Detecting Selection Bias in Meta-Analyses with Multiple Outcomes: A Simulation Study

Autor/inn/en	Fernández-Castilla, Belén; Declercq, Lies; Jamshidi, Laleh; Beretvas, S. Natasha; Onghena, Patrick; Van den Noortgate, Wim
Titel	Detecting Selection Bias in Meta-Analyses with Multiple Outcomes: A Simulation Study
Quelle	In: Journal of Experimental Education, 89 (2021) 1, S.125-144 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2019.1582470
Schlagwörter	Statistical Bias; Meta Analysis; Publications; Regression (Statistics); Error of Measurement; Effect Size; Simulation + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Regression; Regressionsanalyse; Messfehler; Simulation program; Simulationsprogramm
Abstract	This study explores the performance of classical methods for detecting publication bias--namely, Egger's regression test, Funnel Plot test, Begg's Rank Correlation and Trim and Fill method--in meta-analysis of studies that report multiple effects. Publication bias, outcome reporting bias, and a combination of these were generated. Egger's regression test and the Funnel Plot test were extended to three-level models, and possible cutoffs for the estimator of the Trim and Fill method were explored. Furthermore, we checked whether the combination of results of several methods yielded a better control of Type I error rates. Results show that no method works well across all conditions and that performance depends mainly on the population effect size value and the total variance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01