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A Monte Carlo evaluation of growth mixture modeling

Published online by Cambridge University Press:  15 March 2021

Tiffany M. Shader Open the ORCID record for Tiffany M. Shader [Opens in a new window]  and
Theodore P. Beauchaine Open the ORCID record for Theodore P. Beauchaine [Opens in a new window]
Tiffany M. Shader
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
Theodore P. Beauchaine*
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
*
Author for Correspondence: Theodore P. Beauchaine, Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, OH 43210; Email: beauchaine.1@osu.edu
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Abstract

Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

Keywords

growth mixture modelinglongitudinalMonte Carlosimulationlatent classes
Type
Regular Article
Information
Development and Psychopathology , Volume 34 , Issue 4 , October 2022 , pp. 1604 - 1617
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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