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GWIPP Research: Policy Research Methods


Title: Improving Prediction and Causal Inference with Graphical Methods and Models
Researcher(s): Langche Zeng (Dept. of Political Science, GWU)
Funding: The National Science Foundation
Start Date:
Status: Completed

Summary:
This project explores the utility of various graphical models and methods, in particular causal graph theory, social networks and random graphs for relational data, in improving prediction and causal inference in empirical political and social sciences. Graphical models are naturally suited for conceptualizing and representing relationships, and graphical methods provide promising tools for studying structural properties of political and social systems. The project also seeks to extend the methods to accommodate special features of social science data, such as functional complexity and rareness of certain events.


Title: Creating Cross-Institutional Preference Measures: Methodological Improvements for Studying Constraints on the Supreme Court
Researcher(s): Forrest Maltzman (GWIPP and the George Washington University Department of Political Science), and Michael Bailey ( Georgetown University).
Funding: National Science Foundation
Start Date:
Status: Completed

Summary:
Since the founding of the United States, interaction between the Supreme Court and other political actors has been an important element of the American political landscape. Understanding the nature and implications of the interactions between the modern Court and other political actors has been an ongoing intellectual challenge. The key problem is the difficulty of comparing the policy preferences of political actors across institutional boundaries. Bailey and Maltzman propose a three-part research design to address these needs. First, Bailey and Maltzman propose undertaking extensive original data collection of cross-institutional position taking. These data provide the foundation for comparing preferences of justices, legislators, the president, and interest groups. Second, Bailey and Maltzman propose analyzing these data with novel preference measurement techniques to create ideal point estimates that are comparable across institutions. Third, they propose using the measures to model the influence of external actors on Supreme Court decision making.