Nonparametric Combination (NPC): A Framework for Testing Elaborate Theories
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NPC170223.pdf
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Author(s)
Caughey, Devin
Dafoe, Allan
Seawright, Jason
Date Issued
April 2017
Journal
The Journal of Politics
Publisher
University of Chicago Press
Citation
Caughey, Devin, Allan Dafoe, and Jason Seawright. “Nonparametric Combination (NPC): A Framework for Testing Elaborate Theories.” The Journal of Politics 79, no. 2 (April 2017): 688–701.
Version
Author's final manuscript
Abstract
Social scientists are commonly advised to deduce and test all observable implications of their theories. We describe a principled framework for testing such “elaborate” theories: nonparametric combination. Nonparametric combination (NPC) assesses the joint probability of observing the theoretically predicted pattern of results under the sharp null of no effects. NPC accounts for the dependence among the component tests without relying on modeling assumptions or asymptotic approximations. Multiple-testing corrections are also easily implemented with NPC. As we demonstrate with four applications, NPC leverages theoretical knowledge into greater statistical power, which is particularly valuable for studies with strong research designs but small sample sizes. We implement these methods in a new R package, NPC.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1086/689287
