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Fishing, Commitment, and Communication: A Proposal for Comprehensive Nonbinding Research Registration

  • Macartan Humphreys (a1), Raul Sanchez de la Sierra (a2) and Peter van der Windt (a3)


Social scientists generally enjoy substantial latitude in selecting measures and models for hypothesis testing. Coupled with publication and related biases, this latitude raises the concern that researchers may intentionally or unintentionally select models that yield positive findings, leading to an unreliable body of published research. To combat this “fishing” problem in medical studies, leading journals now require preregistration of designs that emphasize the prior identification of dependent and independent variables. However, we demonstrate here that even with this level of advanced specification, the scope for fishing is considerable when there is latitude over selection of covariates, subgroups, and other elements of an analysis plan. These concerns could be addressed through the use of a form of comprehensive registration. We experiment with such an approach in the context of an ongoing field experiment for which we drafted a complete “mock report” of findings using fake data on treatment assignment. We describe the advantages and disadvantages of this form of registration and propose that a comprehensive but nonbinding approach be adopted as a first step to combat fishing by social scientists. Likely effects of comprehensive but nonbinding registration are discussed, the principal advantage being communication rather than commitment, in particular that it generates a clear distinction between exploratory analyses and genuine tests.


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Authors' note: Our thanks to Ali Cirone, Andy Gelman, Grant Gordon, Alan Jacobs, Ryan Moore, and Ferran Elias Moreno for helpful comments. Our thanks to the Population Center at Columbia for providing access to the High Performance Computing (HPC) Cluster. This research was undertaken in the context of a field experiment in DRC; we thank the International Rescue Committee and CARE International for their partnership in that research and the International Initiative for Impact Evaluation (3IE) for their support. M. H. thanks the Trudeau Foundation for support while this work was undertaken. Replication data and code for tables and figures can be found at Supplementary materials for this article are available on the Political Analysis Web site.



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