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ERIC Number: EJ1473580
Record Type: Journal
Publication Date: 2025-May
Pages: 31
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0049-1241
EISSN: EISSN-1552-8294
Available Date: 0000-00-00
How Valid Are Trust Survey Measures? New Insights from Open-Ended Probing Data and Supervised Machine Learning
Sociological Methods & Research, v54 n2 p534-564 2025
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.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: United States
Grant or Contract Numbers: N/A
Author Affiliations: 1Mannheim Centre for European Social Research, University of Mannheim, Mannheim, Baden-Württemberg, Germany; 2Department of Politics, University of Freiburg, Freiburg, Germany; 3Department of Statistics, Ludwig Maximilian University of Munich, Munich, Germany