Crowdsourcing prior information to improve study design and data analysis

Jeffrey S Chrabaszcz, Joe W Tidwell and Michael R Dougherty

PLOS ONE, 2017, vol. 12, issue 11, 1-16

Abstract: Though Bayesian methods are being used more frequently, many still struggle with the best method for setting priors with novel measures or task environments. We propose a method for setting priors by eliciting continuous probability distributions from naive participants. This allows us to include any relevant information participants have for a given effect. Even when prior means are near-zero, this method provides a principle way to estimate dispersion and produce shrinkage, reducing the occurrence of overestimated effect sizes. We demonstrate this method with a number of published studies and compare the effect of different prior estimation and aggregation methods.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0188246

DOI: 10.1371/journal.pone.0188246

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