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The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis

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  • Xiner Zhou
  • Hans-Georg Müller

Abstract

We study the U.S. Supreme Court dynamics by analyzing the temporal evolution of the underlying policy positions of the Supreme Court Justices as reflected by their actual voting data, using functional data analysis methods. The proposed fully flexible nonparametric method makes it possible to dissect the time-dynamics of policy positions at the level of individual Justices, as well as providing a comprehensive view of the ideology evolution over the history of Supreme Court since its establishment. In addition to quantifying individual Justice’s policy positions, we uncover average changes over time and also the major patterns of change over time. Additionally, our approach allows for representing highly complex dynamic trajectories by a few principal components which complements other models of analyzing and predicting court behavior.

Suggested Citation

  • Xiner Zhou & Hans-Georg Müller, 2022. "The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-21, July.
  • Handle: RePEc:plo:pone00:0269598
    DOI: 10.1371/journal.pone.0269598
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    References listed on IDEAS

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    1. Kevin M. Quinn, 2007. "Assessing Preference Change on the US Supreme Court," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 23(2), pages 365-385, June.
    2. Bailey, Michael A. & Maltzman, Forrest, 2008. "Does Legal Doctrine Matter? Unpacking Law and Policy Preferences on the U.S. Supreme Court," American Political Science Review, Cambridge University Press, vol. 102(3), pages 369-384, August.
    3. Daniel Martin Katz & Michael J Bommarito II & Josh Blackman, 2017. "A general approach for predicting the behavior of the Supreme Court of the United States," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
    4. Kehui Chen & Xiaoke Zhang & Alexander Petersen & Hans-Georg Müller, 2017. "Quantifying Infinite-Dimensional Data: Functional Data Analysis in Action," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 582-604, December.
    5. Zhang, Xiaoke & Wang, Jane-Ling, 2018. "Optimal weighting schemes for longitudinal and functional data," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 165-170.
    6. Londregan, John, 1999. "Estimating Legislators' Preferred Points," Political Analysis, Cambridge University Press, vol. 8(1), pages 35-56, January.
    7. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    8. Peter Hall & Hans‐Georg Müller & Fang Yao, 2008. "Modelling sparse generalized longitudinal observations with latent Gaussian processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 703-723, September.
    9. 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.
    10. Brandon L. Bartels & Andrew J. O'Geen, 2015. "The Nature of Legal Change on the U.S. Supreme Court: Jurisprudential Regimes Theory and Its Alternatives," American Journal of Political Science, John Wiley & Sons, vol. 59(4), pages 880-895, October.
    11. Bailey, Michael & Chang, Kelly H, 2001. "Comparing Presidents, Senators, and Justices: Interinstitutional Preference Estimation," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 17(2), pages 477-506, October.
    12. 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.
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