An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling
An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling
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DOI：
10.1111/j.1751-9004.2007.00054.x
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发表时间：
2008-01-01
影响因子：
4.6
通讯作者：
Wickrama, K. A. S.
中科院分区：
文献类型：
--
作者：
Jung, Tony;Wickrama, K. A. S.
近年来，研究人员对使用潜在类别和增长混合建模技术用于社会和心理科学中的应用越来越感兴趣，部分原因是由于为此目的设计的计算机软件的进步和可用性（例如，Mplus和SAS Proc Traj）。潜在增长建模方法，如潜在类增长分析（LCGA）和增长混合建模（GMM），已越来越多地认识到其有用的识别同质亚群在较大的异质性人口和识别有意义的群体或类的个人。本文的目的是提供一个概述的LCGA和GMM，比较不同的潜在增长建模技术，讨论当前的争论和问题，并为读者提供一个实用的指导进行LCGA和GMM使用Mplus软件。
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.