Testing for Funnel Plot Asymmetry of Standardized Mean Differences

Autor/inn/en	Pustejovsky, James E.; Rodgers, Melissa A.
Titel	Testing for Funnel Plot Asymmetry of Standardized Mean Differences
Quelle	In: Research Synthesis Methods, 10 (2019) 1, S.57-71 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Pustejovsky, James E.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1332
Schlagwörter	Effect Size; Regression (Statistics); Statistical Analysis; Error of Measurement; Meta Analysis; Correlation + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Statistische Analyse; Messfehler; Meta-analysis; Metaanalyse; Korrelation
Abstract	Publication bias and other forms of outcome reporting bias are critical threats to the validity of findings from research syntheses. A variety of methods have been proposed for detecting selective outcome reporting in a collection of effect size estimates, including several methods based on assessment of asymmetry of funnel plots, such as the Egger's regression test, the rank correlation test, and the Trim-and-Fill test. Previous research has demonstated that the Egger's regression test is miscalibrated when applied to log-odds ratio effect size estimates, because of artifactual correlation between the effect size estimate and its standard error. This study examines similar problems that occur in meta-analyses of the standardized mean difference, a ubiquitous effect size measure in educational and psychological research. In a simulation study of standardized mean difference effect sizes, we assess the Type I error rates of conventional tests of funnel plot asymmetry, as well as the likelihood ratio test from a three-parameter selection model. Results demonstrate that the conventional tests have inflated Type I error due to the correlation between the effect size estimate and its standard error, while tests based on either a simple modification to the conventional standard error formula or a variance-stabilizing transformation both maintain close-to-nominal Type I error. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01