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Voting Experiences, Perceptions of Fraud, and Voter Confidence

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  • R. Michael Alvarez
  • Jian Cao
  • Yimeng Li

Abstract

Objective Assuring voter confidence is important for the legitimacy of democratic elections. In this article, we test four hypotheses about the correlates of voter confidence at various levels of election administration. Methods We take advantage of a large online survey of registered voters in a single election jurisdiction, Orange County (CA), which was implemented immediately after the November 2018 midterm elections. We measure voting experience and social media usage using item response theory as well as voters' perceptions of various election or voter fraud, and estimate their association with voter confidence via logistic regression models. Results Our results show that voters who cast mail ballots are less confident about their own votes being counted correctly than in‐person voters. For both types of voters, those who have poor experiences with the voting process are much less likely to report confidence in the election. We also find that voters who have strong concerns about election fraud are less likely to report being confident in the election. Our last result indicates that information from news and social media is associated with a decline in voter confidence in election administration at the national level. Conclusion Given the many conversations about election fraud that have occurred since the 2016 presidential election, this research provides important implications for election administration in future elections in the United States.

Suggested Citation

  • R. Michael Alvarez & Jian Cao & Yimeng Li, 2021. "Voting Experiences, Perceptions of Fraud, and Voter Confidence," Social Science Quarterly, Southwestern Social Science Association, vol. 102(4), pages 1225-1238, July.
  • Handle: RePEc:bla:socsci:v:102:y:2021:i:4:p:1225-1238
    DOI: 10.1111/ssqu.12940
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    References listed on IDEAS

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    1. Lonna Rae Atkeson & R. Michael Alvarez & Thad E. Hall & J. Andrew Sinclair, 2014. "Balancing Fraud Prevention and Electoral Participation: Attitudes Toward Voter Identification," Social Science Quarterly, Southwestern Social Science Association, vol. 95(5), pages 1381-1398, December.
    2. Cranmer, Skyler J. & Gill, Jeff, 2013. "We Have to Be Discrete About This: A Non-Parametric Imputation Technique for Missing Categorical Data," British Journal of Political Science, Cambridge University Press, vol. 43(2), pages 425-449, April.
    3. Zhou, Xiang, 2019. "Hierarchical Item Response Models for Analyzing Public Opinion," Political Analysis, Cambridge University Press, vol. 27(4), pages 481-502, October.
    4. James Honaker & Gary King, 2010. "What to Do about Missing Values in Time‐Series Cross‐Section Data," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 561-581, April.
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    1. Geoffrey Layman & Frances Lee & Christina Wolbrecht, 2023. "Political Parties and Loser’s Consent in American Politics," The ANNALS of the American Academy of Political and Social Science, , vol. 708(1), pages 164-183, July.
    2. Mitchell Linegar & Betsy Sinclair & Sander van der Linden & R. Michael Alvarez, 2024. "Prebunking Elections Rumors: Artificial Intelligence Assisted Interventions Increase Confidence in American Elections," Papers 2410.19202, arXiv.org.

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