A Kinked Meta-Regression Model for Publication Bias Correction

Autor/inn/en	Bom, Pedro R. D.; Rachinger, Heiko
Titel	A Kinked Meta-Regression Model for Publication Bias Correction
Quelle	In: Research Synthesis Methods, 10 (2019) 4, S.497-514 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bom, Pedro R. D.) ORCID (Rachinger, Heiko)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1352
Schlagwörter	Bias; Publications; Models; Regression (Statistics); Meta Analysis; Research Problems; Error Correction; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Analogiemodell; Regression; Regressionsanalyse; Meta-analysis; Metaanalyse; Forschungskritik; Korrektur; Monte-Carlo-Methode
Abstract	Publication 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01