Please use this identifier to cite or link to this item: http://earsiv.odu.edu.tr:8080/xmlui/handle/11489/4015
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dc.contributor.authorCitak, Senel-
dc.date.accessioned2024-03-15T06:47:00Z-
dc.date.available2024-03-15T06:47:00Z-
dc.date.issued2023-
dc.identifier.citationÇitak, S. (2023). Latent profile analysis of gambling. Front. Psychol., 14. https://doi.org/10.3389/fpsyg.2023.1293933en_US
dc.identifier.issn1664-1078-
dc.identifier.urihttp://dx.doi.org/10.3389/fpsyg.2023.1293933-
dc.identifier.urihttps://www.webofscience.com/wos/woscc/full-record/WOS:001100101200001-
dc.identifier.urihttp://earsiv.odu.edu.tr:8080/xmlui/handle/11489/4015-
dc.descriptionWoS Categories: Psychology, Multidisciplinaryen_US
dc.descriptionWeb of Science Index: Social Science Citation Index (SSCI)en_US
dc.descriptionResearch Areas: Psychologyen_US
dc.description.abstractEarly 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.sponsorshipThe author declares that no financial support was received for the research, authorship, and/or publication of this article.en_US
dc.language.isoengen_US
dc.publisherFRONTIERS MEDIA SA-LAUSANNEen_US
dc.relation.isversionof10.3389/fpsyg.2023.1293933en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectgambling, gambling motives, latent profiles analyze, personal virtues, resilienceen_US
dc.subjectBRIEF RESILIENCE SCALE, PERSONALITY-DISORDERS, PREVALENCE, MODEL, PREDICTORS, MOTIVATION, GAMBLERS, THERAPY, ADOLESCENTS, COMORBIDITYen_US
dc.titleLatent profile analysis of gamblingen_US
dc.typearticleen_US
dc.relation.journalFRONTIERS IN PSYCHOLOGYen_US
dc.contributor.departmentOrdu Üniversitesien_US
dc.identifier.volume14en_US
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