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.
Authors 4
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Affiliation as printed
Department of Methodology and Statistics , Leiden University , The Netherlands
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Ben-Gurion University of the Negev
Affiliation as printed
Department of Politics and Government , Ben-Gurion University , Israel
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London School of Economics and Political Science
Affiliation as printed
Corso Italia 55 , 95128 , Catania , Italy
Department of Statistics , London School of Economics and Political Science , London , UK
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