文章基本信息

标题：Evaluation of Analysis Approaches for Latent Class Analysis with Auxiliary Linear Growth Model
本地全文：下载
作者：Akihito Kamata ; Yusuf Kara ; Chalie Patarapichayatham 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2018
卷号：9
页码：1-12
DOI：10.3389/fpsyg.2018.00130
出版社：Frontiers Media
摘要：This study investigated the performance of three selected approaches to estimating a two-phase mixture model, where the first phase was a two-class latent class analysis model and the second phase was a linear growth model with four time points. The three evaluated methods were (a) one-step approach, (b) three-step approach, and (c) case-weight approach. As a result, some important results were demonstrated. First, the case-weight and three-step approaches demonstrated higher convergence rate than the one-step approach. Second, it was revealed that case-weight and three-step approaches generally did better in correct model selection than the one-step approach. Third, it was revealed that parameters were similarly recovered well by all three approaches for the larger class. However, parameter recovery for the smaller class differed between the three approaches. For example, the case-weight approach produced constantly lower empirical standard errors. However, the estimated standard errors were substantially underestimated by the case-weight and three-step approaches when class separation was low. Also, bias was substantially higher for the case-weight approach than the other two approaches.
关键词：mixture model; latent class analysis; one-step approach; case-weight approach; Three-step approach