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Stepwise Latent Class Models for Explaining Group-Level Outcomes Using Discrete Individual-Level Predictors.

Margot Bennink1, Marcel A Croon2, Jeroen K Vermunt2

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|December 31, 2015
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Summary
This summary is machine-generated.

This study introduces stepwise latent class analysis to aggregate discrete individual-level data for group-level analysis, correcting for measurement errors. This method enhances multilevel mediation models, improving explanations of group outcomes from individual predictors.

Keywords:
discrete variableslatent class analysismicro-macro analysismultilevel analysisstepwise modeling

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Area of Science:

  • Multilevel modeling
  • Latent variable analysis
  • Quantitative psychology

Background:

  • Explaining group outcomes requires aggregating individual data and correcting for measurement errors.
  • Discrete variables pose challenges for standard aggregation and error correction methods.
  • Existing methods lack clear approaches for discrete individual-level predictors in multilevel analyses.

Purpose of the Study:

  • To present a novel stepwise latent class analysis (LCA) approach for aggregating discrete individual-level predictors to the group level.
  • To correct for measurement errors in aggregated group-level variables derived from discrete individual data.
  • To apply and evaluate this method within multilevel mediation models.

Main Methods:

  • Estimating a latent class model using individual-level discrete predictor scores to form group-level latent classes.
  • Aggregating the individual-level predictor by assigning groups to these latent classes.
  • Conducting group-level analysis with error correction for class assignments, applied to multilevel mediation models.

Main Results:

  • The stepwise LCA approach effectively aggregates discrete individual data for group-level analysis.
  • The method corrects for measurement error in aggregated group-level variables.
  • Simulation studies demonstrate the approach's utility and compare it to existing methods.

Conclusions:

  • Stepwise latent class analysis provides a robust framework for handling discrete individual-level predictors in multilevel research.
  • This method enhances the accuracy of explaining group-level outcomes.
  • The approach is applicable to complex mediation models with multiple individual and group-level variables.