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Estimating Constituency Preferences from Sparse Survey Data Using Auxiliary Geographic Information

Published online by Cambridge University Press:  04 January 2017

Peter Selb and
Simon Munzert
Peter Selb*
Affiliation:
Department of Politics and Public Administration and Center for Quantitative Methods and Survey Research, University of Konstanz, PO Box D85, 78457 Konstanz, Germany
Simon Munzert
Affiliation:
Center for Quantitative Methods and Survey Research, University of Konstanz, PO Box D85, 78457 Konstanz, Germany, e-mail: simon.munzert@uni-konstanz.de
*
e-mail: peter.selb@uni-konstanz.de
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Abstract

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Measures of constituency preferences are of vital importance for the study of political representation and other research areas. Yet, such measures are often difficult to obtain. Previous survey-based estimates frequently lack precision and coverage due to small samples, rely on questionable assumptions or require detailed auxiliary information about the constituencies' population characteristics. We propose an alternative Bayesian hierarchical approach that exploits minimal geographic information readily available from digitalized constituency maps. If at hand, social background data are easily integrated. To validate the method, we use national polls and district-level results from the 2009 German Bundestag election, an empirical case for which detailed structural information is missing.

Information

Type
Articles
Information
Political Analysis , Volume 19 , Issue 4 , Autumn 2011, pp. 455 - 470
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: We are grateful to Michael Herrmann, Thomas Hinz, Winfried Pohlmeier, Susumu Shikano, Marco Steenbergen as well as the editors and reviewers of this journal for helpful comments and support. Supplementary materials for this article are available on the Political Analysis Web site. Appendix is available from the Political Analysis Dataverse at http://hdl.handle.net/1902.1/16363.

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Supplementary Material 1

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