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

Published online by Cambridge University Press:  04 January 2017

Aleks Jakulin ,
Wray Buntine ,
Timothy M. La Pira  and
Holly Brasher
Aleks Jakulin
Affiliation:
Department of Statistics, Columbia University, New York, NY 10027. e-mail: [email protected]
Wray Buntine
Affiliation:
Statistical Machine Learning, NICTA, Canberra ACT 2601, Australia. e-mail: [email protected]
Timothy M. La Pira
Affiliation:
Department of Political Science, College of Charleston, Charleston, SC 29424. e-mail: [email protected]
Holly Brasher*
Affiliation:
Department of Government, University of Alabama at Birmingham, Birmingham, AL 35294-1152
*
e-mail: [email protected] (corresponding author)
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Abstract

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.

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 3 , Summer 2009 , pp. 291 - 310
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

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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