Journal ArticleDOI
The Power of Bias in Economics Research
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The authors survey 159 empirical economics literatures that draw upon 64,076 estimates of economic parameters reported in more than 6,700 empirical studies to investigate two critical dimensions of the credibility of empirical economics research: statistical power and bias.Abstract:
We investigate two critical dimensions of the credibility of empirical economics research: statistical power and bias. We survey 159 empirical economics literatures that draw upon 64,076 estimates of economic parameters reported in more than 6,700 empirical studies. Half of the research areas have nearly 90% of their results under-powered. The median statistical power is 18%, or less. A simple weighted average of those reported results that are adequately powered (power ≥ 80%) reveals that nearly 80% of the reported effects in these empirical economics literatures are exaggerated; typically, by a factor of two and with one-third inflated by a factor of four or more.read more
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Meta-Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection
TL;DR: This study investigates the small‐sample performance of meta‐regression methods for detecting and estimating genuine empirical effects in research literatures tainted by publication selection and finds them to be robust against publication selection.
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Correcting for Bias in Psychology: A Comparison of Meta-Analytic Methods:
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What meta-analyses reveal about the replicability of psychological research.
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Neither fixed nor random: weighted least squares meta-regression.
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Aid, China, and Growth: Evidence from a New Global Development Finance Dataset
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References
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Journal ArticleDOI
Bias in meta-analysis detected by a simple, graphical test
TL;DR: Funnel plots, plots of the trials' effect estimates against sample size, are skewed and asymmetrical in the presence of publication bias and other biases Funnel plot asymmetry, measured by regression analysis, predicts discordance of results when meta-analyses are compared with single large trials.
Book
Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.
Book
Statistical Methods for Meta-Analysis
Larry V. Hedges,Ingram Olkin +1 more
TL;DR: In this article, the authors present a model for estimating the effect size from a series of experiments using a fixed effect model and a general linear model, and combine these two models to estimate the effect magnitude.
Journal ArticleDOI
The file drawer problem and tolerance for null results
TL;DR: Quantitative procedures for computing the tolerance for filed and future null results are reported and illustrated, and the implications are discussed.
Journal ArticleDOI
Power failure: why small sample size undermines the reliability of neuroscience
Katherine S. Button,John P. A. Ioannidis,Claire Mokrysz,Brian A. Nosek,Jonathan Flint,Emma S J Robinson,Marcus R. Munafò +6 more
TL;DR: It is shown that the average statistical power of studies in the neurosciences is very low, and the consequences include overestimates of effect size and low reproducibility of results.
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