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

from Part I - Micro Level

Published online by Cambridge University Press:  23 December 2021

By
JeeWon Cheong ,
Adam D. Wilson  and
Juhan Lee
Edited by
Jalie A. Tucker  and
Katie Witkiewitz
Jalie A. Tucker
Affiliation:
University of Florida
Katie Witkiewitz
Affiliation:
University of New Mexico
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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.

Keywords

alcohol use disorderrecoverymixture modelingperson-centered approachlatent class analysislatent profile analysisgrowth mixture modelingperson-centered mediation analysis
Type
Chapter
Information
Dynamic Pathways to Recovery from Alcohol Use Disorder
Meaning and Methods
 , pp. 137 - 156
Publisher: Cambridge University Press
Print publication year: 2022

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