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1 results

  • 8 - Latent Variable Mixture Modeling Approaches to Investigating Longitudinal Recovery Processes

  • from Part I - Micro Level
  • Edited by Jalie A. Tucker, University of Florida, Katie Witkiewitz, University of New Mexico
  • Book:
    Dynamic Pathways to Recovery from Alcohol Use Disorder
    Published online:
    23 December 2021
    Print publication:
    06 January 2022, pp 137-156

  • Summary

    Alcohol researchers are often interested in identifying heterogeneous subgroups of drinkers, such as those with stable patterns of moderation drinking or patterns of heavy episodic drinking. Subgroups may also be identified based on qualitatively different developmental courses in the onset of alcohol use disorder (AUD) or pathways to recovery from AUD. This chapter provides an overview of latent variable mixture modeling, which can be useful for investigating such heterogeneity. First, mixture models applied to cross-sectional data are described, specifically latent class analysis and latent profile analysis. Then conventional latent growth modeling is discussed as a special case of growth mixture models, where subgroups of individuals are identified based on the shape of their growth trajectories. Mixture models applied to mediation analysis are also discussed. The chapter concludes with some practical issues to consider when using mixture modeling.