On the Application of the Three-Step Approach to Growth Mixture Models. Issue 5 (3rd September 2017)

• Record Type: Journal Article
• Authors: Diallo, Thierno M. O.; Lu, HuiZhong
• Is Part Of: Structural equation modeling. Volume 24:Issue 5(2017)
• Page Start: 714
• Page End: 732
• Publication Date: 2017-09-03
• Subjects: Bias -- classification error -- growth mixture modeling -- three-step; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2017.1322516
• Languages: English
• ISSNs: 1070-5511

Abstract

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.