JOURNAL ARTICLE

Always Silent? Exploring Contextual Conditions for Nonresponses to Vote Intention Questions at the 2020 U.S. Presidential Election.

  • Published In: International Journal of Public Opinion Research, 2023, v. 35, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Camatarri, Stefano; Luartz, Lewis A; Gallina, Marta 3 of 3

Abstract

This article examines how local political climate influences survey respondents' willingness to disclose their vote intentions in the 2020 U.S. presidential election, focusing on the phenomenon of vote intention nonresponse. Using logistic regression analyses on data from the 2020 Cooperative Election Study matched with 2016 county-level presidential election results from the MIT Election Data and Science Lab, the study finds that conservative-leaning voters are more likely to withhold their vote preference in counties where Democratic support was stronger, consistent with a social desirability bias or "spiral of silence" effect. Individual factors such as political interest, ideology, and ethnicity also affect nonresponse rates, but the interaction between ideology and local partisan context is key to understanding reticence in vote disclosure. The findings highlight the importance of considering both individual and contextual factors in electoral survey research and suggest that social pressures in politically heterogeneous environments may contribute to polling inaccuracies.

Additional Information

  • Source:International Journal of Public Opinion Research. 2023/09, Vol. 35, Issue 3, p1
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:0954-2892
  • DOI:10.1093/ijpor/edad025
  • Accession Number:171896168
  • Copyright Statement:Copyright of International Journal of Public Opinion Research is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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