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

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 dissec...

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Detalles Bibliográficos
Autores principales: Zhou, Xiner, Müller, Hans-Georg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269944/
https://www.ncbi.nlm.nih.gov/pubmed/35802688
http://dx.doi.org/10.1371/journal.pone.0269598
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author Zhou, Xiner
Müller, Hans-Georg
author_facet Zhou, Xiner
Müller, Hans-Georg
author_sort Zhou, Xiner
collection PubMed
description 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.
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spelling pubmed-92699442022-07-09 The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis Zhou, Xiner Müller, Hans-Georg PLoS One Research Article 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. Public Library of Science 2022-07-08 /pmc/articles/PMC9269944/ /pubmed/35802688 http://dx.doi.org/10.1371/journal.pone.0269598 Text en © 2022 Zhou, Müller https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhou, Xiner
Müller, Hans-Georg
The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis
title The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis
title_full The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis
title_fullStr The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis
title_full_unstemmed The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis
title_short The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis
title_sort dynamics of ideology drift among u.s. supreme court justices: a functional data analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269944/
https://www.ncbi.nlm.nih.gov/pubmed/35802688
http://dx.doi.org/10.1371/journal.pone.0269598
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