American Political Science Review



ARTICLES

The Statistical Analysis of Roll Call Data


JOSHUA CLINTON a1c1, SIMON JACKMAN a2c2 and DOUGLAS RIVERS a2c3
a1 Princeton University
a2 Stanford University

Abstract

We develop a Bayesian procedure for estimation and inference for spatial models of roll call voting. This approach is extremely flexible, applicable to any legislative setting, irrespective of size, the extremism of the legislators' voting histories, or the number of roll calls available for analysis. The model is easily extended to let other sources of information inform the analysis of roll call data, such as the number and nature of the underlying dimensions, the presence of party whipping, the determinants of legislator preferences, and the evolution of the legislative agenda; this is especially helpful since generally it is inappropriate to use estimates of extant methods (usually generated under assumptions of sincere voting) to test models embodying alternate assumptions (e.g., log-rolling, party discipline). A Bayesian approach also provides a coherent framework for estimation and inference with roll call data that eludes extant methods; moreover, via Bayesian simulation methods, it is straightforward to generate uncertainty assessments or hypothesis tests concerning any auxiliary quantity of interest or to formally compare models. In a series of examples we show how our method is easily extended to accommodate theoretically interesting models of legislative behavior. Our goal is to provide a statistical framework for combining the measurement of legislative preferences with tests of models of legislative behavior.


Correspondence:
c1 Assistant Professor, Department of Politics, Princeton University, Princeton, NJ 08540 (clinton@princeton.edu).
c2 Associate Professor and Director of the Political Science Computational Laboratory, Department of Political Science, Stanford University, Stanford, CA 94305-6044 (jackman@stanford.edu).
c3 Professor, Department of Political Science, Stanford University, Stanford, CA 94305-6044 (rivers@stanford.edu).


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