On the Application of the Three-Step Approach to Growth Mixture Models

This series of simulation studies evaluate, in the context of applied research settings, the impact of the parameterization of the covariance structure of the growth mixture model (GMM) on the regression coefficient and standard error estimates in the 3-step method. The results show that the 1-step...

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Bibliographic Details
Published in:Structural equation modeling 2017-09, Vol.24 (5), p.714-732
Main Authors: Diallo, Thierno M. O., Lu, HuiZhong
Format: Article
Language:English
Subjects:
Bias
classification error
growth mixture modeling
Growth models
Mathematical models
three-step
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Summary:This series of simulation studies evaluate, in the context of applied research settings, the impact of the parameterization of the covariance structure of the growth mixture model (GMM) on the regression coefficient and standard error estimates in the 3-step method. The results show that the 1-step approach performs better than the 3-step method across the simulation studies. However, the performance of the 3-step method depends slightly or importantly on the parameterization of the GGM from the first step, on the inclusion or not of the predictor at the first step of the analysis, on the population model, and on the type (i.e., logit vs. linear) and size of the regression coefficient estimates.
ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2017.1322516