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Area of Science:

  • Psychological research methods
  • Statistical modeling
  • Bayesian inference

Background:

  • Mediation analysis is crucial for understanding indirect effects in psychological research.
  • Traditional frequentist methods for mediation analysis may lack statistical power, especially with small sample sizes.
  • Bayesian approaches have been proposed to potentially enhance power in mediation analysis.

Purpose of the Study:

  • To compare the statistical power of Bayesian credibility intervals against frequentist confidence intervals for mediated effects.
  • To evaluate the influence of prior distribution precision on the power of Bayesian methods in mediation analysis.
  • To assess performance across different sample sizes (N≤200) and effect sizes.

Main Methods:

  • Simulation study comparing Bayesian credibility intervals with normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals.
  • Examination of Bayesian methods using both diffuse and informative prior distributions.
  • Analysis of varying degrees of precision in prior distributions.

Main Results:

  • Bayesian methods with diffuse priors showed power comparable to distribution of the product and bootstrap methods.
  • Bayesian methods utilizing informative priors demonstrated the highest statistical power.
  • Increased prior precision enhanced power under specific conditions related to sample size and effect magnitude.

Conclusions:

  • Bayesian credibility intervals, especially with informative priors, provide greater power for mediation analysis than common frequentist intervals.
  • The precision of prior distributions significantly impacts the power of Bayesian mediation analysis.
  • Bayesian methods offer a powerful alternative for mediation analysis, particularly valuable in psychological studies with limited sample sizes.