Multilevel Latent Class Analysis: State-of-the-Art Methodologies and Their Implementation in the <tt>R</tt> Package <tt>multilevLCA</tt>

Latent class (LC) analysis is a model-based clustering approach for categorical data, with a wide range of applications in the social sciences and beyond. When the data have a hierarchical structure, the multilevel LC model can be used to account for higher-level dependencies between the units by means of a further categorical LC variable at the group level. The research interest of LC analysis typically lies in the relationship between the LCs and external covariates, or predictors. To estimate LC models with covariates, researchers can use the one-step approach, or the generally recommended stepwise estimators, which separate the estimation of the clustering model from the subsequent estimation of the regression model. The package multilevLCA has the most comprehensive set of model specifications and estimation approaches for this family of models in the open-source domain, estimating single- and multilevel LC models, with and without covariates, using the one-step and stepwise approaches.