EconPapers    
Economics at your fingertips  
 

Achieving Statistical Significance with Covariates and without Transparency

Gabriel Lenz and Alexander Sahn
Additional contact information
Gabriel Lenz: UC Berkeley

No s42ba, MetaArXiv from Center for Open Science

Abstract: How often do articles depend on suppression effects for their findings? How often do they disclose this fact? By suppression effects, we mean 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. In this article, we 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.

Date: 2017-04-07
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://osf.io/download/58e82561594d9002510ab37f/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:metaar:s42ba

DOI: 10.31219/osf.io/s42ba

Access Statistics for this paper

More papers in MetaArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().

 
Page updated 2020-01-17
Handle: RePEc:osf:metaar:s42ba