Using Cluster Analysis to Derive Early Warning Indicators for Political Change in the Middle East, 1979-1996

This paper uses event data to develop an early warning model of major political changes in the Levant for the period April 1979 to July 1996. Following a general review of statistical early warning research, the analysis focuses on the behavior of eight Middle Eastern actors—Egypt, Israel, Jordan, Lebanon, the Palestinians, Syria, the United States and USSR/Russia—using WEIS-coded event data generated from Reuters news service lead sentences with the KEDS machine-coding system. The analysis extends earlier work (Schrodt and Gerner 1995) demonstrating that clusters of behavior identified by conventional statistical methods correspond well with changes in political behavior identified a priori. We employ a new clustering algorithm that uses the correlation between the dyadic behaviors at two points in time as a measure of distance, and identifies cluster breaks as those time points that are closer to later points than to preceding points. We also demonstrate that these data clusters begin to "stretch" prior to breaking apart; this characteristic is used as an early-warning indicator. A Monte-Carlo analysis shows that the clustering and early warning measures perform very differently in simulated data sets having the same mean, variance, and autocorrelation as the observed data (but no cross-correlation) which reduces the likelihood that the clustering patterns are due to chance. The initial analysis uses Goldstein's (1992) weighting system to aggregate the WEIS-coded data. In an attempt to improve on the Goldstein scale, we use a genetic algorithm to optimize the weighting of the WEIS event categories for the purpose of clustering. This does not prove very successful and only differentiates clusters in the first half of the data set, a result similar to one we obtained using the cross-sectional K-Means clustering procedure. Correlating the frequency of events in the twenty-two 2-digit WEIS categories, on the other hand, gives clustering and early warning results similar to those produced by the Goldstein scale. The paper concludes with some general remarks on the role of quantitative early warning and directions for further research. This research was funded by the National Science Foundation through grant SBR-9410023 and the University of Kansas General Research Fund Grant 3500-X0-0038. © 1996, Philip A. Schrodt and Deborah J. Gerner Schrodt & Gerner: Using Cluster Analysis... Page 1

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