Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Militarized Interstate Dispute Analysis× | Alliance Network Analysis× | |
|---|---|---|
| Область | International Relations | International Relations |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1996 | 2012 |
| Автор метода≠ | Daniel Jones, Stuart Bremer & J. David Singer (Correlates of War project) | Skyler Cranmer, Bruce Desmarais & Elizabeth Menninga |
| Тип≠ | Coding and statistical analysis of interstate militarized confrontations | Network analysis and inferential network modeling of interstate alliances |
| Основополагающий источник≠ | Jones, D. M., Bremer, S. A., & Singer, J. D. (1996). Militarized interstate disputes, 1816–1992: Rationale, coding rules, and empirical patterns. Conflict Management and Peace Science, 15(2), 163–213. DOI ↗ | Cranmer, S. J., Desmarais, B. A., & Menninga, E. J. (2012). Complex dependencies in the alliance network. Conflict Management and Peace Science, 29(3), 279–313. DOI ↗ |
| Другие названия | MID Analysis, Militarized Dispute Coding, Correlates of War Dispute Analysis, Dyadic Conflict Onset Analysis | International Alliance Networks, Alliance Portfolio Network Analysis, Network Models of Alliance Formation, Interstate Alliance Graph Analysis |
| Связанные | 3 | 3 |
| Сводка≠ | Militarized interstate dispute (MID) analysis is the coding and quantitative study of confrontations in which one state threatens, displays, or uses military force against another. Built on the Correlates of War project's MID dataset and the coding rules codified by Jones, Bremer, and Singer (1996), it provides the standard observational measure of interstate conflict short of and including war, structured as dyad-years so that the onset, escalation, and outcomes of disputes can be modeled statistically across two centuries of the international system. | Alliance network analysis studies international alliances as a graph of states linked by formal security commitments, and models how that network forms and evolves. Rather than treating each alliance dyad as independent, it uses network science and inferential models such as the exponential random graph model (ERGM) — applied to alliance data by Cranmer, Desmarais, and Menninga (2012) — to capture the complex dependencies, such as a state's tendency to ally with its allies' allies, that ordinary dyadic regression assumes away. |
| ScholarGateНабор данных ↗ |
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