the Similarity of States: Using S to Compute Dyadic Interest Similarity
Kevin Sweeney and
Omar M.G. Keshk
Additional contact information
Kevin Sweeney: Department of Political Science Ohio State University Columbus, Ohio, USA
Omar M.G. Keshk: Department of Political Science Ohio State University Columbus, Ohio, USA, keshk.1@osu.edu
Conflict Management and Peace Science, 2005, vol. 22, issue 2, 165-187
Abstract:
Several leading international relations theories argue that the degree of interest similarity is an important determinant of dyadic conflict and cooperation. Empirical scholars have long wrestled with operationalizing and measuring this central, yet elusive, concept. Signorino and Ritter's (1999) S algorithm, combined with multiple data sources, provides an attractive solution to this problem. To date, however, many scholars have failed to take full advantage of this solution. In this research note we examine the properties of S via simulation and with real data sources, highlighting its virtues and potential limitations. In particular, we stress the need to include multiple data sources in the computation and provide scholars with an easy-to-use tool to greatly simplify this task.
Keywords: interstate dyads; interest similarity; preference similarity; Scompute (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
https://journals.sagepub.com/doi/10.1080/07388940590948583 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:compsc:v:22:y:2005:i:2:p:165-187
DOI: 10.1080/07388940590948583
Access Statistics for this article
More articles in Conflict Management and Peace Science from Peace Science Society (International)
Bibliographic data for series maintained by SAGE Publications ().