共 50 条
Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers
被引:472
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
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
相关论文
共 50 条