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

Roberto Mari (), Zsuzsa Bakk, Jennifer Oser and Jouni Kuha
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Roberto Mari: University of Catania
Zsuzsa Bakk: Leiden University
Jennifer Oser: Ben-Gurion University
Jouni Kuha: London School of Economics and Political Science

Psychometrika, 2023, vol. 88, issue 4, No 3, 1144-1170

Abstract: 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.

Keywords: multilevel latent class analysis; covariates; stepwise estimators; pseudo ML (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-023-09929-2

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