Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers

被引：443
|
作者：
Ferguson, Sarah L. [1 ]
Moore, E. Whitney G. [2 ]
Hull, Darrell M. [3 ]
机构：
[1] Rowan Univ, Glassboro, NJ USA
[2] Wayne State Univ, Detroit, MI 48202 USA
[3] Univ North Texas, Denton, TX 76203 USA
来源：
INTERNATIONAL JOURNAL OF BEHAVIORAL DEVELOPMENT | 2020年 / 44卷 / 05期
关键词：
Latent profile analysis; latent variable modeling; teaching paper; AUXILIARY VARIABLES; INDEX PERFORMANCE; MIXTURE-MODELS; NUMBER; COMPONENTS; POWER;
D O I：
10.1177/0165025419881721
中图分类号：
B844 [发展心理学（人类心理学）];
学科分类号：
040202 ;
摘要：
The present guide provides a practical guide to conducting latent profile analysis (LPA) in the Mplus software system. This guide is intended for researchers familiar with some latent variable modeling but not LPA specifically. A general procedure for conducting LPA is provided in six steps: (a) data inspection, (b) iterative evaluation of models, (c) model fit and interpretability, (d) investigation of patterns of profiles in a retained model, (e) covariate analysis, and (f) presentation of results. A worked example is provided with syntax and results to exemplify the steps.
引用
收藏
页码：458 / 468
页数：11
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