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Summary
This summary is machine-generated.

This study introduces the SSDbain R package for calculating sample sizes in Bayesian ANOVA. It ensures sufficient statistical power for hypothesis testing using Bayes factors, aiding researchers in planning studies.

Keywords:
Bayes factorBayesian ANOVAsSSDbaininformative hypothesissample size

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Area of Science:

  • Statistics
  • Social and Behavioral Sciences

Background:

  • Researchers often test hypotheses about group means in ANOVA models.
  • Equality and order constrained hypotheses allow for specific expectations.
  • Bayesian approaches offer alternatives to traditional ANOVA.

Purpose of the Study:

  • To introduce the R package SSDbain for sample size calculation in Bayesian ANOVA.
  • To provide a method for determining sample size based on Bayes factors and hypothesis testing.
  • To facilitate sample size planning for researchers using Bayesian ANOVA models.

Main Methods:

  • The study utilizes the Approximate Adjusted Fractional Bayes Factor (AAFBF).
  • Sample size is determined to achieve a desired probability of a large Bayes factor.
  • The R package SSDbain implements calculations for Bayesian ANOVA, Bayesian Welch's ANOVA, and Bayesian robust ANOVA.

Main Results:

  • The SSDbain package enables sample size calculation for various Bayesian ANOVA types.
  • The methodology ensures adequate power for detecting effects under specified hypotheses.
  • Tables are provided to assist researchers in sample size determination.

Conclusions:

  • The SSDbain R package simplifies sample size planning for Bayesian ANOVA.
  • Researchers can confidently plan studies using Bayesian hypothesis testing with this tool.
  • This facilitates more rigorous research in social and behavioral sciences through informed sample size selection.