Spurious Precision in Meta-Analysis

Zuzana Irsova, Pedro Bom, Tomas Havranek and Heiko Rachinger

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

Abstract: Meta-analysis upweights studies reporting lower standard errors and hence more precision. 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.

Keywords: Publication bias; p-hacking; selection models; meta-regression; funnel plot; inverse-variance weighting (search for similar items in EconPapers)
JEL-codes: C15 C26 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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https://www.econstor.eu/bitstream/10419/268683/1/maive.pdf (application/pdf)

Related works:
Working Paper: Spurious Precision in Meta-Analysis (2023)
Working Paper: Spurious Precision in Meta-Analysis (2023)
Working Paper: Spurious Precision in Meta-Analysis (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:268683

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