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This article describes a new machine-coded event data set specifically designed to study the spatially, temporally, and tactically disaggregated actions of multiple state and nonstate actors in a systematic fashion. The project develops an extensive set of dictionaries for multiple actors and employs a new coding scheme to organize information on such actors and their behavior. The author describes the machine content-analysis methods used to collect the data and the newly developed coding scheme.

Competing hypotheses on the relationship between government and dissident behavior emerge from both formal and empirical models. Yet, the current literature lacks a comprehensive theoretical account of such contradictory effects. This study develops a theory to account for a large number of competing hypotheses within a single framework. The theory explains various government and dissident tactical choices over the course of an internal political struggle by focusing on leaders, their motivations, and the link between their motivations and actions. The theory gives rise to a process model of sequential government-dissident interactions that is used to test several implied hypotheses. Empirical sequential time-series models of government and dissident behavior find support for most of the theory's implied hypotheses in Israel (1979-2002) and Afghanistan (1990-99). Adapted from the source document.

This study posits a theory to explain government and dissident sequential responses to one another and develops a statistical model to test the implied hypotheses. While competing hypotheses emerge from both formal and empirical models, the current literature lacks a single, coherent, theoretical, and empirically corroborated model of the interactive relationship between dissident and government behavior. The study seeks to fill this lacuna in the literature by developing a comprehensive theory to account for a large number of competing hypotheses within a single framework. The subsequent empirical tests enable one to find support for the various competing hypotheses under different sets of conditions. The analyses of Chile (1983—1992) and Venezuela (1987— 1992) provide evidence that the model captures well the sequential responses of Chilean and Venezuelan governments and dissidents.

While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, "Event Data Analysis and Threats from Temporal Aggregation") political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.

Instructors constantly encourage students to learn and process
information. Brock and Cameron assert, "Individuals process
information, learn concepts, and solve problems in different ways"
(1999, 251). Some students learn by listening, others learn by
taking notes, more learn by seeing, and still others learn by doing
and saying. Yet in many college class-rooms, the dominant teaching
method is the traditional lecture. While lecturing may be a
necessary teaching technique, it is often insufficient for teaching
a large number of students with varying learning preferences.

David A. Kolb's learning stages (1984) accommodate diverse learning preferences by working through four stages: concrete experience, reflexive observation, abstract conceptualization, & active experimentation of learning. The process & results from using Kolb's experimental learning model in an introductory Comparative Politics course are described. Two simulations -- of a mock proportional representation election in Germany, & of government formations in multiparty political systems -- were set up in the undergraduate classroom to work through Kolb's stages & expose students to political systems that differ from the US. Students found the exercise to be valuable. As a teacher's tool, simulations help students to retain more information, provide a break from the traditional lecture format, & provide information about a range of political science & government processes. 4 Figures, 20 References. Adapted from the source document.