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Achieving Statistical Significance with Covariates and without Transparency

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  • Lenz, Gabriel

    (UC Berkeley)

  • Sahn, Alexander

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, Center for Open Science.
    • Handle: RePEc:osf:metaar:s42ba
    DOI: 10.31219/osf.io/s42ba
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    1. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
    2. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    3. Jonah B. Gelbach, 2016. "When Do Covariates Matter? And Which Ones, and How Much?," Journal of Labor Economics, University of Chicago Press, vol. 34(2), pages 509-543.
    4. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 40(4), pages 791-821.
    5. Katherine Casey & Rachel Glennerster & Edward Miguel, 2012. "Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(4), pages 1755-1812.
    6. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    7. Gerber, Alan & Malhotra, Neil, 2008. "Do Statistical Reporting Standards Affect What Is Published? Publication Bias in Two Leading Political Science Journals," Quarterly Journal of Political Science, now publishers, vol. 3(3), pages 313-326, October.
    8. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    9. Tiffany C. Davenport, 2015. "Policy‐Induced Risk and Responsive Participation: The Effect of a Son's Conscription Risk on the Voting Behavior of His Parents," American Journal of Political Science, John Wiley & Sons, vol. 59(1), pages 225-241, January.
    10. Jonas Peters & Peter Bühlmann & Nicolai Meinshausen, 2016. "Causal inference by using invariant prediction: identification and confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 947-1012, November.
    11. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    12. Wooldridge, Jeffrey M., 2016. "Should instrumental variables be used as matching variables?," Research in Economics, Elsevier, vol. 70(2), pages 232-237.
    13. Wyss Richard & Brookhart M. Alan & Stürmer Til & Lunt Mark & Glynn Robert J., 2014. "Reducing Bias Amplification in the Presence of Unmeasured Confounding through Out-of-Sample Estimation Strategies for the Disease Risk Score," Journal of Causal Inference, De Gruyter, vol. 2(2), pages 1-16, September.
    14. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
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    2. Kim-Lee Tuxhorn & John W. D'Attoma & Sven Steinmo, 2019. "Trust in institutions: Narrowing the ideological gap over the federal budget," Journal of Behavioral Public Administration, Center for Experimental and Behavioral Public Administration, vol. 2(1).

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