Further Exploration of the Psychometric Properties of GamTest: A Rasch Analysis

David Forsström, Alexander Rozental, Anders Kottorp, Philip Lindner, Markus Jansson-Fröjmark and Hugo Hesser
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David Forsström: Department of Psychology, Stockholm University, Frescati Hagväg 8, 105 90 Stockholm, Sweden
Alexander Rozental: Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Norra Stationsgatan 69, 113 64 Stockholm, Sweden
Anders Kottorp: Faculty of Health and Society, Malmö University, Jan Waldenströms Gata 25, 214 28 Malmö, Sweden
Philip Lindner: Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Norra Stationsgatan 69, 113 64 Stockholm, Sweden
Markus Jansson-Fröjmark: Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Norra Stationsgatan 69, 113 64 Stockholm, Sweden
Hugo Hesser: School of Law, Psychology and Social Work, Örebro University, 701 82 Örebro, Sweden

IJERPH, 2021, vol. 18, issue 9, 1-12

Abstract: GamTest is a self-rating scale of negative consequences of gambling, included in the popular responsible gambling tool Playscan as part of an overall risk assessment and feedback feature. Two previous psychometric evaluations of this instrument yielded contradictory results: in an online high-gambling population, a five-factor model was supported and the instrument had overall good psychometric properties, but in a low-gambling population, the same factor structure was not supported. Because GamTest is used with both low- and high-gambling populations, more psychometric research is needed to fully understand how the instrument works. The current study examined, for the first time, psychometric performance among a sample of low-gambling respondents using a Rasch analysis. Results indicated that the instrument could be improved by decreasing the scale-steps and removing several problematic items demonstrating misfit. Furthermore, the findings indicated that some items functioned differently depending on gender, and that a shortened, improved nine-item version could not differentiate between different levels of risk. Our findings suggest that the instrument would arguably benefit from being adapted for use in a low-gambling population.

Keywords: gambling; negative consequences; GamTest; Rasch analysis; Playscan (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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