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Choice or Circumstance? Adjusting Measures of Foreign Policy Similarity for Chance Agreement

Published online by Cambridge University Press:  04 January 2017

Frank M. Häge*
Affiliation:
Department of Politics and Public Administration, University of Limerick, Limerick, Ireland. e-mail: [email protected]

Abstract

The similarity of states' foreign policy positions is a standard variable in the dyadic analysis of international relations. Recent studies routinely rely on Signorino and Ritter's (1999, Tau-b or not tau-b: Measuring the similarity of foreign policy positions. International Studies Quarterly 43:115–44) S to assess the similarity of foreign policy ties. However, S neglects two fundamental characteristics of the international state system: foreign policy ties are relatively rare and individual states differ in their innate propensity to form such ties. I propose two chance-corrected agreement indices, Scott's (1955, Reliability of content analysis: The case of nominal scale coding. The Public Opinion Quarterly 19:321–5) π and Cohen's (1960, A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20:37–46) κ, as viable alternatives. Both indices adjust the dyadic similarity score for a large number of common absent ties. Cohen's κ also takes into account differences in individual dyad members' total number of ties. The resulting similarity scores have stronger face validity than S. A comparison of their empirical distributions and a replication of Gartzke's (2007, The capitalist peace. American Journal of Political Science 51:166–91) study of the ‘Capitalist Peace’ indicate that the different types of measures are not substitutable.

Type
Research Article
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Altfeld, Michael F., and Bueno de Mesquita, Bruce. 1979. Choosing sides in wars. International Studies Quarterly 23: 87112.CrossRefGoogle Scholar
Bapat, Navin A. 2007. The internationalization of terrorist campaigns. Conflict Management and Peace Science 24: 265–80.Google Scholar
Bearce, David H., Flanagan, Kristen M., and Floros, Katharine M. 2006. Alliances, internal information, and military conflict among member-states. International Organization 60: 595625.Google Scholar
Bennett, D. Scott, and Rupert, Matthew C. 2003. Comparing measures of political similarity: An empirical comparison of S versus in the study of international conflict. Journal of Conflict Resolution 47: 367–93.Google Scholar
Braumoeller, Bear F. 2008. Systemic politics and the origins of Great Power conflict. American Political Science Review 102: 7793.CrossRefGoogle Scholar
Bueno de Mesquita, Bruce. 1975. Measuring systemic polarity. Journal of Conflict Resolution 19: 187216.Google Scholar
Byrt, Ted, Bishop, Janet, and Carlin, John B. 1993. Bias, prevalence and kappa. Journal of Clinical Epidemiology 46: 423–9.Google Scholar
Cicchetti, Domenic V., and Feinstein, Alvan R. 1990. High agreement but low kappa II: Resolving the paradoxes. Journal of Clinical Epidemiology 43: 551–8.Google Scholar
Cohen, Jacob. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 3746.Google Scholar
Cohen, Jacob. 1968. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin 70: 213–20.Google Scholar
Correlates of War Project. 2003. Formal interstate alliance dataset, 1816-2000. Version 3.03. http://www.correlatesofwar.org/COW2%20Data/Alliances/Alliance_v3.03_dyadic.zip (accessed June 12, 2009).Google Scholar
Correlates of War Project. 2005. National material capabilities dataset.” Version 3.02. http://www.correlatesofwar.org/COW2%20Data/Capabilities/NMC_3.02.csv (accessed June 12, 2009).Google Scholar
Correlates of War Project. 2005. State system membership list. Version 2004.1. http://correlatesofwar.org/COW2%20Data/SystemMembership/system2004.csv (accessed January 8, 2008).Google Scholar
De Vaus, David A. 2001. Research design in social research. London: Sage.Google Scholar
Derouen, Karl, and Heo, Uk. 2004. Reward, punishment or inducement? US economic and military aid, 1946-1996. Defence and Peace Economics 15: 453–70.Google Scholar
Fay, Michael P. 2005. Random marginal agreement coefficients: Rethinking the adjustment for chance when measuring agreement. Biostatistics 6: 171–80.CrossRefGoogle ScholarPubMed
Feinstein, Alvan R., and Cicchetti, Domenic V. 1990. High agreement but low Kappa I: The problems of two paradoxes. Journal of Clinical Epidemiology 43: 543–9.Google Scholar
Gartzke, Erik. 1998. Kant we all just get along? Opportunity, willingness, and the origins of the democratic peace. American Journal of Political Science 42: 127.Google Scholar
Gartzke, Erik. 2007. The capitalist peace. American Journal of Political Science 51: 166–91.Google Scholar
Kastner, Scott L. 2007. When do conflicting political relations affect international trade? Journal of Conflict Resolution 51: 664–88.Google Scholar
Kellstedt, Paul M., and Whitten, Guy D. 2008. The fundamentals of political science research. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kendall, Maurice G. 1938. A new measure of rank correlation. Biometrika 30: 8193.Google Scholar
Kirk, Jennifer. 2010. ‘rmac’: Calculate RMAC or FMAC agreement coefficients.” R package, Version 0.9. http://cran.r-project.org/web/packages/rmac/ (accessed May 3, 2011).Google Scholar
Krippendorff, Klaus. 1970. Bivariate agreement coefficients for reliability of data. Sociological Methodology 2: 139–50.Google Scholar
Krippendorff, Klaus. 2004. Measuring the reliability of qualitative text analysis data. Quality and Quantity 38: 787800.Google Scholar
Lantz, Charles A., and Nebenzahl, Elliott. 1996. Behavior and interpretation of the κ statistic: Resolution of the two paradoxes. Journal of Clinical Epidemiology 49: 431–4.Google Scholar
Lin, Lawrence I.-Kuei. 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45: 255–68.Google Scholar
Long, Andrew G., and Leeds, Brett Ashley. 2006. Trading for security: military alliances and economic agreements. Journal of Peace Research 43: 433–51.Google Scholar
Morrow, James D., Siverson, Randolph M., and Tabares, Tressa E. 1998. The political determinants of international trade: the major powers, 1907-90. American Political Science Review 92: 649.CrossRefGoogle Scholar
Neumayer, Eric. 2003. What factors determine the allocation of aid by Arab countries and multilateral agencies? Journal of Development Studies 39: 134–47.Google Scholar
Oneal, John R., and Russett, Bruce. 1999. Assessing the liberal peace with alternative specifications: Trade still reduces conflict. Journal of Peace Research 36: 423–42.Google Scholar
R Development Core Team. 2011. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.Google Scholar
Scott, John. 2000. Social network analysis: A handbook. London: Sage.Google Scholar
Scott, William A. 1955. Reliability of content analysis: The case of nominal scale coding. The Public Opinion Quarterly 19(3): 321–5.Google Scholar
Shankar, Viswanathan, and Bangdiwala, Shrikant I. 2008. Behavior of agreement measures in the presence of zero cells and biased marginal distributions. Journal of Applied Statistics 35: 445–64.CrossRefGoogle Scholar
Signorino, Curtis S., and Ritter, Jeffrey M. 1999. Tau-b or not tau-b: Measuring the similarity of foreign policy positions. International Studies Quarterly 43: 115–44.Google Scholar
Sim, Julius, and Wright, Chris C. 2005. The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements. Physical Therapy 85: 257–68.CrossRefGoogle ScholarPubMed
Stone, R. W. 2004. The political economy of IMF lending in Africa. American Political Science Review 98: 577–91.Google Scholar
Sweeney, Kevin, and Keshk, Omar M. G. 2005. The similarity of states: Using S to compute dyadic interest similarity. Conflict Management and Peace Science 22: 165–87.Google Scholar
Vach, Werner. 2005. The dependence of Cohen's kappa on the prevalence does not matter. Journal of Clinical Epidemiology 58: 655–61.Google Scholar
Voeten, Eric, and Merdzanovic, Adis. 2009. United Nations General Assembly voting data. hdl:1902.1/12379UNF:3:Hpf6qOk-DdzzvXF9m66yLTg==.http://dvn.iq.harvard.edu/dvn/dv/Voeten/faces/study/StudyPage.xhtml?studyId=38311&;studyListing-Index=0_dee53f12c760141b21c251525331 (accessed June 12, 2009).Google Scholar
Zegers, Frits. 1986. A family of chance-corrected association coefficients for metric scales. Psychometrika 51: 559–62.CrossRefGoogle Scholar
Zwick, Rebecca. 1988. Another look at interrater agreement. Psychological Bulletin 103: 374–8.Google Scholar
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