A Two-Step Estimator for Multilevel Latent Class Analysis with Covariates

Psychometrika, vol. 88, pp. 1144–1170

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.

Authors 4

1. Roberto Di Mari corresponding

   University of Catania

   Affiliation as printed

   Department of Economics and Business, University of Catania, Corso Italia 55, 95128, Catania, Italy. roberto.dimari@unict.it

   Department of Economics and Business, University of Catania, Corso Italia 55, 95128, Catania, Italy

2. Zsuzsa Bakk Aachen Department of Methodology and Statistics

   Leiden University

   Affiliation as printed

   Department of Methodology and Statistics, Leiden University, Leiden, The Netherlands

3. Jennifer Lynn Oser

   Ben-Gurion University of the Negev

   Affiliation as printed

   Department of Politics and Government, Ben-Gurion University, Beer sheva, Israel

4. Jouni Kuha

   London School of Economics and Political Science

   Affiliation as printed

   Department of Statistics, London School of Economics and Political Science, London, UK