Saving Face
Identifying Voter Responses to Black Candidates and Female Candidates
Bibliographic Data
| ID | 7352186 |
|---|---|
| Authors | Yanna Krupnikov (0000-0002-4708-0074, Stony Brook University), Spencer Piston (0000-0001-9933-8295, Syracuse University), Nichole M Bauer (0000-0002-9797-7453, Davidson College) |
| Year | 2015 |
| Volume | 37 |
| Issue | 2 |
| Pages | 253-273 |
| Publication date | 2015-04-19 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0162-895X • E-ISSN: 1467-9221 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/pops.12261 |
| OpenAlex | W2257015508 |
| Language | EN |
| Citations received | 71 |
| References cited | 86 |
Much of what we know about the responses of voters to Black candidates and female candidates comes from experimental research. Yet the accuracy of experimental data can be threatened by the possibility that social desirability pressures contaminate self‐reporting. We address this threat in a project that considers psychological approaches to reducing social desirability pressures. Offering participants the opportunity to explain their decisions about sensitive subjects, such as voting for a Black or female candidate, can lessen social desirability pressures. We analyze this approach across three commonly used samples: undergraduate, adult convenience, and adult national. Our results suggest that existing experimental research overestimates voter support for Black and female candidates, but these issues can be mitigated with the simple innovation presented here
Black male · Face (sociological concept · Political science · Politics · Social desirability · Social desirability bias · Social science · Sociology · Voting · Applied Psychology · Electoral Systems and Political Participation · Gender Politics and Representation · Gender Studies · Psychology · Social and Intergroup Psychology · Social Psychology
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| Unique citing works | 71 |
|---|---|
| Citations per year | 7,1 |
| Citation span | 2016 - 2026 (11) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 71 |