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

Socpus ID

84861007640 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84861007640

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