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Construct Development for the FocaL Adult Gambling Screen (FLAGS): A Risk Measurement for Gambling Harm and Problem Gambling Associated with Electronic Gambling Machines

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This is the first of two papers describing the development of the FocaL Adult Gambling Screen for Electronic Gambling Machine players (FLAGS-EGM). FLAGS-EGM is a measurement approach for identifying gambling risk, a tool that incorporates separate reflective and formative constructs into a single instrument. A set of statements was developed that captured ten constructs associated with gambling risk or which were considered components of problem gambling. Following completion of focus groups with regular slot players, a survey with the reduced set of statements was then administered to a sample of 374 casino slot players in Ontario, Canada. Nine of the proposed constructs passed tests for reliability and validity (Risky Cognitions Beliefs, Risky Cognitions Motives, Preoccupation Desire, Risky Practices Earlier, Risky Practices Later, Impaired Control Continue a Session, Impaired Control Begin a Session, Negative Consequences, and Persistence). A tenth construct (Preoccupation Obsession) requires further development through the addition of improved statements. © 2015 Centre for Addiction and Mental Health. All rights reserved.
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Construct Development for the FocaL Adult Gambling
Screen (FLAGS): A Risk Measurement for Gambling
Harm and Problem Gambling Associated with
Electronic Gambling Machines
Tony Schellinck,
1,2
Tracy Schrans,
2
Heather Schellinck,
1
& Michael Bliemel
1
1
Dalhousie University, Halifax, Nova Scotia, Canada.
2
Focal Research Consultants Limited, Halifax, Nova Scotia, Canada.
Abstract
This is the rst of two papers describing the development of the FocaL Adult
Gambling Screen for Electronic Gambling Machine players (FLAGS-EGM).
FLAGS-EGM is a measurement approach for identifying gambling risk, a tool
that incorporates separate reective and formative constructs into a single
instrument. A set of statements was developed that captured ten constructs
associated with gambling risk or which were considered components of problem
gambling. Following completion of focus groups with regular slot players, a survey
with the reduced set of statements was then administered to a sample of 374 casino
slot players in Ontario, Canada. Nine of the proposed constructs passed tests for
reliability and validity (Risky Cognitions Beliefs, Risky Cognitions Motives,
Preoccupation Desire, Risky Practices Earlier, Risky Practices Later, Impaired
Control Continue a Session, Impaired Control Begin a Session, Negative
Consequences, and Persistence). A tenth construct (Preoccupation Obsession)
requires further development through the addition of improved statements.
Résumé
Voici le premier de deux articles décrivant la mise au point dun instrument appelé
le FocaL Adult Gambling Screen for Electronic Gambling Machine players
(FLAGS-EGM). Il sagit dune méthode dévaluation du risque de dépendance au
jeu qui réunit deux volets distincts en un seul instrument, lun réexif et lautre
formatif. Nous avons formulé un ensemble dénoncés traduisant dix constructs
associés au risque ou considérés comme des éléments constitutifs des problèmes de
jeu. Après avoir mené des groupes de discussion avec des joueurs qui sadonnent
régulièrement aux machines à sous, un questionnaire formulé à partir dun ensemble
limité dénoncés a été administré à 374 joueurs en Ontario (Canada). Neuf constructs
140
Journal of Gambling Issues
Issue 30, May 2015 DOI: http://dx.doi.org/10.4309/jgi.2015.30.7
http://igi.camh.net/doi/pdf/10.4309/jgi.2015.30.7
sur dix ont réussi les tests de abilité et de validité (croyances cognitives risquées,
motivations cognitives risquées, préoccupation relative au désir, pratiques risquées
antécédentes, manque de contrôlecontinue la séance, manque de contrôleentame
une séance, pratiques risquées ultérieures, conséquences négatives et persistence). Un
dixième construct (préoccupation relative à lobsession) nécessitera une mise au point
grâce à lajout dénoncés améliorés.
Introduction
With the current emphasis on preventing gamblers from self-harm prior to the
development of gambling problems, an instrument that clearly identies those at risk
is urgently required. Originally a number of screens, such as the South Oaks
Gambling Screen (SOGS) (Lesieur & Blume, 1987, 1993) and the DSM-IV-TR (4th
ed., text rev.; DSMIVTR; American Psychiatric Association, 2000), were designed
to identify problem gamblers among treatment populations. Debate surrounding the
identication of pathological versus problem gamblers and the utility of the existing
gambling screens for general population use (Dickerson, 1993; Lesieur & Blume,
1993; Volberg, Dickerson, Ladouceur, & Abbott, 1996; Walker & Dickerson, 1996)
led to the development of other measures, including the National Opinion Research
Center DSMIV-based Screen for Gambling Problems (NODS) (Gerstein et al.,
1999), the Canadian Problem Gambling Index (Ferris & Wynne, 2001), the Victoria
Gambling Screen ( Tolchard & Battersby 2010), the Gamblers Beliefs Questionnaire
(Steenbergh, Meyers, May, & Whelan, 2002) and the Gambling Related Cognitions
Scale, developed by Raylu and Oei (2004). Several of these newer screens also
included risk estimates as a component of identifying problem gambling, yet none of
the screens incorporated the use of unique constructs specically to identify gambling
risk as a component separate from problem gambling.
Presently, the Problem Gambling Severity Index (PGSI) component of the Canadian
Problem Gambling Index appears to be used most often to assess risk as a
component of identifying problem gambling severity. With this instrument, a score
of 0 is labelled ‘‘ no risk,’’ scores of 12 are labelled ‘‘ low risk,’’ 37 are labelled
‘‘ moderate risk,’’ and individuals scoring 8 or higher are classied as problem
gamblers (Ferris & Wynne, 2001). As such, the PGSI views risk as part of a single
concept, and includes classication criteria for two risk categories. It assumes that
risk is captured as a lower score whereas higher scores represent problem gambling.
Although no evidence exists to suggest that (1) lower scores reect lower risk as
opposed to lower certainty that someone is a problem gambler, or (2) the severity of
the problem gambling is indeed lower, it nevertheless seems likely that some at-risk
gamblers are in fact identied through this instrument. Nonetheless, no published
research has demonstrated that the PGSI or any other instrument that identies or
categorizes gamblers actually predicts risk due to problem gambling.
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Currie, Hodgins, and Casey (2013) compared the characteristics of gamblers in
the four PGSI categories, on variables previous research had found were
associated with pathological gambling, to determine if there were signicant
differences among the gamblers in each category. The reason for doing so was as
follows: if the gamblers were signicantly different on these dimensions, then the
gamblers could in turn be considered as belonging in valid and distinct groups.
Currie et al. found not only that the no-risk group was distinctively different from
the low- and moderate-risk groups, as well as the problem gambling groups, but also
that few differences existed between the low- and moderate-risk groups. This nding
suggests the PGSI low- and moderate- risk groups do not in fact comprise distinct
categories of gamblers. Currie et al. did improve the distinctiveness of the low and
moderate risk groups by redening the PGSI score thresholds (from 13 for low risk to
14) but this modication did not deal with the fact that the PGSI design, by relying
on a continuum, has consequent limitations. This problem suggests in turn the need for
an instrument that is better able to form distinct groups of gamblers with differing risk
proles.
Thomas, Jackson, and Blaszczynski (2003) have emphasized the need for a tool to
independently determine risk as distinctive from problem or pathological gambling.
Shaffer, LaBrie, LaPlante, Nelson, and Stanton (2004) also pointed out the pressing
need to investigate risk and protective factors that inuence the onset of gambling
disorders. Given the limitations of existing problem gambling screens in identifying
risk, the FocaL Adult Gambling Screen (FLAGS-EGM) was developed specically
to address this measurement gap. Our instrument is designed to work similarly to
screens the medical community has established to identify factors for specic high-
risk conditions (e.g., Naghavi, Falk, Hecht, & Shah, 2006 ).
Using item response theory and statistical modeling, with the detailed play behaviour
and attitudinal data gathered for regular machine gamblers during the 1998 Nova
Scotia Video Lottery Players Study, Schellinck and Schrans (1998) developed the
rst hierarchical model of the antecedents of problem gambling for EGMs. Three
principal considerations underlay underlied the creation of FLAGS-EGM:
1. The ability to identify independently, gambling risk prior to the development of
harm and problem gambling;
2. The suitability of the instrument for multiple use including self-assessment,
prevalence, social policy and responsible gambling evaluation and public health
surveillance; and
3. The sensitivity of the instrument such that it (1) identied accurately changes
in risk and problem gambling levels over time, and (2) provided insight
sufcient to inform action by both individual and policy makers. Such insight
was to be indicated through erroneous beliefs, inappropriate motives, and
risky behaviours, all as exhibited by gamblers themselves. It was also to be
indicated through the nature of the harms being experienced, and through
the existence of impaired control and preoccupation among the gambling
population.
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Operationally Dening Gambling Harm and Problems
As the rst step in achieving these objectives, an operational denition for gambling
harm and problem gambling, as well as of risk, was in all three cases required. In
1994, the American Psychiatric Association (APA) dened pathological gambling as
the ‘‘ persistent and recurrent maladaptive gambling behaviour that disrupts
personal, family and vocational pursuits’’ (4th ed., text rev.; DSMIVTR; APA,
2000). Individuals so dened are preoccupied with gambling, may be unable to
control their gambling, and both chase losses and suffer negative consequences as a
result of these problems. Many subsequent denitions of problem and pathological
gambling have been advanced. Most of these denitions have concerned themselves
in particular with continued excessive involvement in gambling despite associated
negative consequences for the individual. More recently they have also dealt with
negative outcomes for the gamblers family, community or society at large (Neal,
Delfabbro, & ONeil, 2005). These authors also noted the need for an instrument
that would differentiate denitively among individuals at different levels of risk.
We have operationally dened problem gambling as the co-occurrence of two conditions
composed of ‘‘ negative consequences’’ (outcomes) and ‘‘ persistence’’ (behaviour). Problem
gamblers are characterized as those persons who have experienced negative consequences
directly related to gambling in the past 12 months and who persist in gambling despite the
occurrence of these negative consequences. Other characteristics such as those noted in the
DSM-IV (4th ed., text rev.; DSMIVTR; APA, 2000), such as loss of control, chasing
losses, and preoccupation, are conceptualized as risk factors leading to problem gambling,
and are used in the FLAGS-EGM model as precursors to gambling harm and problem
gambling. A key objective of the FLAGS-EGM measure is the development of constructs
to capture fully the dimensions of risk, harm and problem gambling.
Using Reective and Formative Constructs to Develop the Instrument
In creating this instrument, we included both reective and formative constructs.
Previously, researchers have developed and assessed most gambling screens based
only upon reective constructs. Reective constructs presuppose that an underlying
latent construct causes the observed variation in the measures (Nunnally, 1978). As
items within a reective construct are all indicative of the underlying latent variable,
high correlation among the items comprisingthe measure should result: in theory, a
gambler should endorse either all or none of the items being agged through the
construct. This method is a highly desirable for conceptualizing and measuring a
single homogeneous factor, or specic concept, such as preoccupation or persistence.
Either an individual meets the conditions for identication on this dimension or that
person does not. For example, a construct such as impaired control is best designed
as a reective construct. It is designed this way to capture the specic nature of a
gamblers tendency to nd it difcult to stop gambling once engaged in play.
In practice, those researchers developing or interpreting problem gambling screens
may assume that the number of items endorsed for a reective construct represents a
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
continuum. It is, however, incorrect to presuppose that the higher the number of
items selected, the greater the impact or severity. Indeed, research in the area of
construct development has challenged assumptions that constructs should always be
reective (Diamantopolos & Siguaw, 2006; MacKenzie, Podsakoff, & Jarvis, 2005),
and the relative merits of using reective and formative measures for theory
development are still being debated. General consensus argues that formative measures
are suitable for prediction of given outcomes when used with structural equation
modeling (SEM) (Diamantopoulos, Rieer, & Roth, 2008; Diamontaopoulos & Sigaw,
2006; Freeze & Raschke, 2007; Howell, Breivik, & Wilcox, 2007; Wilcox, Howell, &
Breivik, 2008).
In contrast to a reective measure, a formative construct is said to predict the latent
variable (Bollen & Lennox, 1991; Gefen, Straub, & Boudreau, 2000). The items
comprising formative constructs represent different, often uncorrelated dimensions
of the latent variable. Endorsement is additive such that the more items endorsed the
greater the severity of impact. This characteristic is a desirable one for an instrument
intended to identify levels of risk or harm: the more harm components an individual
endorses, the greater the severity of gambling problems, or, in the case of risk
components, the higher ones risk for problem gambling. To represent adequately
the scope of a variable, such as risky beliefs, would require a formative construct that
included various diverse concepts. Examples of those concepts include beliefs that
game outcomes could be inuenced, that chances of winning improved with
continued play, or that outcomes could be predicted. These beliefs may not all be
held by the same persons but all are associated with risk.
A problem may arise when a screen is used as a multi-purpose measure to assess
more than one dimension of gambling, e.g., problem gambling and an individuals
risk for developing problem gambling. Although characteristics of risk for becoming
a problem gambler and the characteristics of being a problem gambler are not
necessarily the same, the items retained in most instruments are all highly correlated
with each other. As a result, those gamblers who are at various stages of risk may be
undetected or misclassied. To resolve this problem, we included separate constructs
to measure individual risk elements.
The analyses undertaken to develop and test each of the constructs for the FLAGS-
EGM instrument was extensive and exceeded the scope of this paper. Consequently,
we have divided the work into two parts: this paper describes the rst phase of
the instrument design process, including (1) selection of the constructs, (2) a
description of the statement process, followed by the assignment of the statement
to the constructs, and (3) testing of the constructsvalidity and reliability.
A companion paper describes the development of the FLAGS-EGM instrument,
using Partial Least Squares (PLS) modeling to establish relationships among the
constructs in a path leading to problem gambling. The results of the PLS analysis in
the second paper have implications for the design and testing of the constructs we
refer to in the current article and the reader should consult that publication for
further information.
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Construct Design for FLAGS-EGM
Many of the general characteristics associated with problem gambling have already been
thoroughly described (Johansson, Grant, Kim, Odlaug, & Götestam, 2009; Turner,
Jain, Spence, & Zangeneh, 2008). To avoid the possibility of creating false positives and
to limit, in the development of our instrument, the size of FLAGS-EGM, we included
only gambling-specic constructs, each comprised of statements that referred to the
respondents gambling cognitions, behaviours or experiences. Based upon our original
research in this area (Schellinck & Schrans, 1998) and a comprehensive literature review,
we created the reective and formative constructs described below.
Reective Constructs
Preoccupation Obsession. An individual who is preoccupied with thoughts of
gambling has been dened as ‘‘ having a xation on gambling, is continually reliving
past experiences, planning the next outing and thinking about how to get money for
such an excursion’’ (4th ed., text rev.; DSMIVTR; APA, 2000). These criteria have
been found to be reliable and valid predictors of problem gambling (Hodgins, 2004;
Lakey, Goodie, Lance, Stincheld, & Winters, 2007; Stincheld, Govoni, & Frisch,
2005; Toce-Gerstein, Gerstein, & Volberg, 2009; Wickwire, Burke, Brown, Parker, &
May, 2008). Gamblers who are obsessed with their play think constantly about their
gambling. This fact in itself may not be considered a negative consequence of gambling;
however, these thoughts can become so prevalent that they are deemed harmful, insofar
as the person is tormented by these thoughts, is unable to function normally, or both. In
these circumstances, gambling is seen to have harmed the gambler.
Preoccupation Desire. Merely whiling away ones time with thoughts of gambling
(Preoccupation Desire) is considered a risk indicator that occurs in the absence of harm.
We have dened Preoccupation Desire as ‘‘ having a strong desire to gamble frequently
or as often as possible’’ ; other researchers have referred to this characteristic as
‘‘ craving’’ (Ashraoun & Rosenberg, 2012; Young & Wahl, 2009). Wanting to gamble
frequently may be a common characteristic of everyone who enjoys gambling; however,
a strong desire that leads to increased or more extreme gambling activity could be an
effective indicator of elevated risk. This characteristic could also be particularly relevant
in subsequent modeling analysis, if it is shown to be a precursor to Impaired Control
and Risky Practices.
Impaired Control Begin and Impaired Control Continue. Impaired control has been
characterized as ‘‘ repeated unsuccessful attempts to resist the urge to gamble in the
context of genuine desire to cease’’ (Blaszczynski & Nower, 2002) as well as an inability
to resist opportunities to gamble (e.g., begin a session) and cease the activity once
engaged (e.g., continue a session) (Dickerson & OConnor, 2006). We operationally
dened the reective construct, Impaired Control, as a gambling-specic construct
based upon an individuals personal experience.
Impaired control has been considered a cause of problem gambling (Ladouceur,
Cantinotti, & Tavares, 2007) and thus has the potential as a risk indicator. Gamblers
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
may attempt to justify their losses by perceiving them as a product of a lack of control,
whether or not this is actually the case (Dickerson & OConnor, 2006). Regardless of
whether the action is perceived or factual, if a gambler recognizes that he is acting
contrary to his intentions, this perception may be an indicator of risk. In creating our
construct we modied several statements from the Scale of Gambling Choices (Baron,
Dickerson, & Blaszczynski, 1995). We also used the two behaviours described by
Dickerson and OConnor (2006). In doing so we considered impaired control to be a
two-dimensional construct such that an individual could suffer from one or both of the
following problems: (1) Impaired Control Continue a session, dened as an inability to
cease gambling once engaged, or (2) Impaired Control Begin a session, dened as an
inability to resist starting a session .
Persistence. Persistence is generally referred to as playing for a long time within a
session, particularly when losing (Dickerson, 1993), and as excessive play in addition to
continuing to play in attempts to recover previous losses (Breen & Zuckerman, 1999).
The term has also been used in the context of assessing the number of trials at play
(Kassinove, 1999) or sessions gambled per month (Ladouceur & Sévigny, 2005).
Whether or not the player suffers harm from playing over an extended period of time is
not generally addressed. Given our interest in using the concept of persistence to
characterize problem gambling, we dened the reective construct as the engagement of
risky practices over an extended period despite that behaviour leading to negative
consequences. We did not include ‘‘ chasing losses’’ in our Negative Consequences
construct, and instead positioned it as an element of the formative construct Risky
Practices, where it could serve as a risk indicator or precursor to negative outcomes.
Formative Constructs. Formative constructs are basically lists of items that as
exhaustively as possible capture and thus dene the latent variable being measured.
A principal goal of this literature review was to identify a broad range of items and,
subsequently, to select those items that dened unique elements of the construct.
Thus, each item retained could contribute to the identication of individuals who
have indications of the latent variable.
Risky Cognitions Beliefs. Many gamblers believe irrationally they can use skill to
inuence the outcome of games that have completely random results. For example,
certain gamblers may think that pressing the buttons quickly on a gambling machine
will increase their odds of winning. Moreover, many players mistakenly believe that
the probability of winning is greater than is actually the case. This ‘‘ illusion of
control’’ concept advanced by Ladouceur and Walker (1996) and further dened by
Toneatto (1999) provided the background for many of the scales developed to
investigate the role of erroneous cognition in maintaining problem gambling. The
PGSI includes two statements regarding faulty cognitions (Ferris & Wynne, 2001),
and items assessing similar concepts have been used to discriminate successfully
between pathological/problem and non-pathological/non-problem gamblers
(Källmén, Andersson, & Andren, 2008; Raylu & Oei, 2004; Steenbergh et al.,
2002; van Holtz, van den Brink, Veltman, & Goudriann, 2010; Xian, Shah, Phillips,
Scherrer, Volberg, & Eisen, 2008). We created the formative construct Risky
146
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Cognitions Beliefs. The construct comprises belief statements consistently found in
the literature as being associated with risk for electronic gambling.
Risky Cognitions Motives. An individuals motivation is considered to be a
reection of the internal and external forces that direct him to take action. For
example, a person could be internally motivated to gamble for the feeling of
excitement or externally motivated to gamble to win money (Lee, Chae, Lee, & Kim,
2007). The Motivation Towards Gambling Scale incorporated the following
motivations: reward seeking; self-imposed pressures, such as the need for
recognition; and goals, such as socialization, knowledge, accomplishment and
stimulation (Chantal, Vallerand, & Vallières, 1995). Subsequent work by Clarke
(2004, 2005, 2008) and Pantalon, Maciejewski, Desai, and Potenza (2008) conrmed
the role of these factors in shaping play behaviours. The motivation ‘‘ to escape ones
problems’’ is also highly correlated with both frequency of gambling and progression
towards pathological gambling (Clarke, 2008; Nelson, Gebauer, Labrie, & Shaffer,
2009; Nower & Blaszczynski, 2010; Thomas, Allen, & Phillips, 2009). Our construct
Risky Cognitions Motives only contains those factors found to be associated with
problem gambling and does not include all the motivators for gambling described in
the literature.
Risky Practices (Earlier and Later). Problem gamblers have been found to
engage in behaviours such as chasing losses, and participating in illegal activities to
nance gambling and lying about the extent of their gambling (4th ed., text rev.;
DSMIVTR; APA, 2000). Such risky practices may also occur during play. For
example, an individual may attempt to borrow money from another player
(Schellinck & Schrans, 1998). As playing behaviour begins to cause problems,
gamblers engage in a variety of risky practices that may escalate in severity
(Campbell-Meiklejohn, Woolrich, Passingham, & Rogers, 2008; Hong, Sacco, &
Cunningham-Williams, 2009; Sumitra & Miller, 2005). Fewer gamblers tend to
engage in the more risky practices (Schellinck & Schrans, 1998; Schellinck, Schrans,
& Walsh, 2000). Although players may frequently use maximum bet options, or use a
bank card to obtain additional cash during a session of play, they less commonly
borrow money on their credit cards to keep playing. If the endorsement rates varied
substantially among the risky behaviours analysed, we would consider creating two
formative constructs, ‘‘ Risky Practices Earlier’’ and ‘‘ Risky Practices Later.’’
Negative Consequences. Several problem gambling screens (Ferris & Wynne,
2001; Lesieur & Blume, 1987; Toce-Gerstein et al., 2009) include statements
regarding harmful outcomes. Our measure included characteristics described by
Thomas et al. (2009), such as negative impacts on work and nancial well-being,
problems with health, interpersonal relationships, and deceptive behaviour. We also
incorporated statements assessing the impact on the individuals sense of self-worth
as described by Suurvali, Cordingley, Hodgins, and Cunningham (2009), in this
formative construct. We have excluded more extreme consequences, such as
engaging in criminal behaviours and having suicidal tendencies, as these questions
were considered to be too threatening for a self-administered survey. As well, the less
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
severe consequences would be experienced rst so the instruments ability to identify
problem gamblers would not be compromised by their exclusion.
Method
Survey Development
We selected 190 prototype statements for assessment in the constructs. Each item
selected for testing was hypothesized to be associated with gambling risk or harm as
variously supported by the literature, the OPGRC risk framework (Simpson,
Goodstadt, Wynne, & Williams, 2008) and our own research examining
determinants of problem gambling (Schellinck & Schrans, 1998; Schellinck et al.,
2000). The instrument was evaluated in a two phase process with regular slot
machine gamblers. First, six qualitative focus groups (n=63) were completed to
conduct a beta-test of the items. Second, based upon the outcome of the focus
groups, a quantitative telephone survey (n=374) with a reduced instrument was
completed. The research was subject to independent ethics approval by the
Institutional Review Board Services.
Participants
Regular slot machine gamblers, i.e., those gamblers who on average played once a
month or more over the past 12 months, were approached at the Slots at Western
Fair in London Ontario over a one-week period (nE650), prescreened and invited to
join a condential research panel. The original panel sample was generated through
on-site recruitment over a ve-day period including three weekdays (Tuesday,
Wednesday and Thursday) and the weekend (Saturday and Sunday) over day and
evening shifts covering periods from 8 a.m. to 10 p.m. Whereas the resulting sample
constituted a convenience sample it was nonetheless fairly representative of the
population of frequent gamblers, with high cooperation rates during the recruiting
process (refusal rates o20%).
ParticipantsQualitative Assessment. Panel members were then re-contacted
to take part in a series of focus groups to assess a beta version of the instrument. We
did this to assist in determining the clarity of the items. Participants were recruited
and grouped depending upon their risk score on the PGSI, (i.e., 0, 14, or 5 or more)
and how long they had been gambling on a regular basis of once a month or more
(less than 2 years, or 2 or more years). An equal number of men and women took
part in the sessions, with ages ranging from 23 to 74 years. All participants arrived
30 minutes before the session to self-complete a beta version of the instrument.
During the discussion that followed, participants referred to a blank copy of the
items to preserve the condentiality of their personal responses. All sessions were
audiotaped and the tapes transcribed by independent support personnel. An
independent observer kept detailed notes during the sessions for use in thematic
analysis of the statements. The discussion groups lasted approximately two hours
and participants received an honorarium of $60.00 for taking part in the sessions.
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
If the participants had several interpretations of the wording, the statements were
either revised or discarded as unsuitable. Many of the statements were intentionally
similar in wording; those examples found to align most closely to our original
intended meaning were retained, with less denitive versions eliminated. Based upon
group responses and subsequent assessment of the 63 surveys, 132 dichotomous
response statements were generated for testing in a larger quantitative sample.
ParticipantsQuantitative Evaluation. The remaining panel members were
contacted by telephone and asked to complete the reduced instrument. Informed
consent was obtained from respondents before data collection took place.
Individuals currently receiving assistance for substance use, gambling, or a mental
health issue were excluded, as were those persons who worked for (1) the media, (2) a
political or lobby group, (3) Addiction Services, or (4) Ontario Lottery Gaming or an
afliate. Participants who wished to do so had their names entered into a draw for
one of four $100 grocery gift certicates (with no cash value) at a retail grocer of
their choice. Of 422 eligible panel members, 374 met the selection criteria, i.e., played
slots more than once a month, were not a member of any of the excluded groups, and
responded to the survey. This process resulted in a completion rate of 69.2 %. Prior
to analyses, 18% of those persons who responded to the survey were re-contacted by
the eld staff supervisor and key questions were repeated to ensure consistency and
accuracy in responses to the survey. This process ensured that (1) the respondents
were answering honestly and that they were not having problems remembering key
estimates, and (2) that the interviewers were administering the survey correctly. No
surveys needed to be deleted upon completion of this process.
This sample was comprised of 150 males and 224 females; the median age was 63
with ages ranging from 23 to 89 years. Over half (53.5%) of the sample played the
slots weekly, 0.8% played daily and 45.7% played less than weekly on the slots.
According to the PGSI 54.8% of the respondents were no risk gamblers, 19.3% were
low risk gamblers, 20.3% were medium risk gamblers and 5.6% were problem
gamblers. The test instrument and classication questions took approximately
26 minutes to administer (range 2046). Statements were randomized for each
participant to reduce the risk of common method bias (Bliemel & Hassanein, 2007).
Data Analysis and Results
Statement Selection Criteria
Following analysis, the 132 statements were reduced to 53 items across the 10
constructs (see Appendix). As described below, the criteria for selecting the
statements for the proposed constructs differed depending on whether the constructs
were reective or formative. Three steps were used to select the statements for the
reective constructs. First, exploratory principal components analysis (PCA) was
performed on the three sets of statements designed to measure the original
constructsimpaired control, preoccupation and persistence. Those statements
having loadings greater than 0.5 on the resulting varimax rotated constructs were
retained (Table 1). Second, statements comprising the resulting ve constructs that
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
had substantially different endorsement rates from the mode endorsement rate for
each construct were dropped. Third, if a construct still had more than ve statements
those statements with the lowest loadings on the component were dropped so that the
maximum number of statements in a construct to be tested for reliability and validity
was ve.
The formative construct statement selection process itself had three steps. First, PCA
analysis of those statements designed for a construct was conducted, though for
formative constructs it was expected that many components would be formed, and
Table 1
Selection Criteria and Descriptive Statistics for the Reective Constructs
FLAGS-EGM Reective Construct and Statements Component
Loading
Frequency
Endorsed
Freq.
Rank
Preoccupation Desire
Pre-des 1: If I could play the machines all the time I would. 0.83 26.2% 22
Pre-des2: I wish I could gamble on the slots more often. 0.80 28.9% 17
Pre-des3: I like to play the slot machines every chance I get. 0.77 29.1% 16
Pre-des4: I would like to play the slots almost every day. 0.74 20.3% 41
Preoccupation Obsession
Pre-obs1: I sometimes dream about playing the slot machines. 0.78 8.0% 83
Pre-obs2: I spend more time than I used to thinking about
playing the slots.
0.70 9.4% 76
Impaired Control Continue a Session
IC-cont1: I often spend more money gambling than I intended. 0.86 24.3% 27
IC-cont2: Even when I intend to spend a few dollars gambling,
I often end up spending much more.
0.85 25.9% 23
IC-cont3: I sometimes gamble with money that I cant really
afford to lose.
0.78 21.4% 38
IC-cont4: Once I have started gambling on the slots I nd it
very hard to stop.
0.74 24.1% 29
IC-cont5: I often spend more time gambling than I intend to. 0.71 24.1% 28
Impaired Control Begin a Session
IC-begin1: I have tried to cut back on my slots play with
little success.
0.87 9.1% 77
IC-begin2: I have tried unsuccessfully to stop or reduce my
gambling on the slots.
0.85 8.0% 84
IC-begin3: There have been times I have gambled despite
my desire not to.
0.74 15.0% 57
Persistence
Persist1: I continue to play the machines despite experiencing
problems or other negative consequences.
0.88 10.2% 72
Persist2: I continue to gamble despite the bad things that
happen to me.
0.85 10.2% 73
Persist3: Even if money is tight, I continue to play the slots
to get big wins.
0.80 8.0% 86
Persist4: I gamble even though I know it is likely to lead to
problems for me.
0.79 11.8% 67
150
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
that one or two statements would be selected from each component to ensure all
dimensions in the construct were captured. Second, the degree in overlap among the
statements was then tested for by examining the Variance Ination Factor (VIF)
score of the statements when tested together, and statements with too much overlap
with the other statements (i.e., VIF 410.0) were dropped. Third, endorsement rates
within the risky practices construct were compared to determine if certain statements
might indicate earlier or later levels of risk. The large disparity in endorsement rates
(4.0% 34.8%) led to the splitting of the construct into two (one indicating early
risky practices, the other later risky practices).
Reective Construct Statement Selection Criteria
The statements created for each reective construct were expected to reect a single
underlying latent variable. Several criteria, outlined below, were used to decide
which statements would be assigned to the reective constructs, which statements
would be dropped, and whether any statements might need modication of wording
to capture better the latent variable being measured.
1. Exploratory PCA was rst conducted on the responses to the 132 dichotomous
response statements, with the expectation that the statements designed for a
particular reective construct would load highly on the same component. A
separate analysis was conducted in each set of those statements designed to
measure one of the three original constructsimpaired control, preoccupation
and persistence. Following varimax rotation of components with eigenvalues
greater than 1, the items that had loadings of at least 0.50 were retained in the
construct. The loadings for the retained statements are reported in Table 1. Using
this method, the loadings could differ an average of 0.07 to 0.08 from tetrachoric
correlationsthat is, those correlations derived using specialized programs
designed specically for dichotomous data (Maguire, 2001). As we used this
analysis to choose items for further testing, we considered this level of accuracy to
be acceptable. Both the impaired control and preoccupation statements formed
two components as predicted by the literature. The statements in the two resulting
impaired control components dealt with, rst, an inability to cease play once
started (Impaired Control Continue) and, second, an inability to resist starting
new gambling sessions (Impaired Control Begin). The statements designed to
measure preoccupation also formed two components, those that indicated a
strong desire to gamble more often (Preoccupation Desire) and those that
indicated an overwhelming obsession with gambling (Preoccupation Obsession).
Though we started with three original constructs the outcome of conducting the
PCA analysis was the creation of ve reective constructs that conrmed what
was indicated in the literature.
2. For reective constructs, each item selected should have, if it is measuring the
same underlying latent variable, a similar rate of endorsement by gamblers
(Diamantopoulos & Winklhofer, 2001; Jarvis, MacKenzie, & Podsakoff, 2003;
MacKenzie, 2003). No clearly dened criteria exist in this context, but we
151
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
dropped statements if their endorsement rates differed by more than 10% from the
mode endorsement rate for statements in a single reective construct.
3. For each reective construct, among those with a minimum loading of 0.70, the
ve highest loading statements were chosen prior to testing the variables for
reliability and validity. A minimum of three statements was required for reliability
testing. The maximum number of items selected per reective construct was ve;
we chose this limit to minimize the total number of items in the instrument. At the
end of this selection process, one reective construct only had two statements that
loaded 0.70 or more on the component (Preoccupation Obsession), one had three
statements (Impaired Control Begin), two had four statements (Preoccupation
Desire, Persistence), and one had ve statements (Impaired Control Continue)
(see Table 1).
Formative Construct Statement Selection Criteria
1. Principal component analysis of the 132 statements was also used to help select
statements to be included in the formative constructs (Table 2). Components with
eigenvalues greater than one were selected and rotated using varimax rotation. A
number of principal components were formed that contained the statements
intended to compose the formative constructs. For example, ve components
were formed from the eleven statements tested for inclusion in the Risky
Cognitions Beliefs construct. We selected the highest loading statement from each
component for inclusion in this construct, resulting in ve statements capturing
ve distinctive belief components. Sometimes a statement intended for a
formative construct did not load strongly on any component, i.e., no loading
higher than 0.70, indicating relative independence from all other statements in the
set. This item was retained in the statements used to form the intended formative
construct, as it may have been measuring a unique characteristic in the construct
itself. In a few instances, particularly for the Negative Consequences construct, we
retained specic statements even though they loaded above 0.70 on a component
with another retained statement. We did this when the items appeared to capture
consequences that were different but which tended to happen to the same people
at such a rate that the items would load on the same component. If subsequent
testing using the Variation Ination Factor (VIF) scores indicated there was too
much overlap in the responses to the questions, we removed one of the items.
2. For each formative construct, the statements were required to have a VIF of less
than 10.0 (Diamantopoulos & Siguaw, 2006). Each set of indicators was tested
against the 10.0 criterion and statements contributing to high levels of
multicollinearity were removed until all VIFs in the construct fell below the
specied criterion. To maintain content validity where possible, a representative
item from each of the components of the PCA analyses was retained in the
construct.
3. Risky Practices was originally conceptualized as a single construct comprised of
twelve statements. When we examined endorsement rates, i.e., the number of
players selecting the item, a large range in endorsement levels were determined.
The nature of the statements being endorsed suggested these items would be better
152
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Table 2
Selection Criteria and Descriptive Statistics for the Formative Constructs
FLAGS-EGM Formative
Constructs and Statements
ComponentComponent
loading
Frequency
Endorsed
Freq.
Rank
T-Score for
Coefcients
Variance
Ination
Factor
Erroneous Cognitions Beliefs
Belief1: You can sometimes
tell when the machine is
about to pay out big because
the symbols start getting
closer to lining up on the line
(e.g., almost winning).
1 0.53 6.4% 92 4.67 1.10
Belief2: I feel the machines
are xed sometimes so
that you cantwinonthem.
2 0.81 67.6% 1 2.10 1.03
Belief3: It is important for me
to use a system or a strategy
when I play the machines.
3 0.80 9.9% 74 1.59 1.06
Belief4: I believe that in the
long run I can win playing
slots at the casino.
4 0.68 5.9% 97 3.35 1.04
Belief5: If a slot machine
hasnt had a big pay out in
a long time, it is more
likely to do so soon.
5 0.75 25.4% 25 1.84 1.11
Erroneous Cognitions Motives
Motive1: I sometimes play
the slots in hopes of
paying off my debts/bills.
6 0.65 11.0% 69 4.98 1.29
Motive2: Gambling on the
slotsisawayIcantrytoget
some money when I need it.
6 0.74 6.1% 95 2.83 1.25
Motive3: I sometimes play
the slots when Im feeling
down or depressed.
7 0.72 19.0% 44 5.58 1.32
Motive4: I can escape by
playing the slots whenever I
am worried or under stress
7 0.67 27.0% 18 2.29 1.23
Risky Behaviours Earlier
RBE1:I sometimes exceed the
amount of money I
intended to spend in order
to win back money I have
lost.
8 0.77 29.7% 14 7.62 1.52
REB2: When gambling on
the slots I usually use my
bank or debit card to get
more money so I can keep
playing.
8 0.65 19.8% 42 4.74 1.58
RBE3: I play max bet if Im
on a winning streak.
9 0.76 29.7% 15 1.62 1.15
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Table 2. Continued.
FLAGS-EGM Formative
Constructs and Statements
ComponentComponent
loading
Frequency
Endorsed
Freq.
Rank
T-Score for
Coefcients
Variance
Ination
Factor
RBE4: If I win big I am
likely to put the money
back into a machine and
keep playing.
10 0.78 17.6% 50 4.73 1.34
RBE5: When gambling on a
slot machine I usually play
as fast as I can.
11 0.76 15.0% 57 2.48 1.17
RBE6: I have sometimes
gambled for more than six
hours straight when I was
playing the slots.
12 0.63 34.8% 11 2.21 1.24
Risky Behaviours Later
RBL1: After losing more
money than I wanted on
the slots I usually try to
win it back by playing
again either later that day
or on another day.
13 0.71 14.7% 58 2.57 1.63
RBL2: When gambling on
the slots I usually use my
credit card to get more
money so I can keep
playing.
13 0.51 10.7% 71 3.25 1.37
RBL3: When I gamble with
friends or family I
sometimes stay and
continue to play after they
have stopped or left.
13 0.51 8.0% 84 3.40 1.64
RBL4: I have sometimes
borrowed money from
others so I could go and
gamble on the slots.
14 0.85 4.3% 110 0.46 1.75
RBL5: I have borrowed
money from other people
at the casino in order to
continue gambling.
14 0.73 4.0% 112 2.11 1.84
RBL6: I have left the casino to
get more money so I can
come back and keep on
gambling.
14 0.48 7.8% 88 2.61 1.60
Negative Consequences
NegCons1: My goals in life
have been jeopardized by
my slot play.
15 .58 4.8% 105 2.40 2.49
NegCons2: I often cant
sleep because I am
worrying about my slot
machine gambling.
15 .73 2.9% 116 1.20 2.08
154
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Table 2. Continued.
FLAGS-EGM Formative
Constructs and Statements
ComponentComponent
loading
Frequency
Endorsed
Freq.
Rank
T-Score for
Coefcients
Variance
Ination
Factor
NegCons3: I have had
problems paying off debts
accumulated from playing
the slots.
15 .78 5.1% 102 0.36 2.79
NegCons4: Since I started
playing the slots I dontlike
the type of person I have
become.
15 .63 3.7% 114 0.15 2.29
NegCons5: Sometimes I have
to juggle money and bills to
cover the cost of my slot
machine gambling.
15 .77 5.9% 98 3.31 2.71
NegCons6: I wouldntwant
anyone to know how much
time or money I spend at
the casino.
16 .54 14.4% 61 2.43 1.63
NegCons7: Sometimes I feel
depressed over my slots
play.
16 .54 13.1% 62 3.78 1.74
NegCons8: Others are
disappointed in me because
of my gambling.
17 .68 5.1% 101 2.09 1.62
NegCons9: I have friends or
family who are concerned
about my slots play.
17 .76 5.1% 103 0.89 1.49
NegCons10: I have
sometimes missed events
or neglected family,
friends or work in
order to play the slots.
18 .72 2.7% 118 1.50 2.09
NegCons11: When I leave the
casino, I have sometimes
been short of cash for
parking, food, or a ride
home.
18 .78 1.6% 122 0.00 2.15
NegCons12: I have become
somewhat of a loner because
of my slot gambling.
19 .59 1.6% 125 0.00 1.80
NegCons13: I sometimes have
spent time gambling on the
slots when I was supposed
to be doing something else
important.
19 .59 5.6% 99 1.20 1.59
NegCons14: My gambling
has caused me to have a
falling out with the people I
used to hang out with.
20 .88 1.6% 121 0.81 1.26
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
represented under two separate categories, Risky Practices Earlier and Risky
Practices Later. For Risky Practices Earlier, six statements had relatively high
endorsement rates, equal to or above 15%. Those statements dealt, for the most part,
with playing style, such as playing max bet if the gambler is on a winning streak;
obtaining more money from existing funds, such as using a debit card; and chasing
losses during a play session rather than between sessions. The statements assigned to
Risky Practices Later were endorsed by fewer than 15% of the respondents (see
Table 2), suggesting that these behaviours were less common than those behaviours
associated with Risky Practices Earlier, and thus more likely to be associated with
higher risk. Four of the items addressed different ways to borrow money, including
accumulating debt, one item concerned chasing losses between sessions, and one item
asked about continuing a session of play even after family and friends depart.
Testing Statements for Validity and Reliability
Analyses for validity and reliability were conducted for the formative and reective
constructs using methods appropriate to the respective form of construct.
Construct Reliability and Convergent Validity for Reective Constructs.The
construct reliability for reective constructs is shown in Table 3. An outcome of the
statement selection process was that the Preoccupation Obsession construct had only
two statements. This fact meant that reliability tests could not be performed in this
instance. We left this two-statement construct in the analyses when testing the other
constructs. The four constructs consisting of three or more statements all had
component reliability above the recommended level of 0.70 (Nunnally, 1978),
indicating sufcient internal consistency. The convergent validity was evaluated using
the average variance extracted (AVE) and all ve reective constructs were found to
perform above the guideline of 0.5 as recommended by Fornell and Larcker (1981).
Discriminant Validity Among Reective Constructs. The discriminant validity of
the ve reective constructs was evaluated using two approaches. The rst approach
compared the square root of the Average Variance Extracted (AVE) to the correlations
of the other constructs. Adequate discriminant validity was indicated if the square root
of the constructs AVE was greater than its correlations with the other constructs
(Compeau, Higgins, & Huff, 1999). Table 4 presents the square root of the AVE in the
diagonal and the correlations in the off diagonal. All ve reective constructs passed the
Table 3
Reliability Measures for Reective Constructs
Reective Construct Average Variance Extracted (AVE) Composite Reliability
Preoccupation Desire 0.64 0.88
Preoccupation Obsessed 0.68 NA
Impaired Control Begin 0.75 0.90
Impaired Control Continue 0.70 0.92
Persistence 0.69 0.90
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Table 4
Square Root of AVE and Inter-construct Correlations to Test for Divergent Validity
Erroneous
Cognitions
Beliefs
Preoccupation
Desire
Risky
Practices
Earlier
Impaired
Control
Continue
Erroneous
Cognitions
Motives
Preoccupation
Obsessed
Impaired
Control
Begin
Risky
Practices
Later
Negative
Consequences
Persistence
Risky
Cognitions
Beliefs
na
Preoccupation
Desire
0.40 0.80
Risky
Practices
Earlier
0.43 0.54 na
Impaired
Control
Continue
0.36 0.64 0.76 0.84
Risky
Cognitions
Motives
0.49 0.39 0.58 0.53 na
Preoccupation
Obsessed
0.50 0.44 0.50 0.52 0.55 0.83
Impaired
Control
Begin
0.40 0.41 0.59 0.61 0.48 0.50 0.87
Risky
Practices
Later
0.45 0.48 0.67 0.60 0.59 0.61 0.66 na
Negative
Consequences
0.44 0.40 0.61 0.59 0.62 0.56 0.70 0.74 na
Persistence 0.47 0.44 0.65 0.67 0.63 0.63 0.73 0.75 0.81 0.83
157
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Table 5
Component Loadings on Constructs to Measure Discriminant Validity of Reective Constructs
Constructs
Statements Preoccupation
Desire
Preoccupation
Obsessed
Impaired
Control
Continue
Impaired
Control
Begin
Persistence Risky
Cognitions
Beliefs
Risky
Cognitions
Motives
Risky
Practices
Earlier
Risky
Practices
Later
Negative
Consequences
Pre-des1 0.84 0.34 0.54 0.33 0.37 0.30 0.36 0.44 0.39 0.34
Pre-des2 0.72 0.25 0.43 0.26 0.29 0.28 0.24 0.42 0.29 0.23
Pre-des3 0.81 0.40 0.50 0.39 0.39 0.33 0.35 0.47 0.42 0.37
Pre-des4 0.82 0.39 0.55 0.31 0.35 0.24 0.29 0.39 0.41 0.31
Pre-obs1 0.25 0.74 0.28 0.27 0.37 0.36 0.38 0.29 0.42 0.40
Pre-obs2 0.44 0.90 0.53 0.52 0.63 0.45 0.52 0.50 0.57 0.51
IC-cont1 0.59 0.42 0.89 0.52 0.58 0.31 0.45 0.68 0.54 0.46
IC-cont2 0.57 0.41 0.87 0.49 0.55 0.29 0.44 0.69 0.53 0.49
IC-cont3 0.49 0.46 0.81 0.49 0.60 0.29 0.43 0.62 0.51 0.54
IC-cont4 0.50 0.46 0.83 0.55 0.57 0.33 0.46 0.63 0.52 0.50
IC-cont5 0.53 0.43 0.79 0.53 0.51 0.30 0.45 0.58 0.42 0.47
IC-begin1 0.38 0.46 0.54 0.91 0.65 0.35 0.38 0.55 0.62 0.67
IC-begin2 0.32 0.48 0.55 0.89 0.66 0.36 0.44 0.49 0.59 0.61
IC-begin3 0.36 0.35 0.51 0.80 0.58 0.33 0.42 0.51 0.48 0.54
Persist1 0.39 0.54 0.61 0.66 0.89 0.40 0.56 0.58 0.63 0.74
Persist2 0.39 0.52 0.58 0.57 0.84 0.37 0.49 0.59 0.63 0.64
Persist3 0.32 0.54 0.47 0.61 0.81 0.44 0.56 0.45 0.62 0.66
Persist4 0.37 0.48 0.56 0.58 0.79 0.36 0.47 0.53 0.63 0.65
158
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
test for discriminant validity. For formative constructs, it is inappropriate to report AVE
and not applicable (n.a.) has therefore been entered in the diagonal for these constructs.
The second approach (Gefen & Straub, 2005) compared the correlations between the
individual items and the PLS calculated latent variable scores generated by conducting
conrmatory factor analysis (Table 5). For the construct to have discriminant validity
the item loadings for the reective construct must be greater by 0.10 than the constructs
correlations with the other items. The ve reective constructs passed the test for
discriminant validity. Again, this test does not apply to formative constructs
(Diamantopoulos & Winklhofer, 2001) as the items comprising such a construct are
not expected to be correlated with each other. The formative constructs were retained in
the table so that correlations with the tested reective constructs can be evaluated.
Validity for Formative Constructs. We adopted the four methods recommended
by Henseler, Ringle, and Sinkovics (2009, p. 309) for assessing the validity of formative
constructs. 1. Nomological validity: The relationships between the formative index and
other constructs in the path model, which are sufciently well-known through prior
research, should be strong and signicant. 2. External validity: The formative index
should explain the variance of an alternative reective measure of the focal construct.
3. Signicance of weights: Estimated weights of formative measurement models should
be signicant. 4. Multicollinearity: Manifest variables in a formative block should be
tested for multicollinearity. The variance inationfactor(VIF)canbeusedforsuch
tests. As noted previously, a VIF greater than 10.0 indicates the presence of harmful
collinearity (Diamantopoulos & Siguaw, 2006).
Nomological validity. In the companion article to this paper, we created a Structural
Equation Model using PLS (SEM PLS); we have reserved discussion of PLS and our
hypotheses for that article. To describe the nomological validity of the constructs,
however, we have reported principal results in Table 6. All ve formative constructs
predicted another construct as hypothesized, i.e., each had signicant SEM PLS
coefcients and four formative constructs were sequentially connected to preceding
constructs as hypothesised.
External validity. To determine if the formative construct could explain a signicant
portion of the variance in an alternative but similar reective measure of the construct,
Table 6
Nomological Validity of Formative Constructs: Signicant and Hypothesised Links in
PLS Model
Link Weight T-Score
Risky Cognitions Beliefs -Risky Cognitions Motives 0.493 7.70
Risky Cognitions Motives -Risky Practices Earlier 0.205 3.92
Risky Cognitions Motives -Risky Practices Later 0.156 2.64
Risky Practices Earlier -Risky Practices Later 0.286 3.97
Risky Practices Later -Negative Consequences 0.494 6.56
Negative Consequences -Persistence 0.569 9.21
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
we undertook such a comparison for Negative Consequences and Persistence, the latter
being a reective measure that has negative consequences as part of its makeup. The
weight for this connection in the PLS model was 0.569 (t=9.21), supporting its validity.
This test was not viable for the other formative constructs as equivalent reective
constructs were not available.
Signicance of weights. The t-scores of the construct item weights for each of the
formative constructs are found in Table 2. As recommended by Henseler et al. (2009),
we retained certain non-signicant indicators if they had sufciently low VIF scores and
were judged to be conceptually correct.
Multicollinearity. The statement selection process for formative constructs elimi-
nated statements in each construct until all statements in the construct had VIF scores
below 10.0 (Diamantopoulos & Siguaw, 2006; Henseler et al., 2009). We reported here
the resulting range of VIF scores for each formative construct. The highest VIF score for
Risky Cognitions Beliefs was 1.11 and for Risky Cognitions Motives 1.32. These scores
indicate that multicollinearity was not an issue for either of these constructs. Both Risky
Practices Earlier with a maximum VIF of 1.58 and Risky Practices Later with a
maximum VIF of 1.84 had some degree of multicollinearity, but they were still well
below the accepted threshold of 10 for inclusion in a formative construct. Similarly,
Negative Consequences, with a maximumVIF score of 2.79, had some multicollinearity
but again still met the criterion.
Testing for Common Method Bias
The data were examined for Common Method Bias using the method recommended
by Podsakoff, MacKenzie, Lee, & Podsakoff (2003) for Harmons one-factor test.
PCA was performed on all 53 indicators chosen for inclusion in the constructs, and
the unrotated solution was assessed to determine the number of components with an
eigenvalue greater than one. Strong method bias is present if the analysis produces a
single component; less bias is present when more components are produced. In the
current analysis, eleven components emerged with eigenvalues greater than one with
the rst component accounting for 33.6% of the variance and, collectively, the 11
other components accounting for 66.3% of the original variance. The results
supported the conclusion that the amount of variance because of common method
bias was not sufcient to explain our ndings.
Discussion
The results of testing and analysis produced an instrument with 53 statements
comprising ve formative and four reective constructs designed to identify
individuals experiencing risk of harm from gambling. A fth reective construct,
Preoccupation Obsession, needs further development and testing to be an accurate
measure of risk. The ve formative constructsRisky Cognitions Beliefs, Risky
Cognitions Motives, Risky Practices Earlier, Risky Practices Later and Negative
Consequencespassed the tests for nomological validity and external validity. The
160
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
majority of the items had signicant regression weights and any other statements
retained were theoretically valid. All of the items passed the test for multicollinearity
with VIF scores signicantly below 10.0 and none greater than 2.8. The hypothesized
relationships found in the PLS analysis are described in the companion paper as well
as the potential applications for the FLAGS-EGM instrument.
The formative construct Risky Practices was divided into two constructs as some
behaviours were found to be quite commonly endorsed by more gamblers whereas
others were less frequent and more likely to be exhibited by those at higher risk, i.e.,
located closer to problem gambling status. As a result, the construct was subdivided
to identify those exhibiting behaviour either early or late in the process of becoming a
problem gambler. Using two constructs to dene operationally early and late risk
practices should provide a superior model of risk factors associated with problem
gambling, in particular in terms of positioning, such constructs in the hierarchy of
risk for problem gambling, e.g., earlier versus later risk factors.
Because FLAGS-EGM is composed of separate distinctive constructs we were able
to focus on creating highly accurate statements to capture the essence of each specic
variable. As a result, when designing the Negative Consequence construct, we could
separate cause from effect, an important temporal consideration when designing an
instrument for prevention applications. For example, borrowing money to gamble is
not a negative consequence of gambling as consumers borrow money regularly for
many purchases. Not being able to repay the loans or having to sacrice other
needed resources to pay for these loans, however, would be a negative consequence
that could result from borrowing.
Some investigators consider obsession with gambling as a negative outcome that should
be included as part of the denition of problem gambling rather than as a risk factor. We
understand that obsession is closely related to problem gambling, but question whether
on its own obsession is sufcient to dene problem gambling. Many individuals can be
obsessed with activities but in and of itself, this characteristic may in fact not be linked to
negative consequences for an individual. For example, if an individual is obsessed with
playing golf there may in fact be no negative impacts unless that person begins to miss
work or neglect family responsibilities. We believe that if gamblers have become
obsessed with gambling but have yet to suffer negative consequences, such a situation
may accordingly be an excellent indicator of high-risk due to gambling. If further
investigation indicates that only problem gamblers exhibit obsession with gambling, then
we would reject the construct as a useful risk predictor.
The use of formative constructs should provide users of FLAGS-EGM added insight
into the nature of the harms or risk factors gamblers face. For example, the gambler
who self-administers the instrument can see which specicbehavioursmaybe
contributing to their risk and may be able to curtail or self-manage these behaviours.
Public health, regulatory bodies and treatment providers will all be able to judge the
degree to which problem gamblers suffer nancial, relationship, or psychological harm
and in turn, inuence policy and practices. Even more importantly from a prevention
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
and harm reduction perspective, it will be possible to assess the respective impacts of
specic actions undertaken to resolve or mitigate risk for problem gambling, a means
of measurement and evaluation that is not possible using existing screens.
Factor analysis and the literature supported the hypothesis that two of the reective
constructs, Preoccupation and Impaired Control, should each be subdivided into two
separate constructs, Preoccupation Desire and Preoccupation Obsession, and Impaired
Control Begin and Impaired Control Continue, respectively. Subsequent testing for
discriminant validity conrmed that they are four distinct constructs. Four of the ve
reective measures: Impaired Control Continue, Impaired Control Begin, Persistence,
and Preoccupation Desire were each shown to hold sufcient internal consistency with
component reliabilities above 0.70 and convergent validity with AVE scores above 0.5.
Moreover, they each had discriminant validity, all having a greater square root of the
Average Variance Extracted compared to the correlations with the other constructs.
Moreover, the PLS-calculated latent variable scores for the reective constructs were
greater by 0.10 than the constructs correlations with the other items.
The constructs were designed using a sample of regular Ontario slot gamblers. It
could be argued that this restriction accordingly limits the generalizability of the
results; however, we believe this limitation could apply to any set of constructs based
on a single sample representing a specic population. The original items comprising
FLAGS-EGM were developed and tested with video lottery players in Nova Scotia,
Canada, and pokiemachine gamblers in Victoria, Australia. We did not use
samples of university students, self-selected samples, or volunteers recruited through
advertisements. Individuals diagnosed with comorbid disorders, treatment popula-
tions or prison inmates were not surveyed. Consequently, we are condent that these
results are reasonably reective of a general population of gamblers.
The item scales are dichotomous (yes/no) to facilitate understanding and ease of
answering the items, a desirable characteristic for an instrument that is designed to be
self-administered and which is composed of a large number of items. Examination of
other screens suggests that it is difcult to create multi-item scales without adding
considerable method bias as a result of the mist between the statement and the scale
anchors. Moreover, we built frequency distinctions into the statements to account for
differences in the occurrence of certain behaviours, beliefs or outcomes. Certain
statistics, particularly factor analysis, may be somewhat inaccurate when applied to
dichotomous data. For example, on average, the loading may vary ±0.08
compared to statistics derived using other techniques (Maguire 2001). Although we
used PCA analysis to nominate statements for inclusion in the constructs, we relied
on other means to actually test the reliability and validity of the constructs. As a
result, we believe the use of factor analysis in this context does not reduce the validity
of the constructs.
The current process did not yield a usable construct to measure Preoccupation
Obsession. In the next phase of the research, additional statements will be included
and tested to capture better this latent variable.
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
FLAGS-EGM holds strong potential. This approach to risk measurement moves
beyond simple identication of those persons who have problems to active risk
tracking for prevention and evaluation purposes. We will be conducting further
research to develop this instrument by applying it to other larger, random samples of
EGM players in different markets, testing new candidate statements for the
preoccupation construct and retesting the constructs for reliability and validity.
The next steps, as detailed in the companion paper in this issue, required the use of SEM
PLS to determine the causal paths of the relationships among the constructs. The
placement and grouping of the constructs as indicators of risk will then determine the
appropriate number of risk levels to create. FLAGS-EGM is composed of separate
construct measures of risk, and thus when compared with current instruments, it should
more accurate assess an individuals risk due to gambling as well as identify those.
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Appendix
FLAGS-EGM
Risky Cognitions Beliefs
You can sometimes tell when the machine is about to pay out big because the
symbols start getting closer to lining up on the pay line (e.g., almost winning).
I feel the machines are xed sometimes so that you cant win on them.
It is important for me to use a system or a strategy when I play the machines.
I believe that in the long run I can win playing slots at the casino.
If a slot machine hasnt had a big pay out in a long time, it is more likely to do so
soon.
Risky Cognitions Motives
I sometimes play the slots in hopes of paying off my debts/bills.
I sometimes play the slots when Im feeling down or depressed.
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Gambling on the slots is a way I can try to get some money when I need it.
I can escape by playing the slots whenever I am worried or under stress.
Preoccupation Desire
If I could play the machines all the time I would.
I wish I could gamble on the slots more often.
I would like to play the slots almost every day.
I like to play the slot machines every chance I get.
Preoccupation Obsession
I sometimes dream about playing the slot machines.
I spend more time than I used to thinking about playing the slots.
Risky Practices Earlier
I sometimes exceed the amount of money I intended to spend in order to win back
money I have lost.
When gambling on the slots I usually use my bank or debit card to get more money
so I can keep playing.
I play max bet if Im on a winning streak.
If I win big I am likely to put the money back into a machine and keep playing.
When gambling on a slot machine I usually play as fast as I can.
I have sometimes gambled for more than six hours straight when I was playing the slots.
Risky Practices Later
After losing more money than I wanted on the slots I usually try to win it back by
playing again either later that day or on another day.
When gambling on the slots I usually use my credit card to get more money so I
can keep playing.
When I gamble with friends or family I sometimes stay and continue to play after
they have stopped or left.
I have sometimes borrowed money so I could go and gamble on the slots.
I have borrowed money from other people at the casino in order to continue gambling.
I have left the casino to get more money so I can come back and keep on gambling.
Impaired Control Continue
I often spend more money gambling than I intended.
Even when I intend to spend a few dollars gambling, I often end up spending much
more.
I sometimes gamble with money that I cant really afford to lose.
Once I have started gambling on the slots I nd it very hard to stop.
I often spend more time gambling than I intend to.
Impaired Control Begin
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
I have tried to cut back on my slots play with little success.
I have tried unsuccessfully to stop or reduce my gambling on the slots.
There have been times I have gambled despite my desire not to.
Negative Consequences
My goals in life have been jeopardized by my slot play.
I often cant sleep because I am worrying about my slot machine gambling.
have had problems paying off debts accumulated from playing the slots.
Since I started playing the slots I dont like the type of person I have become.
Sometimes I have to juggle money and bills to cover the cost of my slot machine
gambling.
I wouldnt want anyone to know how much time or money I spend at the casino.
Sometimes I feel depressed over my slots play.
Others are disappointed in me because of my gambling.
I have friends or family who are concerned about my slots play.
I have sometimes missed events or neglected family, friends or work in order to
play the slots.
When I leave the casino, I have sometimes been short of cash for parking, food, or
a ride home.
I have become somewhat of a loner because of my slot gambling.
I sometimes have spent time gambling on the slots when I was supposed to be
doing something else important.
My gambling has caused me to have a falling out with the people I used to hang
out with.
Persistence
I continue to play the machines despite experiencing problems or other negative
consequences.
I continue to gamble despite the bad things that happen to me.
I gamble even though I know it is likely to lead to problems for me.
Even if money is tight, I continue to play the slots to get big wins.
*******
Manuscript history: Submitted October 16, 2012; revised manuscript accepted
March 16, 2014. This article was peer-reviewed. All URLs were available at the time
of submission.
For correspondence: Tony Schellinck, PhD, Focal Research Consultants Limited,
7071 Bayers Rd., Suite 326, Halifax, NS, Canada B3L 2C2. E-mail:
tschellinck@focalresearch.com, Website address: http://www.focalresearch.com
Competing interests: None declared.
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MEASUREMENT OF RISK FOR PROBLEM GAMBLING
Ethics approval: The Ontario Institutional Review Board (ON IRB). Final protocol
approval was obtained for ‘‘ Preliminary Development of a Self Administered
Gambling Risk Assessment Instrument for Slots’’ on June 23, 2008.
Funding: Funding was received from the Victoria Gambling Research Panel under
the leadership of Dr. Linda Hancock, to develop the Victoria Self-Administered
Problem Gambling Scale (SAPGS) in 2003. Further funding was received from the
Victoria Department of Justice, Melbourne Victoria in 2006 to test the validity and
value of the Victoria SAPGS. In 2008, the Ontario Problem Gambling Research
Centre provided funding for further development of what became known as the
FLAGS-EGM and its then Director, Rob Simpson, provided guidance in expanding
measures used in the instrument.
Contributors: TSchellinck planned the document. TSchellinck and HS drafted and
wrote the manuscript with editorial contributions from TSchrans and MB. HS and
TSchrans conducted the gambling-related literature review. TSchrans and TSschel-
linck conceptualized the research design and conducted the focus group and survey
studies. TSchellinck and MB assessed the current analytical literature and designed
the analysis approach. TSchellinck conducted the analysis and nalized the design of
the constructs
Dr. Tony Schellinck is an Adjunct Professor in the Faculty of Graduate Studies
and the Rowe School of Business at Dalhousie University, Canada, as well as
CEO of Focal Research Consultants Limited. From 1996 to 2013 he was the F. C.
Manning Chair in Economics and Business at Dalhousie University. Since 1989
he has conducted research into gambling behaviour for industry, government,
public health and regulatory agencies. This work included a ten-year large-scale
monthly tracking study of gambling behaviour, over 300 focus group sessions
with gamblers, the 1998 Nova Scotia Video Lottery Study, two large scale studies
into the value of responsible gambling featuresonVLTmachines,andtheNova
Scotia Adolescent Gambling Exploratory Research: Identication of Risk and
Gambling Harms Among Youth. Dr. Schellinck worked on creating the rst
algorithms deployed in casinos that identied using player loyalty data high-risk
gamblers.
Ms. Tracy Schrans is Principal and President of Focal Research Consultants an
independent research rm in Halifax, NS. Over the last twenty years Tracy has
conducted numerous government, public health, and industry-sponsored research
projects on a wide range of issues, with a particular emphasis on gambling- and
alcohol-related issues. She consults internationally in responsible gambling and
corporate social responsibility, social policy, player tracking and loyalty data
analysis. Schrans is one of the developers of new instruments for measuring pre-
harm risk for gambling among adults (FLAGS-EGM and FLAGS General) and
adolescents (FYGRS) for prevention applications. She continues to work at the
forefront of gambling behavior analytics, assisting gambling stakeholders in
172
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
using system data, measurement, and technology to help identify, manage and
prevent gambling risk and harm among their customers.
Dr. Heather Schellinck, PhD, is an Adjunct Professor in the Faculty of Graduate
Studies and Department of Psychology and Neuroscience at Dalhousie University.
Her research is primarily focused on learning and memory in animal models of
neurodegenerative disease.
Dr. Michael Bliemel is an associate professor of Management Information Systems
at Dalhousie University in Halifax, NS. He completed his PhD at McMaster
University in Management Science/Systems, specializing in the quantitative
modeling of consumer behaviour with health information systems. His current
research interests include the strategic management of information systems and
innovation in organizations, and business intelligence applications.
173
MEASUREMENT OF RISK FOR PROBLEM GAMBLING
... Delfabbro and King [32] argue that certain items attributed as harms are labelled incorrectly; they suggest that chasing losses, gambling to obtain more excitement, or betting above affordable means, are behaviours that lead to harm and not the harms themselves. Schellinck et al. [33]also argue that borrowing money is not a harm, but is in fact a predictor for the harms, debt and relationship conflict. Critical appraisal of the defined harms used in each study is therefore necessary. ...
... Twenty-two studies include data on age, and several of these found that being younger was associated with a higher risk of experiencing gambling harms [42][43][44][45][46][47]. One study found that younger age groups (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) were at risk of dependence and social harms [42], and Ferrara et al. [45] found younger age groups showed higher rates of "problematic gambling" and a higher comorbidity with other addictions. In Breen [48] it was found that youths who were exposed to card gambling were more likely to gamble later in life to increase their income, and those who missed school had reduced lifelong aspirations and reduced opportunities. ...
Article
Full-text available
Background Here we present a systematic review of the existing research into gambling harms, in order to determine whether there are differences in the presentation of these across demographic groups such as age, gender, culture, and socioeconomic status, or gambling behaviour categories such as risk severity and participation frequency. Primary and secondary outcome measures Inclusion criteria were: 1) focus on gambling harms; 2) focus on harms to the gambler rather than affected others; 3) discussion of specific listed harms and not just harms in general terms. Exclusion criteria were: 1) research of non-human subjects; 2) not written in English; 3) not an empirical study; 4) not available as a full article. Methods We conducted a systematic search using the Web of Science and Scopus databases in August 2020. Assessment of quality took place using Standard Quality Assessment Criteria. Results A total of 59 studies published between 1994 and 2020 met the inclusion criteria. These were categorised into thematic groups for comparison and discussion. There were replicated differences found in groups defined by age, socioeconomic status, education level, ethnicity and culture, risk severity, and gambling behaviours. Conclusion Harms appear to be dependent on specific social, demographic and environmental conditions that suggests there is a health inequality in gambling related harms. Further investigation is required to develop standardised measurement tools and to understand confounding variables and co-morbidities. With a robust understanding of harms distribution in the population, Primary Care Workers will be better equipped to identify those who are at risk, or who are showing signs of Gambling Disorder, and to target prevention and intervention programmes appropriately.
... Delfabbro and King 32 argue that certain items attributed as harms are labelled incorrectly; they suggest that chasing losses, gambling to obtain more excitement, or betting above affordable means, are behaviours that lead to harm and not the harms themselves. Schellinck et al. 33 also argue that borrowing money is not a harm, but is in fact a predictor for the harms, debt and relationship con ict. Critical appraisal of the de ned harms used in each study is therefore necessary. ...
... Twenty-two studies include data on age, and several of these found that being younger was associated with a higher risk of experiencing gambling harms [42][43][44][45][46][47] . One study found that younger age groups (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) were at risk of dependence and social harms 42 , and Ferrara et al. 45 found younger age groups showed higher rates of "problematic gambling" and a higher comorbidity with other addictions. In Breen 48 it was found that youths who were exposed to card gambling were more likely to gamble later in life to increase their income, and those who missed school had reduced lifelong aspirations and reduced opportunities. ...
Preprint
Full-text available
Background: Here we present a systematic review of the existing research into gambling harms, in order to determine whether there are differences in the presentation of these across demographic groups such as age, gender, culture, and socioeconomic status, or gambling behaviour categories such as risk severity and participation frequency. Primary and Secondary Outcome Measures: Inclusion criteria were: 1) focus on gambling harms; 2) focus on harms to the gambler rather than affected others; 3) discussion of specific listed harms and not just harms in general terms. Exclusion criteria were: 1) research of non-human subjects; 2) not written in English; 3) not an empirical study; 3) not available as a full article. Methods: We conducted a systematic search using the Web of Science and Scopus databases in August 2020. Assessment of quality took place using Standard Quality Assessment Criteria. Results: A total of 59 studies published between 1994 and 2020 met the inclusion criteria. These were categorised into thematic groups for comparison and discussion. There were replicated differences found in groups defined by age, socioeconomic status, education level, ethnicity and culture, risk severity, and gambling behaviours. Conclusion: Harms appear to be dependent on specific social, demographic and environmental conditions that suggests there is a health inequality in gambling related harms. Further investigation is required to develop standardised measurement tools and to understand confounding variables and co-morbidities. With a robust understanding of harms distribution in the population, Primary Care Workers will be better equipped to identify those who are at risk, or who are showing signs of Gambling Disorder, and to target prevention and intervention programmes appropriately.
... Delfabbro and King 32 argue that certain items attributed as harms are labelled incorrectly; they suggest that chasing losses, gambling to obtain more excitement, or betting above affordable means, are behaviours that lead to harm and not the harms themselves. Schellinck et al. 33 also argue that borrowing money is not a harm, but is in fact a predictor for the harms, debt and relationship con ict. Critical appraisal of the de ned harms used in each study is therefore necessary. ...
... Twenty-two studies include data on age, and several of these found that being younger was associated with a higher risk of experiencing gambling harms [42][43][44][45][46][47] . One study found that younger age groups (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) were at risk of dependence and social harms 42 , and Ferrara et al. 45 found younger age groups showed higher rates of "problematic gambling" and a higher comorbidity with other addictions. In Breen 48 it was found that youths who were exposed to card gambling were more likely to gamble later in life to increase their income, and those who missed school had reduced lifelong aspirations and reduced opportunities. ...
Preprint
Full-text available
Background: Here we present a systematic review of the existing research into gambling harms, in order to determine whether there are differences in the presentation of these across demographic groups such as age, gender, culture, and socioeconomic status, or gambling behaviour categories such as risk severity and participation frequency. Primary and Secondary Outcome Measures: Inclusion criteria were: 1) focus on gambling harms; 2) focus on harms to the gambler rather than affected others; 3) discussion of specific listed harms and not just harms in general terms. Exclusion criteria were: 1) research of non-human subjects; 2) not written in English; 3) not an empirical study; 3) not available as a full article. Methods: We conducted a systematic search using the Web of Science and Scopus databases in August 2020. Assessment of quality took place using Standard Quality Assessment Criteria. Results: A total of 59 studies published between 1994 and 2020 met the inclusion criteria. These were categorised into thematic groups for comparison and discussion. There were replicated differences found in groups defined by age, socioeconomic status, education level, ethnicity and culture, risk severity, and gambling behaviours. Conclusion: Harms appear to be dependent on specific social, demographic and environmental conditions that suggests there is a health inequality in gambling related harms. Further investigation is required to develop standardised measurement tools and to understand confounding variables and co-morbidities. With a robust understanding of harms distribution in the population, Primary Care Workers will be better equipped to identify those who are at risk, or who are showing signs of Gambling Disorder, and to target prevention and intervention programmes appropriately.
... Delfabbro and King 29 argue that certain items attributed as harms are labelled incorrectly; they suggest that chasing losses, gambling to obtain more excitement, or betting above affordable means, are behaviours that lead to harm and not the harms themselves. Schellinck et al. 30 also argue that borrowing money is not a harm, but is in fact a predictor for the harms, debt and relationship con ict. Critical appraisal of the de ned harms used in each study is therefore necessary. ...
... Twenty-two studies include data on age, and several of these found that being younger was associated with a higher risk of experiencing gambling harms [39][40][41][42][43][44] . In particular one study found that younger age groups (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) were at risk of dependence and social harms 39 , and Ferrara et al. 42 found that as well as higher rates of what they label "problematic gambling", younger age groups showed a higher comorbidity with other addictions. ...
Preprint
Full-text available
Background: Here we present a systematic review of the existing research into gambling harms, in order to determine whether there are differences in the presentation of these across demographic groups such as age, gender, culture, and socioeconomic status, or gambling behaviour categories such as risk severity and play frequency. Primary and Secondary Outcome Measures: Inclusion criteria were: 1) focus on gambling harms; 2) focus on harms to the gambler rather than affected others; 3) discussion of specific listed harms and not just harms in general terms. Exclusion criteria were: 1) research of non-human subjects; 2) not written in English; 3) not an empirical study; 3) not available as a full article. Methods: We conducted a systematic search using the Web of Science and Scopus databases in August 2020. Assessment of quality took place using Standard Quality Assessment Criteria. Results: A total of 59 studies published between 1994 and 2020 met the inclusion criteria. These were categorised into thematic groups for comparison and discussion. There were replicated differences found in groups defined by age, socioeconomic status, education level, ethnicity and culture, risk severity, and gambling behaviours. Conclusion: Harms appear to be dependent on specific social, demographic and environmental conditions that suggests there is a health inequality in gambling related harms. Further investigation is required to develop standardised measurement tools and to understand confounding variables and co-morbidities. With a robust understanding of harms distribution in the population, Primary Care Workers will be better equipped to identify those who are at risk, or who are showing signs of Gambling Disorder, and to target prevention and intervention programmes appropriately.
... Delfabbro and King 29 argue that certain items attributed as harms are labelled incorrectly; they suggest that chasing losses, gambling to obtain more excitement, or betting above affordable means, are behaviours that lead to harm and not the harms themselves. Schellinck et al. 30 also argue that borrowing money is not a harm, but is in fact a predictor for the harms, debt and relationship con ict. Critical appraisal of the de ned harms used in each study is therefore necessary. ...
... Twenty-two studies include data on age, and several of these found that being younger was associated with a higher risk of experiencing gambling harms [39][40][41][42][43][44] . In particular one study found that younger age groups (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) were at risk of dependence and social harms 39 , and Ferrara et al. 42 found that as well as higher rates of what they label "problematic gambling", younger age groups showed a higher comorbidity with other addictions. In Breen 45 it was found that youths who were exposed to card gambling at a young age were more likely to gamble later in life to increase their income, and that youths who missed school had reduced lifelong aspirations and reduced opportunities. ...
Preprint
Full-text available
Background: Here we present a systematic review of the existing research into gambling harms, in order to determine whether there are differences in the presentation of these across demographic groups such as age, gender, culture, and socioeconomic status, or gambling behaviour categories such as risk severity and play frequency. Primary and Secondary Outcome Measures: Inclusion criteria were: 1) focus on gambling harms; 2) focus on harms to the gambler rather than affected others; 3) discussion of specific listed harms and not just harms in general terms. Exclusion criteria were: 1) research of non-human subjects; 2) not written in English; 3) not an empirical study; 3) not available as a full article. Methods: We conducted a systematic search using the Web of Science and Scopus databases in August 2020. Assessment of quality took place using Standard Quality Assessment Criteria. Results: A total of 59 studies published between 1994 and 2020 met the inclusion criteria. These were categorised into thematic groups for comparison and discussion. There were replicated differences found in groups defined by age, socioeconomic status, education level, ethnicity and culture, risk severity, and gambling behaviours. Conclusion: Harms appear to be dependent on specific social, demographic and environmental conditions that suggests there is a health inequality in gambling related harms. Further investigation is required to develop standardised measurement tools and to understand confounding variables and co-morbidities. With a robust understanding of harms distribution in the population, Primary Care Workers will be better equipped to identify those who are at risk, or who are showing signs of Gambling Disorder, and to target prevention and intervention programmes appropriately.
... Despite the heavy reliance on surveys as the main method for data collection on gambling harm, the question of response format has not yet been explored within gambling research. Even though the SGHS and the FocaL Adult Gambling Screen (FLAGS; Schellinck, Schrans, Schellinck, & Bliemel, 2015) are both scored using a binary response format, neither has been subject to a similar analysis in response to the concerns raised by Delfabbro and King (2017). The aim of the present study is, therefore, to examine the influence of different response formats have on the psychometric properties of the SGHS. ...
Article
Full-text available
Gambling-related harm has become a key metric for measuring the adverse consequences of gambling on a population level. Yet, despite this renewed understanding in contemporary research, little exploration has been conducted to evaluate which instrument is best suited to capture the harmful consequences of gambling. This study was designed with the aim of determining whether Likert scales were better suited to capture gambling harm than binary scales. We hypothesized that the Short Gambling Harm Screen (SGHS), initially scored using a binary scale, would perform similarly to the alternate form that was Likertized for the purpose of this study. A corresponding comparison in the reverse direction was executed for the Problem Gambling Severity Index. The SGHS's performance was assessed via a repeated-measures design in combination with three other measures of validity administered at the conclusion of the survey. In the end, we found that changing the scoring format (i.e., from binary to Likert) had negligible impact on the SGHS's psychometric performance. We conclude that the original scoring method of the SGHS is not only appropriate but also no less suitable than Likert scales in measuring gambling harm.
... Summary of validation procedure, DSM-5 proposal and four IGD scales implications in terms of scale construction, validation, and (composite) scale score interpretation. Crucially, it would be incorrect to presuppose, in the case of a formative construct, that a higher number of items endorsed reflects a greater severity or impact.45 Using a structured approach, 36,44 the current study evaluates to what extent it might be appropriate to employ formative constructs in the definition and validation of IGD. ...
Article
Background: Some people have serious problems controlling their internet and video game use. The DSM-5 now includes a proposal for 'Internet Gaming Disorder' as a condition in need of further study. Various studies aim to validate the proposed diagnostic criteria for Internet Gaming Disorder and multiple new scales have been introduced that cover the suggested criteria. Approach: Using a structured approach, we demonstrate that Internet Gaming Disorder might be better interpreted as a formative construct, as opposed to the current practice of conceptualizing it as a reflective construct. Incorrectly approaching a formative construct as a reflective one causes serious problems in scale development including (a) incorrect reliance on item-to-total scale correlation to exclude items and incorrectly relying on indices of inter-item reliability that do not fit the measurement model (e.g., Cronbach's α) (b) incorrect interpretation of composite or mean scores that assume all items are equal in contributing value to a sum score, and (c) biased estimation of model parameters in statistical models. Implications: We show that these issues are impacting current validation efforts through two recent examples. A reinterpretation of Internet Gaming Disorder as a formative construct has broad consequences for current validation efforts and provides opportunities to reanalyze existing data. We discuss three broad implications for current research: (1) Composite latent constructs should be defined and used in models, (2) Item exclusion and selection should not rely on item-to-total scale correlations, and (3) Existing definitions of Internet Gaming Disorder should be enriched further.
Article
Full-text available
We examine the manner in which the population prevalence of disordered gambling has usually been estimated, on the basis of surveys that suffer from a potential sample selection bias. General population surveys screen respondents using seemingly innocuous “trigger,” “gateway” or “diagnostic stem” questions, applied before they ask the actual questions about gambling behavior and attitudes. Modeling the latent sample selection behavior generated by these trigger questions using up-to-date econometrics for sample selection bias correction leads to dramatically different inferences about population prevalence and comorbidities with other psychiatric disorders. The population prevalence of problem or pathological gambling in the United States is inferred to be 7.7%, rather than 1.3% when this behavioral response is ignored. Comorbidities are inferred to be much smaller than the received wisdom, particularly when considering the marginal association with other mental health problems rather than the total association. The issues identified here apply, in principle, to every psychiatric disorder covered by standard mental health surveys, and not just gambling disorder. We discuss ways in which these behavioral biases can be mitigated in future surveys.
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We study Danish adult gambling behavior with an emphasis on discovering patterns relevant to public health forecasting and economic welfare assessment of policy. Methodological innovations include measurement of formative in addition to reflective constructs, estimation of prospective risk for developing gambling disorder rather than risk of being falsely negatively diagnosed, analysis with attention to sample weights and correction for sample selection bias, estimation of the impact of trigger questions on prevalence estimates and sample characteristics, and distinguishing between total and marginal effects of risk-indicating factors. The most significant novelty in our design is that nobody was excluded on the basis of their response to a 'trigger' or 'gateway' question about previous gambling history. Our sample consists of 8405 adult Danes. We administered the Focal Adult Gambling Screen to all subjects and estimate prospective risk for disordered gambling. We find that 87.6% of the population is indicated for no detectable risk, 5.4% is indicated for early risk, 1.7% is indicated for intermediate risk, 2.6% is indicated for advanced risk, and 2.6% is indicated for disordered gambling. Correcting for sample weights and controlling for sample selection has a significant effect on prevalence rates. Although these estimates of the 'at risk' fraction of the population are significantly higher than conventionally reported, we infer a significant decrease in overall prevalence rates of detectable risk with these corrections, since gambling behavior is positively correlated with the decision to participate in gambling surveys. We also find that imposing a threshold gambling history leads to underestimation of the prevalence of gambling problems.
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Previous research, based on a survey of 374 electronic machine gamblers living in Ontario, Canada, led to the selection of statements and the creation of ten constructs for the development of a new instrument, the FocaL Adult Gambling Screen for Electronic Gambling Machines (FLAGS-EGM). In this study, we used the Partial Least Squares Path Analysis form of Structural Equation Modelling to produce a hierarchical set of the ten constructs with proven predictive power for problem gambling. Receiver Operating Characteristic analysis identified cut off values for all of the constructs that predicted the target values with the desired degree of accuracy. Active gamblers were placed in five categories: No Detectable Risk, Early Risk, Intermediate Risk, Advanced Risk and Problem Gamblers. As described here, the FLAGS-EGM instrument has the potential to be applied in many situations in which identification of at-risk EGM gamblers is needed. © 2015 Centre for Addiction and Mental Health. All rights reserved.
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The Gamblers' Beliefs Questionnaire (GBQ) is a self-report measure of gamblers' cognitive distortions. GBQ test items were constructed on the basis of theory, empirical evidence, and expert review. Four hundred three adults completed the initial set of items, and 21 items were selected to make up the final GBQ. The factor structure of the GBQ consisted of 2 closely related factors: Luck/Perseverance and Illusion of Control. The full scale showed good internal consistency (alpha = .92) and adequate test-retest reliability (r = .77). Problem and pathological gamblers scored higher than nonproblem gamblers on the GBQ and its factors. GBQ scores were moderately correlated with the duration of gambling sessions among problem and pathological gamblers, and there was no relationship between GBQ scores and social desirability.
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The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
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Presenting the latest conclusions about the psychological processes leading to impaired self-control, this book challenges the current harm-prevention policy of “responsible gambling.” The authors present the most recent and evolving research into gambling, demonstrating the psychological variables that govern the erosion or maintenance of self-control over gambling behavior. These studies provide an empirical basis for a model of impaired control of gambling. Impaired control, in its broadest sense, is considered to be the defining psychological construct of all the addictive behaviors and occupies a central position in conceptualizing the addictive aspects of gambling.
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Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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