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

Ferguson, Sarah L., G. Moore, E. Whitney, Hull, Darrell M.·2020·International Journal of Behavioral Development

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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Metadata

Authors
Ferguson, S. L., G. Moore, E. W., & Hull, D. M.
Venue
International Journal of Behavioral Development
Published
2019-11-22
Volume
44
Issue
5
Pages
458-468
ISSN
  • 0165-0254 (ISSN-L)
  • 1464-0651
OpenAlex
W2991271416
Cited by
911
References
46
FWCI
27.72
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers. https://app.cochat.ai/papers/10.1177/0165025419881721
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Ferguson, S. L., G. Moore, E. W., & 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. https://doi.org/10.1177/0165025419881721

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.