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Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis

Author

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  • Paweł A. Atroszko

    (Faculty of Social Sciences, University of Gdańsk, 80-309 Gdańsk, Poland)

  • Bartosz Atroszko

    (Faculty of Social Sciences, University of Gdańsk, 80-309 Gdańsk, Poland)

  • Edyta Charzyńska

    (Faculty of Social Sciences, University of Silesia in Katowice, 40-007 Katowice, Poland)

Abstract

Background: Relatively strong theoretical assumptions and previous studies concerning co-occurring addictive behaviors suggest a subpopulation representing general proclivity to behavioral addictions (BAs), and there are gender-specific subpopulations. This study aimed to compare latent profile analysis (LPA) and latent class analysis (LCA) as the methods of investigating different clusters of BAs in the general student population and among students positively screened for at least one BA. Participants and procedure: Analyses of six BAs (study, shopping, gaming, Facebook, pornography, and food) and their potential antecedents (personality) and consequences (well-being) were conducted on a full sample of Polish undergraduate students ( N = 1182) and a subsample ( n = 327) of students including individuals fulfilling cutoff for at least one BA. Results: LPA on the subsample mostly replicated the previous four profiles found in the full sample. However, LCA on a full sample did not replicate previous findings using LPA and showed only two classes: those with relatively high probabilities on all BAs and low probabilities. LCA on the subsample conflated profiles identified with LPA and classes found with LCA in the full sample. Conclusions: LCA on dichotomized scores (screened positively vs. negatively) were less effective in identifying clear patterns of interrelationships between BAs based on relatively strong theoretical assumptions and found in previous research. BAs can be investigated on the whole spectrum of behavior, and person-centered analyses might be more useful when they are based on continuous scores. This paper provides more detailed analyses of the four basic clusters of BAs, prevalence, and co-occurrence of particular BAs within and between them, their gender and personality risk factors, relationships to well-being, and their interrelationships as emerging from the results of this and previous studies.

Suggested Citation

  • Paweł A. Atroszko & Bartosz Atroszko & Edyta Charzyńska, 2021. "Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8590-:d:614360
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    References listed on IDEAS

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    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Paweł A. Atroszko & Zsolt Demetrovics & Mark D. Griffiths, 2020. "Work Addiction, Obsessive-Compulsive Personality Disorder, Burn-Out, and Global Burden of Disease: Implications from the ICD-11," IJERPH, MDPI, vol. 17(2), pages 1-13, January.
    3. Mélissa Lemoine & Gerhard Gmel & Simon Foster & Simon Marmet & Joseph Studer, 2020. "Multiple trajectories of alcohol use and the development of alcohol use disorder: Do Swiss men mature-out of problematic alcohol use during emerging adulthood?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    4. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
    5. Neal V. Dawson & Robert Weiss, 2012. "Dichotomizing Continuous Variables in Statistical Analysis," Medical Decision Making, , vol. 32(2), pages 225-226, March.
    6. Darrat, Aadel A. & Darrat, Mahmoud A. & Amyx, Douglas, 2016. "How impulse buying influences compulsive buying: The central role of consumer anxiety and escapism," Journal of Retailing and Consumer Services, Elsevier, vol. 31(C), pages 103-108.
    7. Sara Konrath & Brian P Meier & Brad J Bushman, 2014. "Development and Validation of the Single Item Narcissism Scale (SINS)," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-15, August.
    8. Pawe³ A. Atroszko & Bartosz Atroszko, 2020. "The Costs of Work-Addicted Managers in Organizations: Towards Integrating Clinical and Organizational Frameworks," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 22(Special 1), pages 1265-1265, November.
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