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Measuring ideology in Congress

In: Handbook of Social Choice and Voting

Author

Listed:
  • Christopher Hare
  • Keith T. Poole

Abstract

This Handbook provides an overview of interdisciplinary research related to social choice and voting that is intended for a broad audience. Expert contributors from various fields present critical summaries of the existing literature, including intuitive explanations of technical terminology and well-known theorems, suggesting new directions for research.

Suggested Citation

  • Christopher Hare & Keith T. Poole, 2015. "Measuring ideology in Congress," Chapters, in: Jac C. Heckelman & Nicholas R. Miller (ed.), Handbook of Social Choice and Voting, chapter 18, pages 327-346, Edward Elgar Publishing.
  • Handle: RePEc:elg:eechap:15584_18
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    File URL: https://www.elgaronline.com/view/9781783470723.00027.xml
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    References listed on IDEAS

    as
    1. Hoadley, John F., 1980. "The Emergence of Political Parties in Congress, 1789–1803," American Political Science Review, Cambridge University Press, vol. 74(3), pages 757-779, September.
    2. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    3. McCarty, Nolan M & Poole, Keith T, 1995. "Veto Power and Legislation: An Empirical Analysis of Executive and Legislative Bargaining from 1961 to 1986," Journal of Law, Economics, and Organization, Oxford University Press, vol. 11(2), pages 282-312, October.
    4. David F. Scott & William G. Jens & Raymond E. Spudeck, 1991. "Analysis," Challenge, Taylor & Francis Journals, vol. 34(6), pages 58-60, November.
    5. Simon Hix & Abdul Noury & Gérard Roland, 2006. "Dimensions of Politics in the European Parliament," American Journal of Political Science, John Wiley & Sons, vol. 50(2), pages 494-520, April.
    6. Lewis, Jeffrey B. & Poole, Keith T., 2004. "Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap," Political Analysis, Cambridge University Press, vol. 12(2), pages 105-127, April.
    7. J. Carroll & Jih-Jie Chang, 1970. "Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 283-319, September.
    8. Voeten, Erik, 2000. "Clashes in the Assembly," International Organization, Cambridge University Press, vol. 54(2), pages 185-215, April.
    9. Anthony Downs, 1957. "An Economic Theory of Political Action in a Democracy," Journal of Political Economy, University of Chicago Press, vol. 65, pages 135-135.
    10. Jackman, Simon, 2001. "Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking," Political Analysis, Cambridge University Press, vol. 9(3), pages 227-241, January.
    11. Howard Rosenthal & Erik Voeten, 2004. "Analyzing Roll Calls with Perfect Spatial Voting: France 1946–1958," American Journal of Political Science, John Wiley & Sons, vol. 48(3), pages 620-632, July.
    12. Quinn, Kevin M., 2004. "Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses," Political Analysis, Cambridge University Press, vol. 12(4), pages 338-353.
    13. Royce Carroll & Jeffrey B. Lewis & James Lo & Keith T. Poole & Howard Rosenthal, 2013. "The Structure of Utility in Spatial Models of Voting," American Journal of Political Science, John Wiley & Sons, vol. 57(4), pages 1008-1028, October.
    14. Berinsky, Adam J. & Lewis, Jeffrey B., 2007. "An Estimate of Risk Aversion in the U.S. Electorate," Quarterly Journal of Political Science, now publishers, vol. 2(2), pages 139-154, May.
    15. Clinton, Joshua D. & Meirowitz, Adam, 2003. "Integrating Voting Theory and Roll Call Analysis: A Framework," Political Analysis, Cambridge University Press, vol. 11(4), pages 381-396.
    16. Jessee, Stephen A., 2009. "Spatial Voting in the 2004 Presidential Election," American Political Science Review, Cambridge University Press, vol. 103(1), pages 59-81, February.
    17. Lauderdale, Benjamin E., 2010. "Unpredictable Voters in Ideal Point Estimation," Political Analysis, Cambridge University Press, vol. 18(2), pages 151-171, April.
    18. Dougherty, Keith L. & Heckelman, Jac C., 2006. "A Pivotal Voter from a Pivotal State: Roger Sherman at the Constitutional Convention," American Political Science Review, Cambridge University Press, vol. 100(2), pages 297-302, May.
    19. Schofield, Normal & Martin, Andrew D. & Quinn, Kevin M. & Whitford, Andrew B., 1998. "Multiparty Electoral Competition in the Netherlands and Germany: A Model Based on Multinomial Probit," Public Choice, Springer, vol. 97(3), pages 257-293, December.
    20. Poole, Keith & Lewis, Jeffrey B. & Lo, James & Carroll, Royce, 2011. "Scaling Roll Call Votes with wnominate in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i14).
    21. Michael Peress, 2012. "Identification of a Semiparametric Item Response Model," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 223-243, April.
    22. Shawn Treier & Simon Jackman, 2008. "Democracy as a Latent Variable," American Journal of Political Science, John Wiley & Sons, vol. 52(1), pages 201-217, January.
    23. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(2), pages 134-153, April.
    24. Poole, Keith T., 2000. "Nonparametric Unfolding of Binary Choice Data," Political Analysis, Cambridge University Press, vol. 8(3), pages 211-237, March.
    25. Jackman, Simon, 2000. "Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation," Political Analysis, Cambridge University Press, vol. 8(4), pages 307-332, July.
    26. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
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