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Achieving Statistical Significance with Control Variables and Without Transparency

Published online by Cambridge University Press:  16 November 2020

Gabriel S. Lenz Open the ORCID record for Gabriel S. Lenz [Opens in a new window]  and
Alexander Sahn
Gabriel S. Lenz*
Affiliation:
Travers Department of Political Science, University of California, Berkeley, CA94720-1950, USA. Email: glenz@berkeley.edu, asahn@berkeley.edu
Alexander Sahn
Affiliation:
Travers Department of Political Science, University of California, Berkeley, CA94720-1950, USA. Email: glenz@berkeley.edu, asahn@berkeley.edu
*
Corresponding author Gabriel S. Lenz
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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 authors have a strong explanation for them, especially when the statistical significance of the key finding depends on them. In a reanalysis 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.

Keywords

reproducibilitytransparencyopen sciencecovariatescontrolssuppression effectsregression
Type
Article
Information
Political Analysis , Volume 29 , Issue 3 , July 2021 , pp. 356 - 369
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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https://doi.org/10.7910/DVN/XIEJCR
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