Adjusting for Publication Bias in JASP — Selection Models and Robust Bayesian Meta-Analysis

@inproceedings{Barto2020AdjustingFP,
  title={Adjusting for Publication Bias in JASP — Selection Models and Robust Bayesian Meta-Analysis},
  author={Franti{\vs}ek Barto{\vs} and Maximilian A. Maier and Eric-Jan Wagenmakers},
  year={2020}
}
Meta-analysis is essential for cumulative science, but its validity is compromised by publication bias. In order to mitigate the impact of publication bias, one may apply selection models, which estimate the degree to which non-significant studies are suppressed. Implemented in JASP, these methods allow researchers without programming experience to conduct state-of-the-art publication bias adjusted meta-analysis. In this tutorial, we demonstrate how to conduct a publication bias adjusted meta… 

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