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Circular Data in Political Science and How to Handle It

Published online by Cambridge University Press:  04 January 2017

Jeff Gill and
Dominik Hangartner
Jeff Gill
Affiliation:
Center for Applied Statistics, Department of Political Science, Washington University, One Brookings Dr., Seigle Hall, LL-085, Box 1203, St. Louis, MO 63130-4899
Dominik Hangartner
Affiliation:
Institute for Political Science, University of Bern, Lerchenweg 36, Bern, BE 3012 Switzerland and Center for Applied Statistics, Washington University, One Brookings Dr., Seigle Hall, LL-085, Box 1203, St. Louis, MO 63130-4899
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Abstract

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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.

Information

Type
Research Article
Information
Political Analysis , Volume 18 , Issue 3 , Summer 2010, pp. 316 - 336
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

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