Comparison of Three Approaches to Class Enumeration in Growth Mixture Modeling when Time Structures are Variant Across Latent Classes. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Lee, Sooyong; Whittaker, Tiffany A.
• Is Part Of: Structural equation modeling. Volume 29:Issue 1(2022)
• Page Start: 23
• Page End: 35
• Publication Date: 2022-01-02
• Subjects: Growth mixture modelling -- misspecification -- time structure -- unrestricted mixture modeling; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2021.1956320
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: In conventional approaches to Growth Mixture Modeling (GMM), a trajectory is first estimated using latent growth curve modeling that serves as a baseline trajectory for the GMM. In this approach, time structures are held invariant across latent classes when identifying the number of latent classes. However, this popular way of conducting GMM could undermine a proper estimation, especially under the condition where a distinct trajectory exists for different classes in the population. This study compared the class enumeration performance in a conventional GMM against two alternatives in which latent classes do not take the same functional forms of change across time: (1) Unstructured Mixture Models (UMM) and (2) Latent Basis Models (LBM). Results revealed that the UMM performs well when one latent class takes a different shape of growth. Based on various design conditions, the relative performance of the three approaches in terms of class enumeration is examined and discussed.