Ideologically biased evaluation of evidence - A signal-detection approach to measure individual differences

In a context of increasing political polarization, ideological bias poses a challenge to evidence-based reasoning and the evaluation of factual information. However, its systematic study is hindered by the lack of reliable and valid measurement tools and by the frequent conflation of ideological bias with ideological stance. Addressing this gap, we introduce the Ideological Bias Expression (IBE) scale, a novel measure grounded in signal detection theory that disentangles discrimination accuracy from ideological bias in individuals' evaluations of evidence. Across two studies using German samples (NStudy1 = 446; NStudy2 = 1,359), we provide initial validation of the IBE scale across two distinct political issue domains (climate change/energy & domestic security/migration). Results indicate that the IBE scale is an effective tool for assessing individual differences in biased evidence evaluation, providing a foundation for future research on its determinants and potential interventions to promote more evidence-based public discourse. Using the scale, we find evidence that ideological bias is empirically distinct from knowledge-related factors, including formal education and political knowledge, indicating that such bias cannot be reduced to deficits in knowledge or reasoning alone. Moderate coherence across two issue domains also suggests that biased evidence evaluation is not purely issue-specific. Finally, in the German political context, ideological bias appears to reflect a combination of social identity dynamics and value-based cognition rather than partisan alignment alone.

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Publication Details

Journal
PLoS ONE
Published
2026-09-11
DOI
https://doi.org/10.1371/journal.pone.0357011
Primary Topic
Social and Intergroup Psychology
Type
article
Field-Weighted Citation Impact
0.00

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article

Ideologically biased evaluation of evidence - A signal-detection approach to measure individual differences

Carolin‐Theresa Ziemer, Christian Thiel, Tobias Rothmund, Christine Finn et al.
PLoS ONE
Social and Intergroup Psychology
article

Ideologically biased evaluation of evidence - A signal-detection approach to measure individual differences

Carolin‐Theresa Ziemer, Christian Thiel, Tobias Rothmund, Christine Finn, Arne Stolp
article en

Abstract

In a context of increasing political polarization, ideological bias poses a challenge to evidence-based reasoning and the evaluation of factual information. However, its systematic study is hindered by the lack of reliable and valid measurement tools and by the frequent conflation of ideological bias with ideological stance. Addressing this gap, we introduce the Ideological Bias Expression (IBE) scale, a novel measure grounded in signal detection theory that disentangles discrimination accuracy from ideological bias in individuals' evaluations of evidence. Across two studies using German samples (NStudy1 = 446; NStudy2 = 1,359), we provide initial validation of the IBE scale across two distinct political issue domains (climate change/energy & domestic security/migration). Results indicate that the IBE scale is an effective tool for assessing individual differences in biased evidence evaluation, providing a foundation for future research on its determinants and potential interventions to promote more evidence-based public discourse. Using the scale, we find evidence that ideological bias is empirically distinct from knowledge-related factors, including formal education and political knowledge, indicating that such bias cannot be reduced to deficits in knowledge or reasoning alone. Moderate coherence across two issue domains also suggests that biased evidence evaluation is not purely issue-specific. Finally, in the German political context, ideological bias appears to reflect a combination of social identity dynamics and value-based cognition rather than partisan alignment alone.

PLoS ONEVol. 21(9)
Science Communication Institute (US), Max Planck Institute for Human Development (DE), Institute of Engineering Science (RU), International Data Group (Sweden) (SE), Czech Academy of Sciences, Institute of Computer Science (CZ)
Bundesministerium für Bildung und Forschung
Reduced inequalities
Openalex Percentile: Top 5%
Social and Intergroup Psychology
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