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Publication Biases in Replication Studies

Published online by Cambridge University Press:  16 November 2020

Adam J. Berinsky
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA, USA. Email: berinsky@mit.edu, teppei@mit.edu
James N. Druckman*
Affiliation:
Department of Political Science, Northwestern University, Evanston, IL, 60208. Email: druckman@northwestern.edu
Teppei Yamamoto
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA, USA. Email: berinsky@mit.edu, teppei@mit.edu
*
Corresponding author James N. Druckman

Abstract

One of the strongest findings across the sciences is that publication bias occurs. Of particular note is a “file drawer bias” where statistically significant results are privileged over nonsignificant results. Recognition of this bias, along with increased calls for “open science,” has led to an emphasis on replication studies. Yet, few have explored publication bias and its consequences in replication studies. We offer a model of the publication process involving an initial study and a replication. We use the model to describe three types of publication biases: (1) file drawer bias, (2) a “repeat study” bias against the publication of replication studies, and (3) a “gotcha bias” where replication results that run contrary to a prior study are more likely to be published. We estimate the model’s parameters with a vignette experiment conducted with political science professors teaching at Ph.D. granting institutions in the United States. We find evidence of all three types of bias, although those explicitly involving replication studies are notably smaller. This bodes well for the replication movement. That said, the aggregation of all of the biases increases the number of false positives in a literature. We conclude by discussing a path for future work on publication biases.

Type
Article
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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