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(Mis)Using Dyadic Data to Analyze Multilateral Events

Published online by Cambridge University Press:  04 January 2017

Paul Poast
Paul Poast*
Affiliation:
Department of Political Science, University of Michigan, 5700 Haven Hall, Ann Arbor, MI 48109. e-mail: [email protected]
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Abstract

Dyadic (state-pair) data is completely inappropriate for analyzing multilateral events (such as large alliances and major wars). Scholars, particularly in international relations, often divide the actors in a multilateral event into a series of dyadic relations. Though this practice can dramatically increase the size of data sets, using dyadic data to analyze what are, in reality, k-adic events leads to model misspecification and, inevitably, statistical bias. In short, one cannot recover a k-adic data generating process using dy-adic data. In this paper, I accomplish three tasks. First, I use Monte Carlo simulations to confirm that analyzing k-adic events with dyadic data produces substantial bias. Second, I show that choice-based sampling, as popularized by King and Zeng (2001a, Explaining rare events in international relations. International Organization 55:693–715, and 2001b, Logistic regression in rare events data. Political Analysis 9:137–63), can be used to create feasibly sized k-adic data sets. Finally, I use the study of alliance formation by Gibler and Wolford (2006, Alliances, then democracy: An examination of the relationship between regime type and alliance formation. The Journal of Conflict Resolution 50:1–25) to illustrate how to apply this choice-based sampling solution and explain how to code independent variables in a k-adic context.

Type
Research Article
Information
Political Analysis , Volume 18 , Issue 4 , Autumn 2010 , pp. 403 - 425
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

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