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Cited by 390

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

Candidate Qualities through a Partisan Lens: A Theory of Trait Ownership

Hayes, Danny·2005·American Journal of Political Science

Abstract

UsingPetrocik's (1996)theory of issue ownership as a point of departure, I develop and test a theory of “trait ownership” that provides an explanation for the origins of candidate trait perceptions and illustrates an important way that candidates affect voters. Specifically, I argue for a direct connection between the issues owned by a political party and evaluations of the personal attributes of its candidates. As a result, the American public views Republicans as stronger leaders and more moral, while Democrats hold advantages on compassion and empathy. I also draw on “expectations gap” arguments from psychology and political science to demonstrate how a candidate may gain an electoral advantage by successfully “trespassing” on his opponent's trait territory. National Election Studies data from the 1980–2004 presidential elections are used to demonstrate the existence, durability, and effects of trait ownership in contemporary American political campaigns.

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Crossref · 10.1111/j.1540-5907.2005.00163.x

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Metadata

Authors
Hayes, D.
Venue
American Journal of Political Science
Publisher
Wiley
Published
2005-09-27
Volume
49
Issue
4
Pages
908-923
ISSN
  • 0092-5853 (ISSN-L)
  • 1540-5907
OpenAlex
W2027431738
Cited by
390
References
2
FWCI
37.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Candidate Qualities through a Partisan Lens: A Theory of Trait Ownership. https://app.cochat.ai/papers/10.1111/j.1540-5907.2005.00163.x
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Hayes, D. (2005). Candidate Qualities through a Partisan Lens: A Theory of Trait Ownership. American Journal of Political Science. https://doi.org/10.1111/j.1540-5907.2005.00163.x

Data sources

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.