Publication | Closed Access
Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers
749
Citations
37
References
2019
Year
EngineeringFactor ModelsEducationProgram EvaluationLatent GroupsParallel AnalysisLatent ModelingProfiling TechniqueData ScienceData MiningObserved DataFactor AnalysisStatisticsLatent Variable MethodsKnowledge DiscoveryUser ProfilingLatent Variable ModelLatent Variable ModelingFunctional Data AnalysisMplus Software SystemQuantitative Social Science ResearchLatent Profile AnalysisData Modeling
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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