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

By Sarah Ferguson, E. W. G. Moore, D. Hull. 2020

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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.

Subfields: Artificial Intelligence, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1fd2df5156afb88e232b2e1f29f5850fffbc4c11