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Latent profile analysis of gambling

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dc.contributor.author Citak, Senel
dc.date.accessioned 2024-03-15T06:47:00Z
dc.date.available 2024-03-15T06:47:00Z
dc.date.issued 2023
dc.identifier.citation Çitak, S. (2023). Latent profile analysis of gambling. Front. Psychol., 14. https://doi.org/10.3389/fpsyg.2023.1293933 en_US
dc.identifier.issn 1664-1078
dc.identifier.uri http://dx.doi.org/10.3389/fpsyg.2023.1293933
dc.identifier.uri https://www.webofscience.com/wos/woscc/full-record/WOS:001100101200001
dc.identifier.uri http://earsiv.odu.edu.tr:8080/xmlui/handle/11489/4015
dc.description WoS Categories: Psychology, Multidisciplinary en_US
dc.description Web of Science Index: Social Science Citation Index (SSCI) en_US
dc.description Research Areas: Psychology en_US
dc.description.abstract Early age of gambling onset, ease of gambling with technological developments and lack of controlling online gambling games have led to unmanageable risk of gambling. Individual-centered approaches play a significant role in managing the risk that gambling poses on public health and discerning the heterogeneity of gambling addiction. Therefore, this study employed Latent Profile Analysis (LPA), one of the individual-centered approaches, to model the interactions across the psychosocial characteristics of gamblers. The study aims to reveal the latent profiles of gambling addiction. Unlike variable-centered approaches, LPA is a contemporary technique that provides objective information regarding individual psychological processes and behaviors. The profile indicators of the study involve psychosocial characteristics such as resilience, motives to gamble (excitement/fun, avoidance, making money, socializing), purposefulness, responsibility and worthiness. Data were collected from 317 volunteers (M = 68.9%; F = 31.1%; mean age = 25.16 +/- 6.46) through the Brief Resilience Scale (BRS), Gambling Motives Scale (GMS) and Personal Virtues Scale (PVS). The emerging profiles were defined as adventurous players (14.2%), social gamblers (9.8%), professional gamblers (32.8%), problem gamblers (24.6%) and avoidant gamblers (18.6%). The individual-centered modeling is congruent with the literature on gambling and provides a complementary perspective to understand the heterogeneous structure of gambling. The results are expected to assist mental health professionals in developing educational and clinical intervention programs for gambling behavior. Finally yet importantly, it is recommended that new LPA models be offered through the use of different indicators related to gambling addiction. en_US
dc.description.sponsorship The author declares that no financial support was received for the research, authorship, and/or publication of this article. en_US
dc.language.iso eng en_US
dc.publisher FRONTIERS MEDIA SA-LAUSANNE en_US
dc.relation.isversionof 10.3389/fpsyg.2023.1293933 en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject gambling, gambling motives, latent profiles analyze, personal virtues, resilience en_US
dc.subject BRIEF RESILIENCE SCALE, PERSONALITY-DISORDERS, PREVALENCE, MODEL, PREDICTORS, MOTIVATION, GAMBLERS, THERAPY, ADOLESCENTS, COMORBIDITY en_US
dc.title Latent profile analysis of gambling en_US
dc.type article en_US
dc.relation.journal FRONTIERS IN PSYCHOLOGY en_US
dc.contributor.department Ordu Üniversitesi en_US
dc.identifier.volume 14 en_US


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