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Estimating Voter Preference Distributions from Individual-Level Voting Data

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  • Lewis, Jeffrey B.

Abstract

This paper presents a method for inferring the distribution of voter ideal points on a single dimension from individual-level binary choice data. The statistical model and estimation technique draw heavily on the psychometric literature on test taking and, in particular, on the work of Bock and Aitkin (1981) and are similar to several recent methods of estimating legislative ideal points (Londregan 2000; Bailey 2001). I present Monte Carlo results validating the method. The method is then applied to determining the partisan and ideological basis of support for presidential candidates in 1992 and to U.S. mass and congressional partisan realignment on abortion policy since 1973.

Suggested Citation

  • Lewis, Jeffrey B., 2001. "Estimating Voter Preference Distributions from Individual-Level Voting Data," Political Analysis, Cambridge University Press, vol. 9(3), pages 275-297, January.
    • Handle: RePEc:cup:polals:v:9:y:2001:i:03:p:275-297_00
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    Cited by:

    1. Keith Krehbiel & Zachary Peskowitz, 2015. "Legislative organization and ideal-point bias," Journal of Theoretical Politics, , vol. 27(4), pages 673-703, October.
    2. Elisabeth R. Gerber & Jeffrey B. Lewis, 2004. "Beyond the Median: Voter Preferences, District Heterogeneity, and Political Representation," Journal of Political Economy, University of Chicago Press, vol. 112(6), pages 1364-1383, December.
    3. Arianna Degan, 2003. "A Dynamic Model of Voting," PIER Working Paper Archive 04-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 May 2004.
    4. Christopher Hare & Tzu-Ping Liu & Robert N. Lupton, 2018. "What Ordered Optimal Classification reveals about ideological structure, cleavages, and polarization in the American mass public," Public Choice, Springer, vol. 176(1), pages 57-78, July.
    5. Kuriwaki, Shiro, 2020. "A Clustering Approach for Characterizing Voter Types: An Application to High-Dimensional Ballot and Survey Data," OSF Preprints v3rhz, Center for Open Science.
    6. Richard F. Potthoff, 2018. "Estimating Ideal Points from Roll-Call Data: Explore Principal Components Analysis, Especially for More Than One Dimension?," Social Sciences, MDPI, vol. 7(1), pages 1-27, January.
    7. Krehbiel, Keith & Peskowitz, Zachary, 2012. "Legislative Organization and Ideal-Point Bias," Research Papers 2124, Stanford University, Graduate School of Business.
    8. Funk, Patricia & Gathmann, Christina, 2013. "Voter preferences, direct democracy and government spending," European Journal of Political Economy, Elsevier, vol. 32(C), pages 300-319.

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