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Achieving Statistical Significance with Covariates and without Transparency

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  • Lenz, Gabriel

    (UC Berkeley)

  • Sahn, Alexander

    (University of North Carolina, Chapel Hill)

Abstract

How often do articles depend on suppression effects for their findings? How often do they disclose this fact? By suppression effects, we mean control-variable-induced increases in estimated effect sizes. Researchers generally scrutinize suppression effects as they want reassurance that researchers have a strong explanation for them, especially when the statistical significance of the key finding depends on them. In a re-analysis of observational studies from a leading journal, we find that over 30% of articles depend on suppression effects for statistical significance. Although increases in key effect estimates from including control variables are of course potentially justifiable, none of the articles justify or disclose 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.

Suggested Citation

  • Lenz, Gabriel & Sahn, Alexander, 2017. "Achieving Statistical Significance with Covariates and without Transparency," MetaArXiv s42ba_v1, Center for Open Science.
    • Handle: RePEc:osf:metaar:s42ba_v1
    DOI: 10.31219/osf.io/s42ba_v1
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