A two-step estimator for multilevel latent class analysis with covariates

Abstract

We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (i) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (ii) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.

arXiv commentManuscript version accepted for publication in Psychometrika

References in corpus (4)

• Identifiability of parameters in latent structure models with many observed variables
• A two-step estimator for multilevel latent class analysis with covariates
• Identifiability of Latent Class Models with Covariates
• Multilevel latent class analysis with covariates: Analysis of cross-national citizenship norms with a two-stage approach

Cited by in corpus (2)

• A two-step estimator for multilevel latent class analysis with covariates
• Clustering Hierarchies via a Semi-Parametric Generalized Linear Mixed Model: a statistical significance-based approach