What is it about?

Publication bias - the preferential publishing of studies that show the predicted effect - leads to an overestimation of effect sizes in research. We developed statistical techniques that correct for this bias, in order to estimate the effect size as it would manifest itself if all experiments were equally likely to be published.

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Why is it important?

Several methods already exist to adjust for publication bias. Our method applies all of them at the same time and weights them by how well they describe the data (Bayesian model-averaging). Through simulation studies and applied examples, we show that robust Bayesian meta-analysis outperforms the alternatives in recovering the effect corrected for publication bias.

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This page is a summary of: Robust Bayesian meta-analysis: Addressing publication bias with model-averaging., Psychological Methods, May 2022, American Psychological Association (APA), DOI: 10.1037/met0000405.
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