Skip to main content Accessibility help
×
Home

(Mis)Using Dyadic Data to Analyze Multilateral Events

  • Paul Poast (a1)

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.

Copyright

References

Hide All
Achen, Christopher. 2005. Let's put garbage-can regressions and garbage-can probits where they belong. Conflict Management and Peace Science 22: 327–39.
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.
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.
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.
Beck, Nathaniel, King, Gary, and Zeng, Lanche. 2000. Improving quantitative studies of international conflict: A conjecture. The American Political Science Review 94: 2135.
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.
Bennett, Scott, and Stam, Allan. 2007. The behavioral origins of war. Ann Arbor: University of Michigan Press.
Bianco, William T., and Bates, Robert H. 1990. Cooperation by design: Leadership, structure, and collective dilemmas. The American Political Science Review 84: 133–47.
Bremer, Stuart A. 1992. Dangerous dyads: Conditions affecting the likelihood of interstate war, 1816-1965. The Journal of Conflict Resolution 36: 309–41.
Clarke, Kevin. 2005. The phantom menace: Omitted variable bias in econometric research. Conflict Management and Peace Science 22: 341–52.
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.
Findley, Michael, and Teo, Tze Kwang. 2006. Rethinking third party interventions into civil wars: An actor-centric approach. Journal of Politics 68: 828–37.
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.
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.
Franzese, Robert, and Hays, Jude. 2007b. Empirical models of international capital-tax competition. In International taxation handbook. 4372.
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.
Franzese, Robert, and Hays, Jude. 2008. Interdependence in comparative politics. Comparative Political Studies 41: 742–80.
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.
Gent, Stephen. 2007. Strange bedfellows: The strategic dynamics of major power military interventions. Journal of Politics 69: 1089–102.
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.
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.
Green, Donald, Kim, Soo Yeon, and Yoon, David H. 2001. Dirty pool. International Organization 441–68.
Golder, Sona. 2006. The logic of pre-electoral coalition formation. Columbus: Ohio State University Press.
Hays, Jude. 2003. Globalization and capital taxation in consensus and majoritarian democracies. World Politics 56(3): 79113.
Hoff, Peter. 2005. Bilinear mixed-effects models for dyadic data. Journal of the American Statistical Association 100: 286–95.
Hoff, Peter, and Ward, Michael. 2004. Modeling dependencies in international relations networks. Political Analysis 12: 160–75.
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.
King, Gary. 2001. Proper nouns and methodological propriety: Pooling dyads in international relations data. International Organization 55: 497507.
King, Gary, and Zeng, Langche. 2001a. Explaining rare events in international relations. International Organization 55: 693715.
King, Gary, and Zeng, Langche. 2001b. Logistic regression in rare events data. Political Analysis 9: 137–63.
Lai, Brian, and Reiter, Dan. 2000. Democracy, political similarity, and international alliances, 1816-1992. The Journal of Conflict Resolution 44: 203–27.
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.
Mansfield, Edward, Milner, Helen, and Rosendorff, Edward. 2002. Why democracies cooperate more: Electoral control and international trade agreements. International Organization 56: 477513.
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.
Martin, Lanny, and Stevenson, Randolph. 2001. Government formation in parliamentary democracies. American Journal of Political Science 45: 3350.
Morrow, James. 1991. Alliances and asymmetry: An alternative to the capability aggregation model of alliances. American Journal of Political Science 35: 904–33.
Oneal, John, and Russett, Bruce. 1997. The classic liberals were right: Democracy, interdependence, and conflict, 1950-1985. International Studies Quarterly 41: 267–93.
Peceny, Mark, Beer, Caroline, and Sanchez-Terry, Shannon. 2002. Dictatorial peace? The American Political Science Review 96: 1526.
Ray, James Lee 2005. Constructing multivariate analyses (of dangerous dyads). Conflict Management and Peace Science 22: 277–92.
Reiter, Dan, and Stam, Allan. 2002. Democracies at war. Princeton, NJ: Princeton University Press.
Reiter, Dan, and Stam, Allan. 2003. Indentifying the culprit: Democracy, dictatorship, and dispute initiation. The American Political Science Review 97: 333–7.
Remmer, Karen. 1998. Does democracy promote interstate cooperation? Lessons from the Mercosur region. International Studies Quarterly 42: 2551.
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).
Robins, Garry, and Morris, Martina. 2007. Advances in exponential random graph models. Social Networks 29: 169–72.
Signorino, Curtis. 1999. Strategic interaction and the statistical analysis of international conflict. The American Political Science Review 93: 279–97.
Starr, Harvey. 2005. Cumulation from proper specification: Theory, logic, research design, and ‘nice’ laws. Conflict Management and Peace Science 22: 353–63.
Steglich, Christian, Snijders, Tom, and Pearson, Michael. Forthcoming 2010. Dynamic networks behavior: Separating selection from influence. Sociological Methodology.
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.
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.
Wallace, Michael. 1976. Arms races and the balance of power: A preliminary model. Applied Mathematical Modeling 1: 8392.
Wallace, Michael. 1979. Arms races and escalation: Some new evidence. The Journal of Conflict Resolution 23: 316.
Warren, T. Camber. Forthcoming 2010. The geometry of security: Modeling interstate alliances as evolving networks. Journal of Peace Research.
Weede, Erich. 1980. Arms races and escalation: Some persisting doubts. The Journal of Conflict Resolution 24: 285–7.
MathJax
MathJax is a JavaScript display engine for mathematics. For more information see http://www.mathjax.org.
Type Description Title
UNKNOWN
Supplementary materials

Poast supplementary material
Supplementary Material

 Unknown (33.5 MB)
33.5 MB

(Mis)Using Dyadic Data to Analyze Multilateral Events

  • Paul Poast (a1)

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed