, 39, 10131024. doi:10.1081/JA-120030897

Tochkov, K. (2009). The effects of anticipated regret on risk preferences of

social and problem gamblers. Judgment and Decision Making, 4, 227

234.

Toneatto, T. (1999). Cognitive psychopathology of problem gambling.

S ub st an ce U se & M is us e, 3 4, 15931604. doi:10.3109/10826089909039417

Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers.

Psychological Bulletin, 76, 105110. doi:10.1037/h0031322

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heu-

ristics and biases. Science, 185, 11241131. doi:10.1126/science.185

.4157.1124

Wagenaar, W. A. (1988). Paradoxes of gambling behaviour. London, UK:

Lawrence Erlbaum.

Walker, M. B. (1992). The psychology of gambling. Oxford, UK: Perga-

mon Press.

Wohl, M. J. A., & Enzle, M. E. (2002). The deployment of personal luck:

Sympathetic magic and illusory control in games of pure chance.Per-


8/12/2019 2013-06064-001

15/15

sonality and Social Psychology Bulletin, 28, 13881397. doi:10.1177/

014616702236870

Wohl, M. J. A., Young, M. M., & Hart, K. E. (2005). Untreated young

gamblers with game-specific problems: Self-concept involving luck,

gambling ecology and delay in seeking professional treatment. Addiction

Research and Therapy, 13, 445 459. doi:10.1080/16066350500168444

Wohl, M. J. A., Young, M. M., & Hart, K. E. (2007). Self-perceptions of

dispositional luck: Relationship to DSM gambling symptoms, subjectiveenjoyment of gambling and treatment readiness. Substance Use & Mis-

use, 42, 43 63. doi:10.1080/10826080601094223

Xian, H., Shah, K. R., Phillips, S. M., Scherrer, J. F., Volberg, R., & Eisen,

S. A. (2008). The association of cognitive distortions with problem and

pathological gambling in adult male twins. Psychiatry Research, 160,

300307. doi:10.1016/j.psychres.2007.08.007

Young, M. M., Wohl, M. J. A., Matheson, K., Baumann, S., & Anisman,

H. (2008). The desire to gamble: The influence of outcomes on the

priming effects of a gambling episode. Journal of Gambling Studies, 24,

275293. doi:10.1007/s10899-008-9093-9

Received December 21, 2011

Revision received September 11, 2012

Accepted September 15, 2012


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