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Detecting mediation effects with the Bayes factor: Performance evaluation and tools for sample size determination.

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Bayes factors (BFs) can test mediation effects in social science. Prior specification impacts BF accuracy for detecting mediation, informing sample size determination tools.

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Area of Science:

  • Social Science Research
  • Statistical Modeling
  • Psychological Research Methods

Background:

  • Mediation effect testing is crucial in social science.
  • Bayesian hypothesis testing using Bayes factors (BFs) is gaining traction.
  • BF application for mediation effects remains under-explored.

Purpose of the Study:

  • Systematically evaluate the performance of Bayes factors for testing mediation.
  • Investigate the influence of prior specifications on mediation detection rates.
  • Develop tools for sample size determination in Bayesian mediation analysis.

Main Methods:

  • Simulation studies to assess Bayes factor performance.
  • Examination of prior odds impact on false and true positive rates.
  • Development of an R function and web application for sample size calculation.

Main Results:

  • Prior specification significantly affects the accuracy of Bayes factors in detecting mediation.
  • False and true positive rates are sensitive to prior odds of treatment-mediator and mediator-outcome paths.
  • The developed tools aid in determining appropriate sample sizes for mediation studies.

Conclusions:

  • Bayes factors offer a valuable tool for mediation effect testing in social sciences.
  • Understanding prior specification is essential for accurate Bayesian mediation analysis.
  • New resources facilitate robust sample size planning for mediation research.