Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/268683 
Erscheinungsjahr: 
2023
Verlag: 
ZBW – Leibniz Information Centre for Economics, Kiel, Hamburg
Zusammenfassung: 
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.
Schlagwörter: 
Publication bias
p-hacking
selection models
meta-regression
funnel plot
inverse-variance weighting
JEL: 
C15
C26
C83
Dokumentart: 
Working Paper

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