Ideologically biased evaluation of evidence - A signal-detection approach to measure individual differences.
Where this comes from
- Record sourced from PubMed, PMID 42726889.
- Also identified by DOI 10.1371/journal.pone.0357011.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
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.
Medical subject headings
- Politics
- Individuality
- Signal Detection, Psychological