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glca: An R Package for Multiple-Group Latent Class Analysis.

Youngsun Kim1, Saebom Jeon2, Chi Chang3

  • 1Korea University, Seoul, Korea.

Applied Psychological Measurement
|July 11, 2022
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Summary
This summary is machine-generated.

This study introduces the R package glca for exploring group differences in latent class analysis (LCA). It handles multilevel data and offers methods for both fixed-effect and random-effect LCA to uncover population-level variations.

Keywords:
R packageglcalatent class analysismeasurement invariancemultilevel data

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Area of Science:

  • Statistics
  • Psychometrics
  • Social Sciences

Background:

  • Latent Class Analysis (LCA) is used to identify unobserved subgroups within a population.
  • Understanding group similarities and differences is crucial in LCA, but complex data structures can pose challenges.
  • Measurement invariance tests are key to distinguishing identical versus differing latent structures across groups.

Purpose of the Study:

  • To develop an R package, glca, for exploring and testing differences in latent class structures across populations.
  • To accommodate multilevel data structures within latent class analysis.
  • To provide statistical procedures for comparing latent class models between groups.

Main Methods:

  • Implementation of fixed-effect LCA for populations segmented by observed group variables.
  • Implementation of nonparametric random-effect LCA for situations with numerous group levels, identifying group-level latent variables.
  • Development of statistical tests within the glca package for exploring group differences in various LCA models.

Main Results:

  • The glca package offers a comprehensive framework for multilevel latent class analysis.
  • It provides distinct approaches for fixed-effect and random-effect LCA, enhancing flexibility.
  • The package facilitates robust statistical testing of measurement invariance and latent structure differences across groups.

Conclusions:

  • The glca R package provides valuable tools for researchers investigating population heterogeneity using LCA.
  • It effectively addresses the complexities of multilevel data in latent class modeling.
  • The package supports rigorous examination of group differences in latent structures, advancing comparative research.