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A combination of technological change, methodological drift and a certain degree of intellectual sloth, particularly with respect to philosophy of science, has allowed contemporary quantitative political analysis to accumulate a series of dysfunctional habits that have rendered much of contemporary research more or less meaningless. I identify these 'seven deadly sins' as: Garbage can models that ignore the effects of collinearity; Pre-scientific explanation in the absence of prediction; Excessive reanalysis of a small number of datasets; Using complex methods without understanding the underlying assumptions; Interpreting frequentist statistics as if they were Bayesian; A linear statistical monoculture that fails to consider alternative structures; Confusing statistical controls and experimental controls. The answer to these problems is not to abandon quantitative approaches, but rather engage in solid, thoughtful, original work driven by an appreciation of both theory and data. The article closes with suggestions for changes in current practice that might serve to ameliorate some of these problems.

We propose a framework for forecasting and analyzing regional and international conflicts. It generates forecasts that (1) are accurate but account for uncertainty, (2) are produced in (near) real time, (3) capture actors' simultaneous behaviors, (4) incorporate prior beliefs, and (5) generate policy contingent forecasts. We combine the CAMEO event-coding framework with Markov-switching and Bayesian vector autoregression models to meet these goals. Our example produces a series of forecasts for material conflict between the Israelis and Palestinians for 2010. Our forecast is that the level of material conflict between these belligerents will increase in 2010, compared to 2009.

Text is arguably the most pervasive—and certainly the most persistent—artifact of political behavior. Extensive collections of texts with clearly recognizable political—as distinct from religious—content go back as far as 2500 BCE in the case of Mesopotamia and 1300 BCE for China, and 2400-year-old political discussions dating back to the likes of Plato, Aristotle, and Thucydides are common fare even in the introductory study of political thought. Political tracts were among the earliest productions following the introduction of low-cost printing in Europe—fueling more than a few revolutions and social upheavals—and continuous printed records of legislative debates, such as the British parliament's Hansard and precursors tracing to 1802, cover centuries of political discussion.

Every good book has a small bit—a sentence, paragraph, maybe a page—that the authors intended as a simple aside but which brings an epiphany to the reader. In Brady and Collier (2004), this occurs at the beginning of chapter 3: Brady's critique of the “quantitative template,” where the recovering seminarian frames our discourse on the philosophy of social inquiry in terms of pragmatic theology and homeliletics, rather than science or sociology. Hey, that is it!—while this debate is not in any sense about religion, its dynamics are best understood as though it were about religion. We have always known that, it just needed to be said.

The goal of this paper is to provide a general overview of event data and its importance in the study of political conflict. In doing so, we illustrate how the level of methodological sophistication in the data collection, coding, and analyzing process has progressed from the earliest event-data projects in the 1970s to the most current ones today. Additionally, we highlight ongoing debates in the discipline as well as address key challenges that practitioners of event data will have to overcome to generate increasingly accurate and nuanced analyses in the future. [PUBLICATION ABSTRACT]

Event data on the Israel-Lebanon and Israel-Palestinian conflicts in the Levant (1979-1999) and the Serbia-Croatia and Serbia-Bosnia conflicts in the Balkans (1991-1999) are used to test two sets of process-related hypotheses embedded in the theoretical and qualitative literatures on mediation. Cross-correlation analysis is used to examine the time delay in the effects of mediation on the level of violence over time. Results show that these effects vary somewhat, depending on the conflict, and differ substantially, depending on who is conducting the mediation. Whether conflict reduction is most effectively achieved with sanctions or rewards is tested. Results show that a reduction in violence is generally associated with mediation combined with conflictual action directed toward both of the antagonists and combined with cooperative action directed to the weaker antagonist.

Schrodt comments on King and Zeng's paper that presents a method for determining the potential for research findings to be model-dependent rather than data-dependent using counterfactuals. Schrodt argues that King and Zeng's claims promise much more than they actually deliver.

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.

This paper is a study of the validity of a machine-coded event data series for six Middle Eastern actors and the United States. The series is based on Reuters newswire story leads coded into the WEIS categories. The face validity of the data is assessed by examining the monthly net cooperation scores based on Goldstein's (1992) scale in comparison to narrative accounts of the interactions between the actors; the event data series clearly shows the major patterns of political interaction. The machine-coded data are also compared to a human-coded WEIS data set based on the New York Times and Los Angeles Times. Almost all dyads show a statistically significant correlation between the number of events reported by the two series, as well as the number of cooperative events. About half of the dyads show significant correlation in net cooperation and the number of conflictual events; many of these differences appear to be due to the higher density of events in Reuters. Finally, the machine-coded and WEIS data sets are used in two statistical time series studies and are shown to produce generally comparable results.

Existing formal models of political behavior have followed the lead of the natural sciences and generally focused on methods that use continuous-variable mathematics. In 2002, Stephen Wolfram produced an extended critique of that approach in the natural sciences, and suggested that a great deal of natural behavior can be accounted for using rules that produce discrete patterns. This paper reports some initial findings designed to apply this pattern-based method to political event data. We believe that discrete sequence rule (DSR) models can provide a new social science methodology that is capable of preserving the agential basis of social interaction, tracking multiple agents as they enact rules through behavior directed at one another, and capturing the evolution of such interaction over time. The core of this project is a new, publicly accessible Web-based tool designed for the visualization and analysis of event data patterns (http://www.kss.org). Using event data on the Israel—Palestine conflict generated by the TABARI automated coding program of the Kansas Event Data System (KEDS) for the period 1979–2004, we perform an initial exploration of this methodology. Specifically, we identify patterned behavior for which specific rule use can be imputed, and then examine several agent-based rules, plus four \"meta-rules,\" to parse Israeli—Palestinian interaction over time. Face validity of the analysis is apparent, and we also find the qualitative historical record can be augmented through observation of rule enactment in the event stream. Several descriptive empirical applications are demonstrated, including moving totals and increasingly complex sequences of rule enactment that go beyond the simple variations on tit-for-tat responses. While this paper represents an exploratory analysis of the method, the results are promising enough to warrant further investigation beyond its use in thick description as demonstrated here, to ultimately include hypothesis generation and falsification.