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Circular Data in Political Science and How to Handle It

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  • Gill, Jeff
  • Hangartner, Dominik

Abstract

There has been no attention to circular (purely cyclical) data in political science research. We show that such data exist and are mishandled by models that do not take into account the inherently recycling nature of some phenomenon. Clock and calendar effects are the obvious cases, but directional data are observed as well. We describe a standard maximum likelihood regression modeling framework based on the von Mises distribution, then develop a general Bayesian regression procedure for the first time, providing an easy-to-use Metropolis-Hastings sampler for this approach. Applications include a chronographic analysis of U.S. domestic terrorism and directional party preferences in a two-dimensional ideological space for German Bundestag elections. The results demonstrate the importance of circular models to handle periodic and directional data in political science.

Suggested Citation

  • Gill, Jeff & Hangartner, Dominik, 2010. "Circular Data in Political Science and How to Handle It," Political Analysis, Cambridge University Press, vol. 18(3), pages 316-336, July.
    • Handle: RePEc:cup:polals:v:18:y:2010:i:03:p:316-336_01
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    Citations

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    Cited by:

    1. James E. Monogan & David M. Konisky & Neal D. Woods, 2017. "Gone with the Wind: Federalism and the Strategic Location of Air Polluters," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 257-270, April.
    2. Andrade, Ana C.C. & Pereira, Gustavo H.A. & Artes, Rinaldo, 2023. "The circular quantile residual," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    3. Moritz N. Lang & Lisa Schlosser & Torsten Hothorn & Georg J. Mayr & Reto Stauffer & Achim Zeileis, 2020. "Circular regression trees and forests with an application to probabilistic wind direction forecasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1357-1374, November.
    4. Ameijeiras-Alonso, Jose & Gijbels, Irène & Verhasselt, Anneleen, 2022. "On a family of two–piece circular distributions," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    5. Garnett P. McMillan & Timothy E. Hanson & Gabrielle Saunders & Frederick J. Gallun, 2013. "A two-component circular regression model for repeated measures auditory localization data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 515-534, August.
    6. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    7. Marco Marzio & Stefania Fensore & Agnese Panzera & Charles C. Taylor, 2018. "Circular local likelihood," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 921-945, December.
    8. Ohara, Kento & Hepplewhite, Matthew, 2024. "Just in Time? A Temporal Analysis of the Initiation of Legislation in Coalition Governments," I4R Discussion Paper Series 104, The Institute for Replication (I4R).
    9. Arnab Kumar Laha & A. C. Pravida Raja & K. C. Mahesh, 2019. "SB-robust estimation of mean direction for some new circular distributions," Statistical Papers, Springer, vol. 60(3), pages 877-902, June.
    10. Tanguiane, Andranick S., 2022. "Analysis of the 2021 Bundestag elections. 2/4. Political spectrum," Working Paper Series in Economics 152, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.

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