Cluster-Based Early Warning Indicators for Political Change in the Contemporary Levant

Philip A. Schrodt, Deborah J. Gerner

Research output: Contribution to journalArticle

46 Scopus citations

Abstract

We use cluster analysis to develop a model of political change in the Levant as reflected 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 in distance to subsequent points than to preceding ones. These clusters begin to "stretch" before breaking apart, which serves as an early warning indicator. The clusters 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 statistical characteristics. Our study demonstrates that the statistical analysis of newswire reports can yield systematic early warning indicators, and it provides empirical support for the theoretical concept of distinct behavioral phases in political activity.

Original languageEnglish (US)
Pages (from-to)803-817
Number of pages15
JournalAmerican Political Science Review
Volume94
Issue number4
DOIs
StatePublished - Dec 2000

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

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