How Valid Are Trust Survey Measures? New Insights From Open-Ended Probing Data and Supervised Machine Learning

Bibliographic Data

ID2331176
AuthorsCamille Landesvatter (0000-0003-0156-5364, Mannheim Centre for European Social Research, University of Mannheim, Mannheim, Baden-Württemberg, Germany, corresponding author), Paul C Bauer (0000-0002-8382-9724, University of Freiburg, corresponding author)
Year2025
Volume54
Issue2
Pages534-564
Publication date2025-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methods & Research (JOURNAL)
Journal identifiersISSN: 0049-1241 • E-ISSN: 1552-8294
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/00491241241234871
OpenAlexW4393041870
LanguageEN
Citations received6
References cited54

Trust is a foundational concept of contemporary sociological theory. Still, empirical research on trust relies on a relatively small set of measures. These are increasingly debated, potentially undermining large swathes of empirical evidence. Drawing on a combination of open-ended probing data, supervised machine learning, and a U.S. representative quota sample, our study compares the validity of standard measures of generalized social trust with more recent, situation-specific measures of trust. We find that survey measures that refer to 'strangers' in their question wording best reflect the concept of generalized trust, also known as trust in unknown others. While situation-specific measures should have the desirable property of further reducing variation in associations, that is, producing more similar frames of reference across respondents, they also seem to increase associations with known others, which is undesirable. In addition, we explore to what extent trust survey questions may evoke negative associations. We find that there is indeed variation across measures, which calls for more research

Data science · Machine learning · Mathematics education · Supervised Learning · Artificial Intelligence · Computer Science · Electoral Systems and Political Participation · Psychology · Social Capital and Networks

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