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Assessing Bias from the (Mis)Use of Covariates: A Meta-Analysis


Lenz and Sahn examine how often research findings depend on suppression effects, or covariate-induced increases in effect sizes. Researchers generally scrutinize suppression effects as they want reassurance that researchers have a strong explanation for effect size increases, especially when the statistical significance of the key finding depends on them.

They find that 30-40% of observational articles in a leading journal depend on suppression effects for statistical significance. Although suppression effects are of course potentially justifiable — to address suppressor variables — none of the articles justifies or discloses them. These findings may point to a hole in the review process: journals are accepting articles that depend on suppression effects without readers, reviewers, or editors being made aware.

Publications associated with this project:

  • Lenz, Gabriel S., and Alexander Sahn. “Achieving Statistical Significance with Control Variables and Without Transparency.” Political Analysis, (November 2020) ed, 1–14.
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