Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM

Caspar J. Van Lissa · Mauricio Garnier‐Villarreal · Daniel Anadria

110 citations0/0 files storedStructural Equation Modeling A Multidisciplinary Journal

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Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models

Abstract

Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there is a lack of user-friendly open-source implementations. Based on contemporary academic discourse, this paper introduces recommendations for LCA which are summarized in the SMART-LCA checklist: Standards for More Accuracy in Reporting of different Types of Latent Class Analysis. The free open-source R-package package tidySEM implements the practices recommended here. It is easy for beginners to adopt thanks to user-friendly wrapper functions, and yet remains relevant for expert users as its models are integrated within the OpenMx structural equation modeling framework and remain fully customizable. The Appendices and tidySEM package vignettes include tutorial examples of common applications of LCA.

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2012lavaan : An R Package for Structural Equation Modeling 2007Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study 2007An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling 2012When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions.2010Latent Class Modeling with Covariates: Two Improved Three-Step Approaches 2018Ten frequently asked questions about latent class analysis.2020Latent Class Analysis: A Guide to Best Practice 2020Latent profile analysis: A review and “how to” guide of its application within vocational behavior research

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