Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-06-20T11:30:18.992Z Has data issue: false hasContentIssue false

(Mis)Using Dyadic Data to Analyze Multilateral Events

Published online by Cambridge University Press:  04 January 2017

Paul Poast*
Affiliation:
Department of Political Science, University of Michigan, 5700 Haven Hall, Ann Arbor, MI 48109. e-mail: poastpd@umich.edu

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
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Achen, Christopher. 2005. Let's put garbage-can regressions and garbage-can probits where they belong. Conflict Management and Peace Science 22: 327–39.Google Scholar
Basinger, Scott, and Hallerberg, Mark. 2004. Remodeling the competition for capital: How domestic politics erases the race to the bottom. The American Political Science Review 98: 261–7.CrossRefGoogle Scholar
Beck, Nathaniel, and Katz, Jonathan. 2001. Throwing out the baby with the bath water: A comment on Green, Kim, and Yoon. International Organization 55: 487–95.Google Scholar
Beck, Nathaniel, Katz, Jonathan, and Tucker, Richard. 1998. Taking time seriously: Time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42: 1260–88.Google Scholar
Beck, Nathaniel, King, Gary, and Zeng, Lanche. 2000. Improving quantitative studies of international conflict: A conjecture. The American Political Science Review 94: 2135.Google Scholar
Bennett, Scott, and Stam, Allan. 2000. Research design and estimator choices in the analysis of interstate dyads: When decisions matter. The Journal of Conflict Resolution 44: 653–85.Google Scholar
Bennett, Scott, and Stam, Allan. 2007. The behavioral origins of war. Ann Arbor: University of Michigan Press.Google Scholar
Bianco, William T., and Bates, Robert H. 1990. Cooperation by design: Leadership, structure, and collective dilemmas. The American Political Science Review 84: 133–47.Google Scholar
Bremer, Stuart A. 1992. Dangerous dyads: Conditions affecting the likelihood of interstate war, 1816-1965. The Journal of Conflict Resolution 36: 309–41.Google Scholar
Clarke, Kevin. 2005. The phantom menace: Omitted variable bias in econometric research. Conflict Management and Peace Science 22: 341–52.Google Scholar
Croco, Sarah, and Teo, Tze Kwang. 2005. Assessing the dyadic approach to interstate conflict processes: A.k.a. “dangerous” dyad-years. Conflict Management and Peace Science 22: 518.CrossRefGoogle Scholar
Findley, Michael, and Teo, Tze Kwang. 2006. Rethinking third party interventions into civil wars: An actor-centric approach. Journal of Politics 68: 828–37.Google Scholar
Franklin, M., and Mackie, T. 1984. Reassessing the importance of size and ideology in the formation of governing coalitions in parliamentary democracies. American Journal of Political Science 28: 671–92.Google Scholar
Franzese, Robert, and Hays, Jude. 2007a. Empirical models of spatial interdependence. In Oxford handbook of political methodology, ed. Read, Colin and Gregorious, Greg N., 570604. Oxford: Oxford University Press.Google Scholar
Franzese, Robert, and Hays, Jude. 2007b. Empirical models of international capital-tax competition. In International taxation handbook. 4372.Google Scholar
Franzese, Robert, and Hays, Jude. 2007c. Spatial-econometric models of cross-sectional interdependence in political science panel and time-series-cross-section data. Political Analysis 15: 140–64.Google Scholar
Franzese, Robert, and Hays, Jude. 2008. Interdependence in comparative politics. Comparative Political Studies 41: 742–80.Google Scholar
Franzese, Robert, Hays, Jude, and Kachi, Aya. 2009. The m-STAR model as an approach to modeled, dynamic, endogenous interdependence in comparative and international political economy. Working paper.CrossRefGoogle Scholar
Gent, Stephen. 2007. Strange bedfellows: The strategic dynamics of major power military interventions. Journal of Politics 69: 1089–102.Google Scholar
Gibler, Douglas, Rider, Toby, and Hutchison, Mare. 2005. Taking arms against a sea of troubles: Conventional arms races during periods of rivalry. Journal of Peace Research 42: 131–47.Google Scholar
Gibler, Douglas, and Wolford, Scott. 2006. Alliances, then democracy: An examination of the relationship between regime type and alliance formation. The Journal of Conflict Resolution 50: 125.Google Scholar
Green, Donald, Kim, Soo Yeon, and Yoon, David H. 2001. Dirty pool. International Organization 441–68.Google Scholar
Golder, Sona. 2006. The logic of pre-electoral coalition formation. Columbus: Ohio State University Press.Google Scholar
Hays, Jude. 2003. Globalization and capital taxation in consensus and majoritarian democracies. World Politics 56(3): 79113.Google Scholar
Hoff, Peter. 2005. Bilinear mixed-effects models for dyadic data. Journal of the American Statistical Association 100: 286–95.Google Scholar
Hoff, Peter, and Ward, Michael. 2004. Modeling dependencies in international relations networks. Political Analysis 12: 160–75.Google Scholar
Kadera, Kelly, and Mitchell, Sara McLaughlin. 2005. Manna from heaven or forbidden fruit? The (Ab)use of control variables in research on international conflict. Conflict Management and Peace Science 22: 273–5.CrossRefGoogle Scholar
King, Gary. 2001. Proper nouns and methodological propriety: Pooling dyads in international relations data. International Organization 55: 497507.Google Scholar
King, Gary, and Zeng, Langche. 2001a. Explaining rare events in international relations. International Organization 55: 693715.CrossRefGoogle Scholar
King, Gary, and Zeng, Langche. 2001b. Logistic regression in rare events data. Political Analysis 9: 137–63.CrossRefGoogle Scholar
Lai, Brian, and Reiter, Dan. 2000. Democracy, political similarity, and international alliances, 1816-1992. The Journal of Conflict Resolution 44: 203–27.Google Scholar
Leeds, Brett Ashley, Ritter, Jeffrey M., McLaughlin Mitchell, Sara, and Long, Andrew. 2002. Alliance treaty obligations and provisions, 1815-1944. International Interactions 28: 237–60.CrossRefGoogle Scholar
Mansfield, Edward, Milner, Helen, and Rosendorff, Edward. 2002. Why democracies cooperate more: Electoral control and international trade agreements. International Organization 56: 477513.CrossRefGoogle Scholar
Mansfield, Edward, and Reinhardt, Eric. 2003. Multilateral determinants of regionalism: The effects of GATT/WTO on the formation of preferential trading arrangements. International Organization 57: 829–62.Google Scholar
Martin, Lanny, and Stevenson, Randolph. 2001. Government formation in parliamentary democracies. American Journal of Political Science 45: 3350.CrossRefGoogle Scholar
Morrow, James. 1991. Alliances and asymmetry: An alternative to the capability aggregation model of alliances. American Journal of Political Science 35: 904–33.Google Scholar
Oneal, John, and Russett, Bruce. 1997. The classic liberals were right: Democracy, interdependence, and conflict, 1950-1985. International Studies Quarterly 41: 267–93.Google Scholar
Peceny, Mark, Beer, Caroline, and Sanchez-Terry, Shannon. 2002. Dictatorial peace? The American Political Science Review 96: 1526.Google Scholar
Ray, James Lee 2005. Constructing multivariate analyses (of dangerous dyads). Conflict Management and Peace Science 22: 277–92.Google Scholar
Reiter, Dan, and Stam, Allan. 2002. Democracies at war. Princeton, NJ: Princeton University Press.Google Scholar
Reiter, Dan, and Stam, Allan. 2003. Indentifying the culprit: Democracy, dictatorship, and dispute initiation. The American Political Science Review 97: 333–7.CrossRefGoogle Scholar
Remmer, Karen. 1998. Does democracy promote interstate cooperation? Lessons from the Mercosur region. International Studies Quarterly 42: 2551.Google Scholar
Ripley, Ruth, and Snijders, Tom A. B. 2010. Manual for SIENA version 4.0. Department of Statistics, Nuffield College, University of Oxford. http://stat.gamma.rug.nl/s_man400.pdf (accessed July 15, 2010).Google Scholar
Robins, Garry, and Morris, Martina. 2007. Advances in exponential random graph models. Social Networks 29: 169–72.Google Scholar
Signorino, Curtis. 1999. Strategic interaction and the statistical analysis of international conflict. The American Political Science Review 93: 279–97.Google Scholar
Starr, Harvey. 2005. Cumulation from proper specification: Theory, logic, research design, and ‘nice’ laws. Conflict Management and Peace Science 22: 353–63.Google Scholar
Steglich, Christian, Snijders, Tom, and Pearson, Michael. Forthcoming 2010. Dynamic networks behavior: Separating selection from influence. Sociological Methodology.Google Scholar
Stone, Randall, Slantchev, Branislav, and Tamar, London. 2008. Choosing how to cooperate: A repeated public-goods model of international relations. International Studies Quarterly 52: 335–62.Google Scholar
Ward, Michael, Siverson, Randolph, and Cao, Xun. 2007. Disputes, democracies, and dependencies: A reexamination of the Kantian peace. American Journal of Political Science 51: 583601.Google Scholar
Wallace, Michael. 1976. Arms races and the balance of power: A preliminary model. Applied Mathematical Modeling 1: 8392.Google Scholar
Wallace, Michael. 1979. Arms races and escalation: Some new evidence. The Journal of Conflict Resolution 23: 316.CrossRefGoogle Scholar
Warren, T. Camber. Forthcoming 2010. The geometry of security: Modeling interstate alliances as evolving networks. Journal of Peace Research.Google Scholar
Weede, Erich. 1980. Arms races and escalation: Some persisting doubts. The Journal of Conflict Resolution 24: 285–7.Google Scholar
Supplementary material: File

Poast supplementary material

Supplementary Material

Download Poast supplementary material(File)
File 33.5 MB