Title
Evaluating Voter-Candidate Proximity In A Non-Euclidean Space
Abstract
When applying the proximity model in electoral studies, scholars face the challenge of estimating voter-candidate proximity when voters' responses to issues/policies in a multidimensional policy space are correlated. In this article, we contend that voters' correlated evaluations can be captured by the structure of a non-orthogonal policy space. After orthogonalizing such a space using the Gram-Schmidt process, we can improve our estimation of the spatial distance between voters and candidates. Moreover, our study suggests that in multidimensional space neither the city-block nor the Euclidean distance is ideal for estimating proximity. We propose to use a generalized parametric Minkowski model and our analysis demonstrates that the most appropriate distance metric for a particular study is an empirical issue that hinges on the particular structure of a dataset. © 2011 Copyright Elections, Public Opinion & Parties.
Publication Date
11-1-2011
Publication Title
Journal of Elections, Public Opinion and Parties
Volume
21
Issue
4
Number of Pages
497-521
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1080/17457289.2011.609619
Copyright Status
Unknown
Socpus ID
84861007640 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84861007640
STARS Citation
Ye, Min; Li, Quan; and Leiker, Kyle W., "Evaluating Voter-Candidate Proximity In A Non-Euclidean Space" (2011). Scopus Export 2010-2014. 1918.
https://stars.library.ucf.edu/scopus2010/1918