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dc.contributor.authorTutic, Andreas
dc.date.accessioned2025-02-26T13:13:28Z
dc.date.available2025-02-26T13:13:28Z
dc.date.created2024-12-18T13:00:58Z
dc.date.issued2024
dc.identifier.issn0304-2421
dc.identifier.urihttps://hdl.handle.net/11250/3180666
dc.description.abstractThis study employs Bayesian inference to explore class-based differences in moral judgment. Based on the dual-process perspective in interdisciplinary action theory, we estimate in a first step a process model which differentiates parametrically between emotionally driven deontological, deliberatively driven utilitarian, and residual judgmental inclinations. In a second step, our estimates of these parameters are correlated via beta regressions with indicators of social class and thinking dispositions. We find a considerable association between social class, specifically income, and deontological inclinations, whereas consequentialist inclinations correlate with thinking dispositions but not with social class. This research underscores the utility of Bayesian estimation in closing the gap between theoretical and statistical modelling. Employing this approach enhances our understanding of the nuanced interplay between intuitive and deliberative processes in moral judgment and, more generally, offers a promising direction for advancing sociological action theory.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleClass-based differences in moral judgment: A bayesian approachen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1007/s11186-024-09584-1
dc.identifier.cristin2332002
dc.source.journalTheory and societyen_US
dc.source.pagenumber1441-1472en_US
dc.identifier.citationTheory and society. 2024, 53, 1441-1472.en_US
dc.source.volume53en_US


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