Collaborative Research: The Dimensions of Supreme Court Decision Making, 1946-2000

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What factors best explain the legal decisions of U.S. Supreme Court justices? To what extent do the policy preferences of justices outweigh purely formal, legal concerns when deciding cases? How many policy dimensions structure the preferences of justices in the post-war era? Is the current Court more conservative than the Vinson and early Warren courts? Have the decisions of lower courts become more liberal over time? In what manner have the policy outputs of the Court changed over time? These are but a handful of the substantive questions the researchers will answer using statistical models customized to the peculiarities of the U.S. Supreme Court. More specifically, using a Bayesian inferential approach, the principal investigators develop variants of item response models that: 1) are suitable for multidimensional choice situations with small numbers of subjects; 2) explicitly model dynamics in ideal points and case-parameters; and 3) can be used to explain voting behavior with covariates (case facts, the court of origin, the arguments raised, the issues considered, etc.) while simultaneously controlling for and measuring policy preferences. To model the dynamics of ideal points and case parameters, the principal investigators use the dynamic linear model (DLM) machinery within the context of item response modeling. One of the goals of this proposal is to integrate these modeling strategies so that multidimensional models can be fit to longitudinal data in a computationally efficient manner. The second methodological goal of this proposal is to build and fit models that jointly measure policy preferences and account for the effects of measured covariates on voting decisions. This project lies at the intersection of several recent growth areas in social statistics: binary time-series and time-series cross-sectional data analysis, mixture modeling, hierarchical Bayesian modeling, and latent variable modeling. While the proposed class of models will be customized for work involving decision making on the U.S. Supreme Court, many of the proposed methods will be relevant to other applications in political science (such as behavior on lower courts, committee decision making, and legislative behavior) and other areas of social statistics (such as educational statistics, psychometrics, and econometrics).