Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-26T04:28:24.236Z Has data issue: false hasContentIssue false
  • English
  • Français

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: jakulin@gmail.com
Wray Buntine
Affiliation:
Statistical Machine Learning, NICTA, Canberra ACT 2601, Australia. e-mail: wray.buntine@nicta.com.au
Timothy M. La Pira
Affiliation:
Department of Political Science, College of Charleston, Charleston, SC 29424. e-mail: lapirat@cofc.edu
Holly Brasher*
Affiliation:
Department of Government, University of Alabama at Birmingham, Birmingham, AL 35294-1152
*
e-mail: hbrasher@uab.edu (corresponding author)
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.

References

Banzhaf, John F. III. 1965. Weighted voting doesn't work: A mathematical analysis. Rutgers Law Review 19(2): 317–43.Google Scholar
Buntine, Wray, and Jakulin, Aleks. 2004. Applying discrete PCA in data analysis. In Proceedings of 20th Conference on Uncertainty in Artificial Intelligence (UAI), eds. Chickering, M. and Halpern, J., 59–66. Alberta, Canada: Banff. http://www.hiit.fi/u/buntine/uai2004.html.Google Scholar
Clinton, Joshua D., Jackman, Simon, and Rivers, Douglas. 2004a. ‘The most liberal senator? Analyzing and interpreting congressional roll calls’. Political Science and Politics 37(4): 805–11.CrossRefGoogle Scholar
Clinton, Joshua D., Jackman, Simon, and Rivers, Douglas. 2004b. The statistical analysis of roll call voting: A unified approach. American Political Science Review 98: 355–70. http://www.princeton.edu/∼clinton/CJR_APSR2004.pdf.Google Scholar
Davis, Otto A., Hinich, Melvin J., and Ordeshook, Peter C. 1970. An expository development of a mathematical model of the electoral process. American Political Science Review 64: 426–48.Google Scholar
Gelman, Andrew. 2003. Forming voting blocs and coalitions as a prisoner's dilemma: A possible theoretical explanation for political instability. Contributions to Economic Analysis and Policy 2(1): Article 13. http://www.stat.columbia.edu/gelman/research/published/blocs.pdf.Google Scholar
Gelman, Andrew, Katz, Jonathan N., and Bafumi, Joseph. 2004. Standard voting power indexes don't work: An empirical analysis. British Journal of Political Science 34: 657–74.CrossRefGoogle Scholar
Geman, Stuart, and Geman, Donald. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian relation of images. IEEE Transactions on Pattern Analysis And Machine Intelligence 6: 721–41.Google Scholar
Hix, Simon, Noury, Abdul, and Roland, Gerard. 2005. Power to the parties: Cohesion and competition in the European parliament, 1979–2001. British Journal of Political Science 35(2): 209–34.Google Scholar
Hofmann, Thomas. 1999. Probabilistic latent semantic indexing. In ACM SIGIR Conference on Research and Development in Information Retrieval Anchive Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Table of Contents Berkeley, California, United States, 50–7. New York: ACM.Google Scholar
Jakulin, A., and Bratko, I. 2003. Analyzing attribute dependencies. In Proceedings of Principles of Knowledge Discovery in Data (PKDD), eds. Lavrac, N., Gamberger, D., Blockeel, H., and Todorovski, L., 229–240. Berlin: Springer-Verlag.Google Scholar
Kaufman, Leonard, and Rousseeuw, Peter J. 1990. Finding groups in data: An introduction to cluster analysis. New York: Wiley.CrossRefGoogle Scholar
Lawson, Brian L., Orrison, Michael, and Uminsky, David. 2006. Spectral Analysis of the Supreme Court. Mathematics Magazine 79 (December): 340–46.Google Scholar
de Leeuw, Jan. 2003. Principal component analysis of binary data: Applications to roll-call-analysis. Technical Report 364, UCLA Department of Statistics. http://preprints.stat.ucla.edu/download.php?paper=364.Google Scholar
Lee, Daniel, and Sebatian Seung, H. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401: 788–91.Google Scholar
McCarty, Nolan M., Poole, Keith T., and Rosenthal, Howard. 2001. The hunt for party discipline in congress. American Political Science Review 95: 673–87. http://voteview.uh.edu/d011000merged.pdf.Google Scholar
Poole, K. T. 2000. Non-parametric unfolding of binary choice data. Political Analysis 8: 211–32. http://voteview.uh.edu/apsa2.pdf.Google Scholar
Poole, K. T., and Rosenthal, H. 1997. Congress: A political-economic history of roll call voting. New York: Oxford University Press.Google Scholar
Poole, K. T., and Rosenthal, H. 2007. Ideology and congress: Second revised edition of congress: A political-economic history of roll call voting. New Brunswick, NJ: Transaction Publishers.Google Scholar
Press, William H., Teukolsky, Saul A., Vetterling, William T., and Flannery, Brian P. 1992. Numerical recipes in C. 2nd ed. Cambridge: Cambridge University Press.Google Scholar
Pritchard, Jonathan K., Stephens, Matthew, and Donnelly, Peter J. 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945–59.Google Scholar
Rajski, C. 1961. A metric space of discrete probability distributions. Information and Control 4: 373–7.Google Scholar
Rice, Stuart A. 1928. Quantitative methods in politics. New York: Knopf.Google Scholar
Shannon, Claude E. 1948. A mathematical theory of communication. Bell System Technical Journal 27: 379–423:623–56.Google Scholar
Tipping, Michael E., and Bishop, Chis M. 1999. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61(3): 611–22.CrossRefGoogle Scholar
Woodbury, Max A., and Manton, Kenneth G. 1982. A new procedure for analysis of medical classification. Methods Inf. Med 21: 210–20.Google Scholar