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Multilevel latent class analysis: state-of-the-art methodologies and their implementation in the R package multilevLCA

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Listed:
  • Lyrvall, Johan
  • Di Mari, Roberto
  • Bakk, Zsuzsa
  • Oser, Jennifer
  • Kuha, Jouni

Abstract

Latent class (LC) analysis is a model-based clustering approach for categorical data, with a wide range of applications in the social sciences and beyond. When the data have a hierarchical structure, the multilevel LC model can be used to account for higher-level dependencies between the units by means of a further categorical LC variable at the group level. The research interest of LC analysis typically lies in the relationship between the LCs and external covariates, or predictors. To estimate LC models with covariates, researchers can use the one-step approach, or the generally recommended stepwise estimators, which separate the estimation of the clustering model from the subsequent estimation of the regression model. The package multilevLCA has the most comprehensive set of model specifications and estimation approaches for this family of models in the open-source domain, estimating single- and multilevel LC models, with and without covariates, using the one-step and stepwise approaches.

Suggested Citation

  • Lyrvall, Johan & Di Mari, Roberto & Bakk, Zsuzsa & Oser, Jennifer & Kuha, Jouni, 2025. "Multilevel latent class analysis: state-of-the-art methodologies and their implementation in the R package multilevLCA," LSE Research Online Documents on Economics 127782, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:127782
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    References listed on IDEAS

    as
    1. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    2. Murray Aitkin, 1999. "A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 55(1), pages 117-128, March.
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    More about this item

    Keywords

    latent class analysis; R; categorical data; multilevel; stepwise estimation; Latent class analysis;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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