A kinked meta-regression model for publication bias correction

dc.contributor.authorBom, Pedro
dc.contributor.authorRachinger, Heiko
dc.date.accessioned2024-11-13T10:55:54Z
dc.date.available2024-11-13T10:55:54Z
dc.date.issued2019-12
dc.date.updated2024-11-13T10:55:54Z
dc.description.abstractPublication bias distorts the available empirical evidence and misinforms policymaking. Evidence of publication bias is mounting in virtually all fields of empirical research. This paper proposes the endogenous kink (EK) meta-regression model as a novel method of publication bias correction. The EK method fits a piecewise linear meta-regression of the primary estimates on their standard errors, with a kink at the cutoff value of the standard error below which publication selection is unlikely. We provide a simple method of endogenously determining this cutoff value as a function of a first-stage estimate of the true effect and an assumed threshold of statistical significance. Our Monte Carlo simulations show that EK is less biased and more efficient than other related regression-based methods of publication bias correction in a variety of research conditions.en
dc.identifier.citationBom, P. R. D., & Rachinger, H. (2019). A kinked meta-regression model for publication bias correction. Research Synthesis Methods, 10(4), 497-514. https://doi.org/10.1002/JRSM.1352
dc.identifier.doi10.1002/JRSM.1352
dc.identifier.issn1759-2887
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1817
dc.language.isoeng
dc.publisherJohn Wiley and Sons Ltd
dc.rights© 2019 John Wiley & Sons, Ltd.
dc.subject.otherMeta-analysis
dc.subject.otherMeta-regression
dc.subject.otherPublication bias
dc.subject.otherPublication selection
dc.titleA kinked meta-regression model for publication bias correctionen
dc.typejournal article
dcterms.accessRightsmetadata only access
oaire.citation.endPage514
oaire.citation.issue4
oaire.citation.startPage497
oaire.citation.titleResearch Synthesis Methods
oaire.citation.volume10
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