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Dyadic Analysis in International Relations: A Cautionary Tale
Published online by Cambridge University Press: 04 January 2017
International relations scholars frequently rely on data sets with country pairs, or dyads, as the unit of analysis. Dyadic data, with its thousands and sometimes hundreds of thousands of observations, may seem ideal for hypothesis testing. However, dyadic observations are not independent events. Failure to account for this dependence in the data dramatically understates the size of standard errors and overstates the power of hypothesis tests. We illustrate this problem by analyzing a central proposition among IR scholars, the democratic trade hypothesis, which claims that democracies seek out other democracies as trading partners. We employ randomization tests to infer the correct p-values associated with the trade hypotheses. Our results show that typical statistical tests for significance are severely overconfident when applied to dyadic data.
- Research Article
- Copyright © The Author 2014. Published by Oxford University Press on behalf of the Society for Political Methodology
Authors' note: For comments on earlier drafts we are thankful to Donald Green, Soo Yeon Kim, Jeff Lax, Mark Manger, Yotam Margalit, Ken Scheve, Vera Troeger, Robert Walker, and participants at various academic presentations. Boliang Zhu provided excellent research assistance. Replication materials for this article are available from the Political Analysis dataverse at http://dx.doi.org/10.7910/DVN/23510UNF:5:YY9Ujh8lYTjBOtSWvyRtmw== IQSS Dataverse Network [Distributor] V1 [Version]. Supplementary materials for this article are available on the Political Analysis Web site.