multilevLCA: An R Package for Single-Level and Multilevel Latent Class Analysis with Covariates

TL;DR

multilevLCA is an R package that provides comprehensive tools for conducting single-level and multilevel latent class analysis, including model estimation, selection, covariate effects, and visualization, with real data examples.

Contribution

This paper introduces the multilevLCA R package, offering innovative methods for latent class analysis of multilevel categorical data with covariates, including model selection and visualization features.

Findings

Effective maximum likelihood estimation with refined initialization.

Semi-automatic model selection without prior class number info.

Visualization tools for model results.

Abstract

This contribution presents a guide to the R package multilevLCA, which offers a complete and innovative set of technical tools for the latent class analysis of single-level and multilevel categorical data. We describe the available model specifications, mainly falling within the fixed-effect or random-effect approaches. Maximum likelihood estimation of the model parameters, enhanced by a refined initialization strategy, is implemented either simultaneously, i.e., in one-step, or by means of the more advantageous two-step estimator. The package features i) semi-automatic model selection when a priori information on the number of classes is lacking, ii) predictors of class membership, and iii) output visualization tools for any of the available model specifications. All functionalities are illustrated by means of a real application on citizenship norms data, which are available in the package.