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

Author

Listed:
  • T. D. Stanley

    (Department of Economics, Deakin University, Burwood, Victoria, Australia.)

  • Hristos Doucouliagos

    (Department of Economics, Deakin University, Burwood, Victoria, Australia.)

  • Tomas Havranek

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic & Centre for Economic Policy Research, London)

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

Suggested Citation

  • T. D. Stanley & Hristos Doucouliagos & Tomas Havranek, 2023. "Meta-Analyses of Partial Correlations Are Biased: Detection and Solutions," Working Papers IES 2023/17, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2023.
    • Handle: RePEc:fau:wpaper:wp2023_17
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    Cited by:

    1. Karina Zhemelko & Kristina Kocisova, 2026. "How Effective is Monetary Policy Transmission? A Meta-analysis on Interest Rate Pass-Through and Speed of Adjustment," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 68(1), pages 1-59, March.
    2. Zuzana Irsova & Hristos Doucouliagos & Tomas Havranek & T. D. Stanley, 2024. "Meta‐analysis of social science research: A practitioner's guide," Journal of Economic Surveys, Wiley Blackwell, vol. 38(5), pages 1547-1566, December.
    3. Maria Mitroulia & Evangelos Chytis & Thomas Kitsantas & Michalis Skordoulis & Petros Kalantonis, 2025. "ESG Strategy and Tax Avoidance: Insights from a Meta-Regression Analysis," JRFM, MDPI, vol. 18(9), pages 1-34, September.
    4. Brodeur, Abel & Kattan, Lamis & Musumeci, Marco, 2026. "Job market stars: Statistical significance and academic hiring in economics," European Economic Review, Elsevier, vol. 187(C).
    5. Andreas Sintos & Michael Chletsos & Eleni Xydea, 2026. "Revisiting the Health Spending‐Growth Nexus," Journal of Economic Surveys, Wiley Blackwell, vol. 40(4), pages 2037-2055, September.
    6. Tipaldi, Riccardo & Gallucci, Carmen & Liñares-Zegarra, José M., 2026. "Herding behaviour in equity crowdfunding and P2P lending markets: A systematic meta-analysis," International Review of Financial Analysis, Elsevier, vol. 111(C).
    7. Florian Schneider, 2026. "Do robots boost productivity? A quantitative meta‐study," Journal of Economic Surveys, Wiley Blackwell, vol. 40(3), pages 1531-1571, July.
    8. Jacobo Núñez & David Martín‐Barroso & Juan A. Núñez‐Serrano & Francisco J. Velázquez, 2025. "How much are we willing to pay for quality wine? A meta‐analysis and meta‐regression analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 39(4), pages 1595-1629, September.
    9. Sintos, Andreas & Chletsos, Michael & Xydea, Eleni, 2026. "Revisiting the health spending‐growth nexus," LSE Research Online Documents on Economics 137717, London School of Economics and Political Science, LSE Library.
    10. Samuel Fiifi Eshun & Evzen Kocenda, 2025. "What Really Drives Financial Inclusion? Evidence from a Meta-Analysis of 3,817 Estimates," Working Papers IES 2025/14, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2025.
    11. Yanxia Yu & Chenfei Qu & Tom Coupé & Mathilda Featherston-Lardeux & Andreas Loeschel & Arne R. Weiss & Da Zhang, 2025. "Did China’s Pilot Emissions Trading Scheme Reduce CO2 Emissions? Evidence from a Meta-Analysis," Working Papers in Economics 25/04, University of Canterbury, Department of Economics and Finance.

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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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