(Mis)perceptions of Partisan Polarization in the American Public
Open accessAbstract
Few topics in public opinion research have attracted as much attention in recent years as partisan polarization in the American mass public. Yet, there has been considerably less investigation into whether people perceive the electorate to be polarized and the patterns of these perceptions. Building on work in social psychology, we argue that Americans perceive more polarization with respect to policy issues than actually exists, a phenomenon known as false polarization. Data from a nationally representative probability sample and a novel estimation strategy to make inferences about false polarization show that people significantly misperceive the public to be more divided along partisan lines than it is in reality. Also, people’s misperceptions of opposing partisans are larger than those about their own party. We discuss the implications of these empirical patterns for American electoral politics.
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People tend to overestimate the degree of polarization between political groups.
“People also tend to overestimate the degree of polarization (Levendusky & Malhotra 2016, Westfall et al. 2015) and animosity (Mernyk et al. 2022, Ruggeri et al. 2021) between political groups, which may be exacerbated by the abundance of divisive content they see online.”
From Social Media and Morality. (2024) · cites this paper as Levendusky & Malhotra 2016
Most people are more moderate than people think and consume news from the same sources on social media, while those who engage on social media are the most engaged partisans.
“These false beliefs help explain why social media can fuel (moral) conflicts, as most people are more moderate than people think (Levendusky & Malhotra 2016) and consume news from the same sources on social media (Guess 2021), while those who engage on social media are the most engaged partisans (Settle 2018).”
From Social Media and Morality. (2024) · cites this paper as Levendusky & Malhotra 2016
American citizens estimate that U.S. parties are 20% further apart than they actually are.
“As other research has shown, such as Levendusky and Malhotra (2016:384), American citizens estimate that U.S. parties are 20% further apart than they actually are.”
From ¿Demasiado lejos? Cómo la polarización mal percibida alimenta la polarización afectiva en España (2025) · cites this paper as Levendusky and Malhotra (2016:384)
Partisans might represent their adversaries as more obstructionist, more ideologically extreme, more prejudiced against the in-group, or more violent than they actually are.
“Partisans might also represent their adversaries as more obstructionist95, more ideologically extreme122,151–153, more prejudiced against the in-group55, or more violent154 than they actually are.”
From Cognitive–motivational mechanisms of political polarization in social-communicative contexts (2022) · cites this paper as [152]
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- Authors
- Levendusky, M. S., & Malhotra, N.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Levendusky, Matthew S.
- Venue
- Public Opinion Quarterly
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-10-29
- Volume
- 80
- Issue
- S1
- Pages
- 378-391
- ISSN
- 0033-362X (ISSN-L)
- 1537-5331
- OpenAlex
- W2256445737
- Cited by
- 356
- References
- 25
- Citations by year
- 2026: 31 · 2025: 46 · 2024: 44 · 2023: 41 · 2022: 49 · 2021: 69 · 2020: 31 · 2019: 11 · 2018: 25 · 2017: 8 · 2016: 1
- FWCI
- 73.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Public Opinion Quarterly (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Science Foundation (0818839)
- Topics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Keywords
- partisan polarization, American public opinion, electoral politics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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