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Default Bayes Factors for Model Selection in Regression.

Jeffrey N Rouder1, Richard D Morey2

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This summary is machine-generated.

This study introduces a Bayes factor approach for multiple regression analysis, offering a way to find positive evidence for the absence of an effect. A new web applet provides guidance and software for using these default Bayes factors.

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Area of Science:

  • Statistics
  • Psychological Science
  • Computational Statistics

Background:

  • Bayes factors offer principled evidence for statistical models, including null hypotheses.
  • Conventional significance testing cannot provide positive evidence for the absence of an effect.
  • Lack of guidance and software hinders Bayes factor adoption in psychological science.

Purpose of the Study:

  • To present a Bayes factor solution for inference in multiple regression.
  • To address the need for accessible tools and guidance for Bayes factor application.
  • To facilitate the use of default Bayes factors in psychological research.

Main Methods:

  • Utilizing default Bayes factors for multiple regression designs as developed by Liang et al. (2008).
  • Developing a web applet for convenient computation of Bayes factors.
  • Providing guidance and context for the application of these priors.

Main Results:

  • The study presents a computationally attractive method for Bayes factor calculation in multiple regression.
  • A web applet is introduced for user-friendly computation and guidance.
  • The article discusses the interpretation and benefits of the proposed Bayes factor measures.

Conclusions:

  • The Bayes factor approach offers a valuable alternative for statistical inference in multiple regression.
  • The developed tools and guidance can promote the use of Bayes factors in psychological science.
  • This method allows for positive evidence of no effect, enhancing statistical interpretation.