Cluster-based early warning indicators for political change in the contemporary levant
By: SCHRODT, Philip A
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Contributor(s): GERNER, Deborah J.
Material type: 
ArticlePublisher: 2000American Political Science Review 94, 4, p. 803-818Abstract: We use cluster analysis to develop a model of political change in the Levant as refrected in the World Event Interaction Survey coded event data generated from Reuters between 1979 and 1998. A new statistical algorithm that uses the correlation between dyadic behaviors at two times identifies clusters of political activity. The transition to a new cluster occurs when a point is closer is distance to subsequent points than to preceding ones. These clusters begin to "strech" before breaking apart, which serves as an early warning indicator. The cluster correspond well with phases of political behavior identified a priori. A Monte Carlo analysis shows that the clustering and early warning measures are not random; they perform very differently in simulated data sets with similar statiscial characteristics. Our study deonstrates that the statistical analsis of newswrite reports ance yeold systmatic early warning indicators, and it provides empirical support for the theoretical concept of distinct behavioral phases in political activity
| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
| Periódico | Biblioteca Graciliano Ramos | Periódico | Not for loan |
We use cluster analysis to develop a model of political change in the Levant as refrected in the World Event Interaction Survey coded event data generated from Reuters between 1979 and 1998. A new statistical algorithm that uses the correlation between dyadic behaviors at two times identifies clusters of political activity. The transition to a new cluster occurs when a point is closer is distance to subsequent points than to preceding ones. These clusters begin to "strech" before breaking apart, which serves as an early warning indicator. The cluster correspond well with phases of political behavior identified a priori. A Monte Carlo analysis shows that the clustering and early warning measures are not random; they perform very differently in simulated data sets with similar statiscial characteristics. Our study deonstrates that the statistical analsis of newswrite reports ance yeold systmatic early warning indicators, and it provides empirical support for the theoretical concept of distinct behavioral phases in political activity
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