On the application of the three-step approach to growth mixture models

Journal article


Diallo, Thierno M. O. and Lu, Hui Zhong. (2017) On the application of the three-step approach to growth mixture models. Structural Equation Modeling: A Multidisciplinary Journal. 24(5), pp. 714 - 732. https://doi.org/10.1080/10705511.2017.1322516
AuthorsDiallo, Thierno M. O. and Lu, Hui Zhong
Abstract

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.

KeywordsBias; classification error; growth mixture modeling; three-step
Year2017
JournalStructural Equation Modeling: A Multidisciplinary Journal
Journal citation24 (5), pp. 714 - 732
PublisherRoutledge
ISSN1070-5511
Digital Object Identifier (DOI)https://doi.org/10.1080/10705511.2017.1322516
Scopus EID2-s2.0-85019617017
Page range714 - 732
Research GroupInstitute for Positive Psychology and Education
Place of publicationUnited Kingdom
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https://acuresearchbank.acu.edu.au/item/8897y/on-the-application-of-the-three-step-approach-to-growth-mixture-models

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