A Bayesian Framework for Publication Bias Mitigation Using Behavioral Process Models

• Activity Number: 230
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Joachim Vandekerckhove* and Maime Guan
• Companies: and UC Irvine
• Keywords: Bayesian ; publication bias ; meta-analysis ; Bayesian inference ; psychology

Abstract

The reliability of published research findings in psychology has been a topic of rising concern. Publication bias, or treating positive findings differently from negative findings, is a contributing factor to this "crisis of confidence," in that it likely inflates the number of false positive effects in the literature. We demonstrate a novel approach in which we postulate a set of plausible biasing processes, marginalize over this set, and obtain a mitigated effect size for an individual test or a series of test. The approach can be used to take into account the possibility of publication bias in one particular instance, but may also serve as a method for meta-analysis that accounts for the possibility of bias. Allowing for the possibility of publication bias leads to a more conservative interpretation of published studies as well as meta-analyses. We provide mathematical details of the method and example applications.