- Title
- In search of the optimum structural model for internet gaming disorder
- Creator
- Stavropoulos, Vasileios; Gomez, Rapson; Griffiths, Mark
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176633
- Identifier
- vital:15163
- Identifier
-
https://doi.org/10.1186/s12888-021-03148-8
- Identifier
- ISBN:1471-244X (ISSN)
- Abstract
- Background: Internet gaming Disorder (IGD) constitutes a recently proposed clinical disorder (American Psychiatric Association, Diagnostic and statistical manual of mental disorders, 2013). The present study examined if IGD is best conceptualized as categorical (present/absent), or dimensional (severity ranging from low to high), or both (i.e., hybrid of categorical/dimensional). Methods: Ratings of the nine DSM-5 IGD symptoms, as presented in the Internet Gaming Disorder Scale 9-Short Form (Pontes & Griffiths, Comput Hum Behav 45:137-143, 2015), from 738 gamers, aged 17 to 72 years, were collected. Confirmatory factor analysis (CFA), latent class analysis (LCA), and factor mixture modelling analysis (FMMA) procedures were applied to determine the optimum IGD model. Results: Although the findings showed most support for a FFMA model with two classes and one factor, there was also good statistical and substantive support for the one-factor CFA model, and the LCA model with three classes. Conclusion: It was concluded that while the optimum structure of IGD is most likely to be a hybrid model (i.e., concurrently categorical and dimensional), a uni-dimensional model and/or a three-class categorical model are also plausible. © 2021, The Author(s).
- Publisher
- BioMed Central Ltd
- Relation
- BMC Psychiatry Vol. 21, no. 1 (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © The Author(s). 2021
- Rights
- Open Access
- Subject
- 1103 Clinical Sciences; 1117 Public Health and Health Services; 1701 Psychology; Confirmatory factor analysis; Factor mixture modelling; Factor structure; Internet gaming disorder; Latent class analysis
- Full Text
- Reviewed
- Funder
- Dr. Vasileios Stavropoulos has received the Australian Research Council, Discovery Early Career Researcher Award, DE210101107.
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