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Multilevel Latent Class Analysis: State-of-the-Art Methodologies and Their Implementation in the R Package

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  • 1Department of Economics and Business, University of Catania, Catania, Italy.

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Summary
This summary is machine-generated.

Latent class (LC) analysis clusters categorical data. Multilevel LC models handle hierarchical data, with the multilevLCA package offering comprehensive estimation approaches for these complex models.

Keywords:
Latent class analysisRcategorical datamultilevelstepwise estimation

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Area of Science:

  • Social Sciences
  • Statistics
  • Data Analysis

Background:

  • Latent class (LC) analysis is a model-based clustering method for categorical data.
  • Multilevel LC models extend this to hierarchical data, incorporating group-level dependencies.
  • Research often focuses on the relationship between latent classes and external covariates.

Purpose of the Study:

  • To introduce the multilevLCA package for estimating single- and multilevel latent class models.
  • To provide a comprehensive set of model specifications and estimation approaches in the open-source domain.
  • To facilitate the analysis of latent class models with and without covariates, using one-step and stepwise methods.

Main Methods:

  • The study focuses on the capabilities of the multilevLCA R package.
  • It covers estimation for single- and multilevel latent class models.
  • Both one-step and stepwise estimation approaches for models with covariates are supported.

Main Results:

  • The multilevLCA package offers the most comprehensive set of tools for latent class analysis in open-source software.
  • It supports various model specifications, including multilevel structures and covariate adjustments.
  • Both one-step and stepwise estimation strategies are available for models with predictors.

Conclusions:

  • The multilevLCA package provides a versatile and comprehensive solution for latent class analysis, particularly for multilevel data.
  • Researchers can effectively model complex relationships between latent classes and covariates using this package.
  • It advances the accessibility and application of advanced latent class modeling techniques.