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Cited by 389

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Bringing the Candidate into Models of Candidate Evaluation

Funk, Carolyn L.·1999·The Journal of Politics

Abstract

Candidate characteristics are often treated as a lump sum that can be pulled apart but ultimately sum together in models of candidate evaluation. The present study asks, first, whether distinctions among traits are useful for models of candidate evaluation. Second, it considers whether the role of substantive trait dimensions in overall evaluation is uniform across candidates or varies by candidate and electoral context. Latent variable structural equation models are used to test the impact of trait dimensions on thermometer ratings of presidential candidates from 1984 to 1996. Results support the separation of traits into substantive content dimensions. Trait dimensions vary in their effect on candidate evaluations depending on the candidate and election year. This is consistent with a model of attitude response based on changing considerations. These results suggest that as candidates and campaigns call attention to different underlying trait dimensions, the bases for overall evaluation vary accordingly.

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Metadata

Authors
Funk, C. L.
Venue
The Journal of Politics
Published
1999-08
Volume
61
Issue
3
Pages
700-720
ISSN
  • 0022-3816 (ISSN-L)
  • 1468-2508
OpenAlex
W2096637742
Cited by
389
References
2
FWCI
8.22
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bringing the Candidate into Models of Candidate Evaluation. https://app.cochat.ai/papers/10.2307/2647824
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Funk, C. L. (1999). Bringing the Candidate into Models of Candidate Evaluation. The Journal of Politics. https://doi.org/10.2307/2647824

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.