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

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          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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          Most cited references45

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          Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

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            Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review

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              Power analysis and determination of sample size for covariance structure modeling.

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                International Journal of Behavioral Development
                International Journal of Behavioral Development
                SAGE Publications
                0165-0254
                1464-0651
                September 2020
                November 22 2019
                September 2020
                : 44
                : 5
                : 458-468
                Affiliations
                [1 ]Rowan University, USA
                [2 ]Wayne State University, USA
                [3 ]University of North Texas, USA
                Article
                10.1177/0165025419881721
                5412d115-b357-4cce-af75-e21ac5b2fd50
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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