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Spurious Precision in Meta-Analysis

Author

Listed:
  • Zuzana Irsova

    (Charles University, Prague)

  • Pedro R. D. Bom

    (University of Deusto, Bilbao)

  • Tomas Havranek

    (Charles University, Prague & Centre for Economic Policy Research, London & Meta-Research Innovation Center, Stanford)

  • Heiko Rachinger

    (University of the Balearic Islands, Palma)

Abstract
Meta-analysis upweights studies reporting lower standard errors and hence more preci- sion. But in empirical practice, notably in observational research, precision is not given to the researcher. Precision must be estimated, and thus can be p-hacked to achieve statistical significance. Simulations show that a modest dose of spurious precision creates a formidable problem for inverse-variance weighting and bias-correction methods based on the funnel plot. Selection models fail to solve the problem, and the simple mean can beat sophisticated estimators. Cures to publication bias may become worse than the disease. We introduce an approach that surmounts spuriousness: the Meta-Analysis Instrumental Variable Estimator (MAIVE), which employs inverse sample size as an instrument for reported variance.

Suggested Citation

  • Zuzana Irsova & Pedro R. D. Bom & Tomas Havranek & Heiko Rachinger, 2023. "Spurious Precision in Meta-Analysis," Working Papers IES 2023/05, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Feb 2023.
    • Handle: RePEc:fau:wpaper:wp2023_05
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    File URL: https://ies.fsv.cuni.cz/en/spurious-precision-meta-analysis
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    3. Matej Opatrny & Tomas Havranek & Zuzana Irsova & Milan Scasny, 2023. "Publication Bias and Model Uncertainty in Measuring the Effect of Class Size on Achievement," Working Papers IES 2023/19, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2023.
    4. Irsova, Zuzana & Doucouliagos, Hristos & Havranek, Tomas & Stanley, T. D., 2023. "Meta-Analysis of Social Science Research: A Practitioner’s Guide," EconStor Preprints 273719, ZBW - Leibniz Information Centre for Economics.
    5. Ichiro Iwasaki & Evžen Kočenda, 2024. "Quest for the general effect size of finance on growth: a large meta-analysis of worldwide studies," Empirical Economics, Springer, vol. 66(6), pages 2659-2722, June.
    6. Marvin Schütt, 2024. "Wind Turbines and Property Values: A Meta-Regression Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(1), pages 1-43, January.
    7. Kroupova, Katerina & Havranek, Tomas & Irsova, Zuzana, 2024. "Student Employment and Education: A Meta-Analysis," Economics of Education Review, Elsevier, vol. 100(C).
    8. Tersoo David Iorngurum, 2023. "Method Versus Cross-Country Heterogeneity in the Exchange Rate Pass-Through," Working Papers IES 2023/16, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2023.
    9. Stanley, T. D. & Doucouliagos, Hristos & Havranek, Tomas, 2023. "Reducing the biases of the conventional meta-analysis of correlations," EconStor Preprints 280227, ZBW - Leibniz Information Centre for Economics.

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    More about this item

    Keywords

    Publication bias; p-hacking; selection models; meta-regression; fun- nel plot; inverse-variance weighting;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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