Spurious Precision in Meta-Analysis

Zuzana Irsova, Pedro Ricardo Duarte Bom, Tomas Havranek and Heiko Rachinger

No 3qp2w, MetaArXiv from Center for Open Science

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.

Date: 2023-02-23
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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https://osf.io/download/63eddb12eeb8ff06af211bac/

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:osf:metaar:3qp2w

DOI: 10.31219/osf.io/3qp2w

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