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Crowdsourcing prior information to improve study design and data analysis

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  • Jeffrey S Chrabaszcz
  • Joe W Tidwell
  • Michael R Dougherty

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.

Suggested Citation

  • Jeffrey S Chrabaszcz & Joe W Tidwell & Michael R Dougherty, 2017. "Crowdsourcing prior information to improve study design and data analysis," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    • Handle: RePEc:plo:pone00:0188246
    DOI: 10.1371/journal.pone.0188246
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    References listed on IDEAS

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    1. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
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