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

2019/11/22 by Sarah L. Ferguson, E. Whitney G. Moore, Darrell M. Hull · 2 citations

paper · doi:10.1177/0165025419881721

crossref issued 2019/11/22 · crossref published 2019/11/22 · crossref published-online 2019/11/22 · crossref created 2019/11/22 · crossref published-print 2020/09/01 · crossref deposited 2026/04/29 · crossref indexed 2026/08/04

Abstract

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.

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