ArticlePsychometrika2023
A two-step estimator for multilevel latent class analysis with covariates.
Article in Psychometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Unveiling hidden heterogeneity and inequalities in the continuum of care for reproductive, maternal, and child health services in sub-Saharan Africa: A multilevel latent class analysis approach.Global epidemiology · 2026Article
- Spectral Clustering with Likelihood Refinement for High-Dimensional Latent Class Recovery.Psychometrika · 2026Article
- Multi-dimensional sleep patterns predict complications and mortality in chronic kidney disease patients: a prospective study from the UK Biobank.Frontiers in public health · 2026Article
- Patterns of maternal and child health services utilization and associated socioeconomic disparities in sub-Saharan Africa.Nature communications · 2025Article
- Two-Step Multilevel Latent Class Analysis in the Presence of Measurement Non-Equivalence.Structural equation modeling : a multidisciplinary journal · 2025Article
- Bias-Adjusted Three-Step Multilevel Latent Class Modeling with Covariates.Structural equation modeling : a multidisciplinary journal · 2024Article
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4 authors.
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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 (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) 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.
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