Identification of and correction for publication bias
Isaiah Andrews and
Maximilian Kasy ()
Papers from arXiv.org
Some empirical results are more likely to be published than others. Such selective publication leads to biased estimates and distorted inference. This paper proposes two approaches for identifying the conditional probability of publication as a function of a study's results, the first based on systematic replication studies and the second based on meta-studies. For known conditional publication probabilities, we propose median-unbiased estimators and associated confidence sets that correct for selective publication. We apply our methods to recent large-scale replication studies in experimental economics and psychology, and to meta-studies of the effects of minimum wages and de-worming programs.
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Journal Article: Identification of and Correction for Publication Bias (2019)
Working Paper: Identification of and correction for publication bias (2018)
Working Paper: Identification of and Correction for Publication Bias (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1711.10527
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