Meta-Analyzing Partial Correlation Coefficients Using Fisher's Z Transformation

Autor/in	van Aert, Robbie C. M.
Titel	Meta-Analyzing Partial Correlation Coefficients Using Fisher's Z Transformation
Quelle	In: Research Synthesis Methods, 14 (2023) 5, S.768-773 (6 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (van Aert, Robbie C. M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1654
Schlagwörter	Correlation; Meta Analysis; Sampling; Simulation; Error of Measurement; Robustness (Statistics); Evaluation Methods + Suchen Sie Ihr Suchwort? Korrelation; Meta-analysis; Metaanalyse; Simulation program; Simulationsprogramm; Messfehler; Widerstandsfähigkeit
Abstract	The partial correlation coefficient (PCC) is used to quantify the linear relationship between two variables while taking into account/controlling for other variables. Researchers frequently synthesize PCCs in a meta-analysis, but two of the assumptions of the common equal-effect and random-effects meta-analysis model are by definition violated. First, the sampling variance of the PCC cannot assumed to be known, because the sampling variance is a function of the PCC. Second, the sampling distribution of each primary study's PCC is not normal since PCCs are bounded between -1 and 1. I advocate applying the Fisher's z transformation analogous to applying Fisher's z transformation for Pearson correlation coefficients, because the Fisher's z transformed PCC is independent of the sampling variance and its sampling distribution more closely follows a normal distribution. Reproducing a simulation study by Stanley and Doucouliagos and adding meta-analyses based on Fisher's z transformed PCCs shows that the meta-analysis based on Fisher's z transformed PCCs had lower bias and root mean square error than meta-analyzing PCCs. Hence, meta-analyzing Fisher's z transformed PCCs is a viable alternative to meta-analyzing PCCs, and I recommend to accompany any meta-analysis based on PCCs with one using Fisher's z transformed PCCs to assess the robustness of the results. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01