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Scaling Politically Meaningful Dimensions Using Texts and Votes

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  • Benjamin E. Lauderdale
  • Tom S. Clark

Abstract

Item response theory models for roll‐call voting data provide political scientists with parsimonious descriptions of political actors' relative preferences. However, models using only voting data tend to obscure variation in preferences across different issues due to identification and labeling problems that arise in multidimensional scaling models. We propose a new approach to using sources of metadata about votes to estimate the degree to which those votes are about common issues. We demonstrate our approach with votes and opinion texts from the U.S. Supreme Court, using latent Dirichlet allocation to discover the extent to which different issues were at stake in different cases and estimating justice preferences within each of those issues. This approach can be applied using a variety of unsupervised and supervised topic models for text, community detection models for networks, or any other tool capable of generating discrete or mixture categorization of subject matter from relevant vote‐specific metadata.

Suggested Citation

  • Benjamin E. Lauderdale & Tom S. Clark, 2014. "Scaling Politically Meaningful Dimensions Using Texts and Votes," American Journal of Political Science, John Wiley & Sons, vol. 58(3), pages 754-771, July.
    • Handle: RePEc:wly:amposc:v:58:y:2014:i:3:p:754-771
    DOI: 10.1111/ajps.12085
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    Cited by:

    1. Bonica, Adam & Chilton, Adam S. & Sen, Maya, 2015. "The Political Ideologies of American Lawyers," Working Paper Series 15-049, Harvard University, John F. Kennedy School of Government.
    2. Keith Carlson & Michael A. Livermore & Daniel N. Rockmore, 2020. "The Problem of Data Bias in the Pool of Published U.S. Appellate Court Opinions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(2), pages 224-261, June.
    3. Bonica, Adam & Sen, Maya, 2017. "The Politics of Selecting the Bench from the Bar: The Legal Profession and Partisan Incentives to Introduce Ideology into Judicial Selection," Working Paper Series rwp17-048, Harvard University, John F. Kennedy School of Government.
    4. Spruk, Rok & Kovac, Mitja, 2019. "Replicating and extending Martin-Quinn scores," International Review of Law and Economics, Elsevier, vol. 60(C).
    5. Hausladen, Carina I. & Schubert, Marcel H. & Ash, Elliott, 2020. "Text classification of ideological direction in judicial opinions," International Review of Law and Economics, Elsevier, vol. 62(C).
    6. Sylvester Eijffinger & Ronald Mahieu & Louis Raes, 2016. "Monetary Policy Committees, Voting Behavior and Ideal Points," BAFFI CAREFIN Working Papers 1628, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    7. Joshua Lilley & Stuart Townley, 2024. "Tackling transparency in UK politics: application of large language models to clustering and classification of UK parliamentary divisions," Journal of Computational Social Science, Springer, vol. 7(3), pages 2563-2589, December.

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