Publication detail

Meta-Analyses of Partial Correlations Are Biased: Detection and Solutions

Author(s): prof. PhDr. Tomáš Havránek Ph.D., T. D. Stanley, Hristos Doucouliagos
Type: IES Working Papers
Year: 2023
Number: 17
Published in: IES Working Papers 17/2023
Publishing place: Praha
Keywords: partial correlation coefficients, meta-analysis, bias, small sample
JEL codes: C83
Suggested Citation: Stanley T.D., Doucouliagos H., Havranek T. (2023): " Meta-Analyses of Partial Correlations Are Biased: Detection and Solutions " IES Working Papers 17/2023. IES FSV. Charles University.
Abstract: We demonstrate that all meta-analyses of partial correlations are biased, and yet hundreds of meta-analyses of partial correlation coefficients (PCC) are conducted each year widely across economics, business, education, psychology, and medical research. To address these biases, we offer a new weighted average, UWLS+3. UWLS+3 is the unrestricted weighted least squares weighted average that makes an adjustment to the degrees of freedom that are used to calculate partial correlations and, by doing so, renders trivial any remaining meta-analysis bias. Our simulations also reveal that these meta-analysis biases are small-sample biases (n < 200), and a simple correction factor of (n-2)/(n-1) greatly reduces these small-sample biases. In many applications where primary studies typically have hundreds or more observations, partial correlations can be meta-analyzed in standard ways with only negligible bias. However, in other fields in the social and the medical sciences that are dominated by small samples, these meta-analysis biases are easily avoidable by our proposed methods.
Downloadable: wp_2023_17_stanley, doucouliagos, havranek


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