Document Type
Article
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.
Disciplines
Education | Kinesiology | Sports Sciences
Recommended Citation
Ferguson, S. L., Moore, E. W. G., & Hull, D. M. (2020). Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers. International Journal of Behavioral Development, 44(5), 458-468. DOI: 10.1177/0165025419881721
Included in
Education Commons, Kinesiology Commons, Sports Sciences Commons
Comments
This is the final accepted version of the publication appearing in the International Journal of Behavioral Development, DOI 10.1177/0165025419881721. Per the publisher, reuse of this article is restricted to non-commercial and no derivative uses.