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Crowdsourcing prior information to improve study design and data analysis.

Jeffrey S Chrabaszcz1, Joe W Tidwell2, Michael R Dougherty2

  • 1Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, United States of America.

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We present a novel method for setting Bayesian priors using continuous probability distributions elicited from participants. This approach incorporates participant knowledge to improve effect size estimation and reduce overestimations, even with near-zero means.

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Area of Science:

  • Statistics
  • Cognitive Science
  • Psychology

Background:

  • Bayesian methods are increasingly utilized in research.
  • Challenges persist in establishing appropriate priors for novel measures or environments.
  • Existing methods may not fully leverage available information.

Purpose of the Study:

  • To propose and validate a new method for setting Bayesian priors.
  • To incorporate information from naive participants into prior distributions.
  • To improve the estimation of effect sizes and reduce overestimation.

Main Methods:

  • Eliciting continuous probability distributions from naive participants.
  • Using these distributions to set priors in Bayesian analyses.
  • Applying the method to published studies and comparing aggregation techniques.

Main Results:

  • The proposed method effectively incorporates participant knowledge.
  • It provides a principled way to estimate prior dispersion.
  • This leads to shrinkage and reduces overestimated effect sizes, even with near-zero means.

Conclusions:

  • Eliciting priors from participants offers a practical solution for novel Bayesian applications.
  • This method enhances the reliability of effect size estimates.
  • It supports the broader adoption of Bayesian approaches in diverse research contexts.