title: A Bayesian approach to mitigation of publication bias.
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 Bayesian model averaging approach that takes into account the possibility of publication bias and allows for a better estimate of true underlying effect size. Accounting for the possibility of bias leads to a more conservative interpretation of published studies as well as meta-analyses. We provide mathematical details of the method and examples.
authors.affiliation: University of California, Irvine, CA, USA.
authors.forename: Maime
authors.initials: M
authors.lastname: Guan
authors.affiliation: University of California, Irvine, CA, USA. joachim@uci.edu.
authors.forename: Joachim
authors.initials: J
authors.lastname: Vandekerckhove
mesh_terms: D001499:Bayes Theorem; D035843:Biomedical Research / Q000592:standards*; D003627:Data Interpretation, Statistical; D006801:Humans; D017594:Publication Bias; D012107:Research Design / Q000592:standards*
keywords: Bayesian inference and parameter estimation; Bayesian statistics; Math modeling and model selection; Meta-analysis