03-14-2011

hdl:1902.1/15649

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 datasets, 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 and 2001b), can be used to create feasibly sized k-adic datasets. Finally, I use the study of alliance formation by Gibler and Wolford (2006) to illustrate how to apply this choice-based sampling solution and explain how to code independent variables in a k-adic context.

Paul Poast, 2011, "Replication data for: (Mis)Using Dyadic Data to Analyze Multilateral Events", http://hdl.handle.net/1902.1/15649, Harvard Dataverse, V1

Paul Poast

Harvard University

SCR:011273

DataVerse

Dataverse Network Project

SCR:001997