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Application & Financial Support Due date: February 27th, 2006
I. Institutions and Institutional Analysis II. Empirical Evaluation of Causality III. Complexity: Diversity, Networks, Adaptation, and Emergence IV. Additional Guest Lecturers and Student Presentations
Contact: eitm@umich.edu |
Empirical
Implications of Theoretical Models Design of Instruction The EITM Summer Institutes concentrate on reaserach in areas of political science that integrate both theory and methods. The 2006 EITM V institute follows the successful format of previous EITM summer institutes (2002 EITM I (Harvard), 2003 EITM II (Michigan), 2004 EITM III (Duke), and 2005 EITM IV (Berkeley)), and will combine highly interactive teaching and lecture sessions with ample opportunity for students to develop their research projects in the EITM framework. Each of the first three weeks of instruction will focus on a substantive area where research integrating theory and empirics has already made rich contributions to political science. These are: (1) Institutions and Institutional Analysis ; (2) Empirical Evaluation of Causality ; and (3) Complexity: Diversity, Networks, Adaptation, and Emergence . The fourth week of the institute will focus on students' research projects, and will conclude with student presentations broadcasted on the World Wide Web. The institute will integrate developments and findings in the substantive areas of American politics, comparative politics, international relations, and political economy throughout the entire four weeks.
The First Three Weeks: Substantive Units In each of the first three weeks of instruction, lecturers will conduct a survey of their substantive research area, stressing key previous theoretical and empirical developments. They will explicate the steps needed to construct "tests" of models in that area by, e.g., considering basic assumption validity or drawing testable conjectures from comparative statics and other deductions from the model. And they will discuss appropriate empirical methods for evaluating whether and how data confirm or reject the model, developing more fully any highly specialized techniques required. These empirical modeling considerations could involve specifying test equations with the proper control variables and functional forms, deriving statistical estimators, conducting case studies, designing an experiment, or framing a simulation. |
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