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Default priors in Bayesian structural equation modeling (BSEM) can significantly impact results, especially with small samples. A prior sensitivity analysis is crucial for reliable BSEM research.

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Area of Science:

  • Statistics
  • Psychometrics

Background:

  • Bayesian structural equation modeling (BSEM) offers advantages over classical methods for complex models.
  • Default priors are often used in BSEM but can heavily influence parameter estimates.

Purpose of the Study:

  • Investigate the performance of three default prior types in BSEM: noninformative improper, vague proper, and empirical Bayes priors.
  • Highlight the necessity of prior sensitivity analysis in default BSEM.

Main Methods:

  • A simulation study was conducted to compare the performance of different default priors.
  • A practical guide for performing prior sensitivity analysis in BSEM was developed.

Main Results:

  • The performance of default BSEM methods varied significantly, particularly with small sample sizes.
  • Empirical Bayes priors were introduced as a novel approach in the BSEM literature.

Conclusions:

  • Default priors can substantially affect BSEM parameter estimation, necessitating careful evaluation.
  • A prior sensitivity analysis is essential for robust and reliable results in default BSEM applications.