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Spurious Precision in Meta-Analysis

Zuzana Irsova, Pedro Bom, Tomas Havranek and Heiko Rachinger
Authors registered in the RePEc Author Service: Lorena Skufi, Adam Gersl and Meri Papavangjeli

No 2023/05, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

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.

Keywords: Publication bias; p-hacking; selection models; meta-regression; fun- nel plot; inverse-variance weighting (search for similar items in EconPapers)
JEL-codes: C15 C26 C83 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2023-02, Revised 2023-02
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (6)

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