Validation of the problem gambling severity index using confirmatory factor analysis and rasch modelling

Miller, Natalie V.; Currie, Shawn R.; Hodgins, David C.; Casey, David M.

doi:10.1002/mpr.1392

Cited by 62 publications

(66 citation statements)

References 53 publications

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“…between ‘sometimes’ and ‘most of the time’. Between the second and third classes, high severity items identified by IRT analyses (items 4,6,8) (Miller et al 2013), and three of the four items measuring loss of control (items 2,3,4,8) (Kincaid et al 2013) showed considerable separation between classes (>0.8 for class 3, <0.2 for class 2). However, as these items overlap, it is difficult to adjudge between loss of control or severity explanations between latent classes.…”

Section: Resultsmentioning

confidence: 99%

“…The latent classes were similar across studies, comprising one group displaying no/minimal symptoms, a group showing moderate probability of symptom endorsement, and a group that exceeded the DSM cutoff for Pathological Gambling. Other analyses of prominent gambling assessments support a continuum of severity (Miller et al 2013; Strong and Kahler 2007), but these use analytic methods that already assume a latent dimension is being measured. To examine this further, this report describes the findings of seventeen LCAs across five different surveys of the British population over a fifteen year period, using four assessments measuring problem and pathological gambling constructs.…”

Section: Introductionmentioning

confidence: 99%

“…The PGSI is the predominant contemporary population assessment of problem gambling (Williams et al 2012). It is assumed to measure a continuum of harm (Ferris and Wynne 2001; Miller et al 2013), but has been demonstrated to measure latent categories (James et al 2014; Kincaid et al 2013). The PGSI is partly derived from the DSM-IV Pathological Gambling criteria and the South Oaks Gambling Screen (SOGS) (Stinchfield 2002; Svetieva and Walker 2008), a pathological gambling assessment derived from the DSM-III criteria (Lesieur and Blume 1987), and administered in the BGPS 1999.…”

Section: Introductionmentioning

confidence: 99%

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Loss of Control as a Discriminating Factor Between Different Latent Classes of Disordered Gambling Severity

2016

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Analyses of disordered gambling assessment data have indicated that commonly used screens appear to measure latent categories. This stands in contrast to the oft-held assumption that problem gambling is at the extreme of a continuum. To explore this further, we report a series of latent class analyses of a number of prevalent problem gambling assessments (PGSI, SOGS, DSM-IV Pathological Gambling based assessments) in nationally representative British surveys between 1999 and 2012, analysing data from nearly fifty thousand individuals. The analyses converged on a three class model in which the classes differed by problem gambling severity. This identified an initial class of gamblers showing minimal problems, a additional class predominantly endorsing indicators of preoccupation and loss chasing, and a third endorsing a range of disordered gambling criteria. However, there was considerable evidence to suggest that classes of intermediate and high severity disordered gamblers differed systematically in their responses to items related to loss of control, and not simply on the most ‘difficult’ items. It appeared that these differences were similar between assessments. An important exception to this was one set of DSM-IV criteria based analyses using a specific cutoff, which was also used in an analysis that identified an increase in UK problem gambling prevalence between 2007 and 2010. The results suggest that disordered gambling has a mixed latent structure, and that present assessments of problem gambling appear to converge on a broadly similar construct.Electronic supplementary materialThe online version of this article (doi:10.1007/s10899-016-9592-z) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Loss of Control as a Discriminating Factor Between Different Latent Classes of Disordered Gambling Severity

2016

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“…The only additional data collected online involved the PGSI (Ferris and Wynne, ; Miller et al, ). Since its publication in 2001, the PGSI has become internationally recognized as a robust measure of gambling behavior and has been used in Canada, Australia, Great Britain, Iceland and Norway.…”

Section: Methodsmentioning

confidence: 99%

Tracking online poker problem gamblers with player account-based gambling data only

Luquiens

Tanguy

Benyamina

et al. 2016

Int J Methods Psychiatr Res

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The aim was to develop and validate an instrument to track online problem poker gamblers with player account-based gambling data (PABGD). We emailed an invitation to all active poker gamblers on the online gambling service provider Winamax. The 14,261 participants completed the Problem Gambling Severity Index (PGSI). PGSI served as a gold standard to track problem gamblers (i.e., PGSI ≥ 5). We used a stepwise logistic regression to build a predictive model of problem gambling with PABGD, and validated it. Of the sample 18% was composed of online poker problem gamblers. The risk factors of problem gambling included in the predictive model were being male, compulsive, younger than 28 years, making a total deposit > 0 euros, having a mean loss per gambling session > 1.7 euros, losing a total of > 45 euros in the last 30 days, having a total stake > 298 euros, having > 60 gambling sessions in the last 30 days, and multi-tabling. The tracking instrument had a sensitivity of 80%, and a specificity of 50%. The quality of the instrument was good. This study illustrates the feasibility of a method to develop and validate instruments to track online problem gamblers with PABGD only. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Finally, alternative measures of gambling risk could be employed. The PGSI has been critiqued as being less accurate than some other measures for older populations (Williams and Volberg 2014), and there is ongoing discussion about discriminate validity of the low and moderate risk categories (e.g., see Miller et al 2013). …”

Section: Discussionmentioning

confidence: 99%

Low-Risk, Moderate-Risk, and Recreational Gambling Among Older Adults: Self-Complexity as a Buffer for Quality of Life

Hilbrecht

Mock

2018

Applied Research Quality Life

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Gambling is a popular activity among older adults that has potential for growth as the population ages. Benefits have been identified for recreational gamblers (those experiencing no gambling-related problems), but there are many social, financial, and health harms affecting quality of life related to gambling across the spectrum of risk levels. Because of their greater numbers, low and moderate risk (LMR) older adult gamblers are responsible for higher levels of population harm. Therefore, this study examines outcomes experienced by older adults who participate in gambling at recreational and LMR levels to better understand protective factors that contribute to quality of life. Guided by activity theory and a modified self-complexity model, we explore how leisure complexity and employment buffer the relationship between gambler type and factors associated with well-being, namely social support (both perceived and received) and stress. A sub-sample of 3232 adults, age 55 years and older, who gambled in the past year was drawn from the 2013-2014 Canadian Community Health Survey. Data were analyzed using linear regression with moderation, focusing on the interactions of gambling risk level with leisure complexity and employment. Recreational gamblers reported greater role complexity and more diverse leisure lifestyles, both of which were associated with greater social support and reduced stress. Life stress was higher among older adults who worked for pay. Even so, having a job appeared to temper stress levels among LMR gamblers, suggesting the Applied Research Quality Life

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Validation of the problem gambling severity index using confirmatory factor analysis and rasch modelling

Cited by 62 publications

References 53 publications

Loss of Control as a Discriminating Factor Between Different Latent Classes of Disordered Gambling Severity

Loss of Control as a Discriminating Factor Between Different Latent Classes of Disordered Gambling Severity

Tracking online poker problem gamblers with player account-based gambling data only

Low-Risk, Moderate-Risk, and Recreational Gambling Among Older Adults: Self-Complexity as a Buffer for Quality of Life

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