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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Detecting Conditional Dependence Using Flexible Bayesian Latent Class Analysis.

Jaehoon Lee¹, Kwanghee Jung¹, Jungkyu Park²

¹Department of Educational Psychology and Leadershi, Texas Tech University, Lubbock, TX, United States.

Frontiers in Psychology

|September 9, 2020

Summary

This summary is machine-generated.

Bayesian latent class analysis (LCA) can detect conditional dependence among indicators. Using weakly informative priors with large variance improves model fit, even when indicators are dependent.

Keywords:

Bayesian latent class analysis approximate independence conditional dependence model fit prior variance

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Area of Science:

Statistics
Psychometrics
Data Analysis

Background:

Latent Class Analysis (LCA) assumes conditional independence of indicators given latent class membership.
Violations of this assumption can impact LCA results.
Bayesian LCA offers a flexible approach to model indicator dependencies.

Purpose of the Study:

Investigate the impact of prior variances on Bayesian LCA performance.
Assess the utility of priors for detecting conditional dependence (Type I error and power).
Examine the effect of approximate independence on Bayesian LCA model fit.

Main Methods:

Monte Carlo simulation study.
Varied prior variances from non-informative to informative.
Evaluated Type I error, power, and posterior predictive p-values for model fit.

Main Results:

Weakly informative priors with large variances demonstrated satisfactory model fit.
Model fit remained adequate regardless of indicator independence or dependence.
The choice of prior significantly influences the detection of conditional dependence.

Conclusions:

Recommends using weakly informative priors with large variances in Bayesian LCA.
Provides methodological guidelines for applied researchers using Bayesian LCA.
Highlights the importance of considering prior specifications for accurate analysis.