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Political Analysis Advance Access originally published online on June 30, 2009
Political Analysis 2009 17(3):291-310; doi:10.1093/pan/mpp006
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© The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Analyzing the U.S. Senate in 2003: Similarities, Clusters, and Blocs

Aleks Jakulin

Department of Statistics, Columbia University, New York, NY 10027 e-mail: jakulin{at}gmail.com

Wray Buntine

Statistical Machine Learning, NICTA, Canberra ACT 2601, Australia e-mail: wray.buntine{at}nicta.com.au

Timothy M. La Pira

Department of Political Science, College of Charleston, Charleston, SC 29424 e-mail: lapirat{at}cofc.edu

Holly Brasher

Department of Government, University of Alabama at Birmingham, Birmingham, AL 35294-1152 e-mail: hbrasher{at}uab.edu (corresponding author)

In this paper, we apply information theoretic measures to voting in the U.S. Senate in 2003. We assess the associations between pairs of senators and groups of senators based on the votes they cast. For pairs, we use similarity-based methods, including hierarchical clustering and multidimensional scaling. To identify groups of senators, we use principal component analysis. We also apply a discrete multinomial latent variable model that we have developed. In doing so, we identify blocs of cohesive voters within the Senate and contrast it with continuous ideal point methods. We find more nuanced blocs than simply the two-party division. Under the bloc-voting model, the Senate can be interpreted as a weighted vote system, and we are able to estimate the empirical voting power of individual blocs through what-if analysis.

Authors' note: We are grateful for advice from Brian Lawson, Antti Pajala, and Andrew Gelman. Replication materials are available on the Political Analysis Web site.


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