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

Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser and Jouni Kuha

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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; European Union grant (ERC; PRD; project number 101077659).; Starting Grant FIRE; PIACERI 2020/2022 (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2023-12-01
New Economics Papers: this item is included in nep-ecm
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Published in Psychometrika, 1, December, 2023, 88(4), pp. 1144 - 1170. ISSN: 0033-3123

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:119994

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