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An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling
Author(s) -
Jung Tony,
Wickrama K. A. S.
Publication year - 2008
Publication title -
social and personality psychology compass
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 53
ISSN - 1751-9004
DOI - 10.1111/j.1751-9004.2007.00054.x
Subject(s) - latent class model , mixture model , latent growth modeling , homogeneous , psychology , structural equation modeling , class (philosophy) , growth model , identification (biology) , latent variable , latent variable model , computer science , artificial intelligence , developmental psychology , machine learning , mathematics , botany , mathematical economics , combinatorics , biology
In recent years, there has been a growing interest among researchers in the use of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, in part due to advances in and availability of computer software designed for this purpose (e.g., Mplus and SAS Proc Traj). Latent growth modeling approaches, such as latent class growth analysis (LCGA) and growth mixture modeling (GMM), have been increasingly recognized for their usefulness for identifying homogeneous subpopulations within the larger heterogeneous population and for the identification of meaningful groups or classes of individuals. The purpose of this paper is to provide an overview of LCGA and GMM, compare the different techniques of latent growth modeling, discuss current debates and issues, and provide readers with a practical guide for conducting LCGA and GMM using the Mplus software.