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Bayesian Multilevel Latent Class Profile Analysis: Inference and Estimation for Exploring the Diverse Pathways to

JungWun Lee1, D Betsy McCoach2, Ofer Harel3

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Summary
This summary is machine-generated.

This study introduces Bayesian estimation for multilevel latent class profile analysis (MLCPA), offering a robust alternative to maximum likelihood estimation. Findings reveal when each method performs best for understanding student academic trajectories.

Keywords:
Academic proficiencyBayesian estimationhierarchical data structurelatent class analysislongitudinal study

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Area of Science:

  • Statistics
  • Educational Psychology
  • Data Analysis

Background:

  • Multilevel latent class profile analysis (MLCPA) is crucial for longitudinal studies.
  • Conventional maximum likelihood (ML) estimation faces challenges with small samples and boundary issues.
  • The underflow problem can occur in MLCPA due to multilevel structures.

Purpose of the Study:

  • To propose and evaluate Bayesian estimation for MLCPA as an alternative to ML estimation.
  • To investigate the underflow problem in MLCPA.
  • To compare the performance of Bayesian and ML estimates in various simulation conditions.

Main Methods:

  • Developed a Bayesian estimation approach for MLCPA using non-informative priors.
  • Conducted extensive numerical simulations to compare Bayesian and ML estimates.
  • Analyzed longitudinal academic performance data from the Progress Monitoring and Reporting Network.

Main Results:

  • Bayesian estimates are preferred when latent classes are well-separated.
  • ML estimates are preferred when latent classes overlap.
  • Identified distinct student academic proficiency trajectories and school-level latent groups.

Conclusions:

  • Bayesian estimation provides a viable alternative for MLCPA, especially in challenging scenarios.
  • Findings highlight variations in academic proficiency and inform educational policy.
  • The study offers new perspectives on academic patterns and interventions.