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Affect, Not Ideology: The Heterogeneous Effects of Partisan Cues on Policy Support.

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Title: Affect, Not Ideology: The Heterogeneous Effects of Partisan Cues on Policy Support.
Authors: Fuller, Sam (AUTHOR), de la Cerda, Nicolás (AUTHOR), Rametta, Jack T. (AUTHOR)
Source: Political Behavior. Mar2026, Vol. 48 Issue 1, p273-297. 25p.
Subjects: Multidimensional scaling, Political psychology, Policy sciences, Questionnaires, Political participation, Causal inference, Machine learning, Partisanship
Abstract: How do individuals process political information? What behavioral mechanisms drive partisan bias? In this paper, we evaluate the extent to which partisan bias is driven by affect or ideology in a three-pronged approach informed by both psychological theories and recent advances in methodology. First, we use a novel survey experiment designed to disentangle the competing mechanisms of ideology and partisan affect. Second, we leverage multidimensional scaling methods for latent variable estimation for both partisan affect and ideology. Third, we employ a principled machine learning method, causal forest, to detect and estimate heterogeneous treatment effects. Contrary to previous literature, we find that affect is the sole moderator of partisan cueing processes, and only for out-party cues. These findings not only contribute to the literature on political behavior, but underscore the importance of careful measurement and robust subgroup analysis. [ABSTRACT FROM AUTHOR]
Copyright of Political Behavior is the property of Springer Nature 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Affect, Not Ideology: The Heterogeneous Effects of Partisan Cues on Policy Support.
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  Data: <searchLink fieldCode="AR" term="%22Fuller%2C+Sam%22">Fuller, Sam</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22de+la+Cerda%2C+Nicolás%22">de la Cerda, Nicolás</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rametta%2C+Jack+T%2E%22">Rametta, Jack T.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Political+Behavior%22">Political Behavior</searchLink>. Mar2026, Vol. 48 Issue 1, p273-297. 25p.
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  Data: How do individuals process political information? What behavioral mechanisms drive partisan bias? In this paper, we evaluate the extent to which partisan bias is driven by affect or ideology in a three-pronged approach informed by both psychological theories and recent advances in methodology. First, we use a novel survey experiment designed to disentangle the competing mechanisms of ideology and partisan affect. Second, we leverage multidimensional scaling methods for latent variable estimation for both partisan affect and ideology. Third, we employ a principled machine learning method, causal forest, to detect and estimate heterogeneous treatment effects. Contrary to previous literature, we find that affect is the sole moderator of partisan cueing processes, and only for out-party cues. These findings not only contribute to the literature on political behavior, but underscore the importance of careful measurement and robust subgroup analysis. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Political Behavior is the property of Springer Nature 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.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11109-025-10030-w
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        Text: English
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      – SubjectFull: Causal inference
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      – SubjectFull: Partisanship
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              Text: Mar2026
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