Defining distress tolerance in a structural model of Big Five personality domains.
Published In: Journal of Personality, 2025, v. 93, n. 3. P. 567 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lopez, Maria Martin; Naragon‐Gainey, Kristin; Conway, Christopher C. 3 of 3
Abstract
Objective: Distress tolerance (DT)—willingness to face internal discomforts—has a fuzzy boundary with neuroticism (low emotional stability), raising questions about its independent role in models of personality and mental health. Method: We investigated DT's overlap with neuroticism and other Big Five factors in a structural model of personality and personality disorder features in samples of university students (N = 1025), emotional disorder patients (N = 225), and substance‐use patients (N = 210). Results: In exploratory factor analyses, we found that DT indicators clustered with neuroticism and were essentially unrelated to other Big Five domains. Big Five personality dimensions collectively explained approximately 40%–70% of variation in DT, across different samples and methods of quantifying shared variance. Conclusions: We conclude that DT and neuroticism are near neighbors in empirical space and speculate that much of the observed correlation between DT and mental health outcomes in the literature may be carried by shared neuroticism variance. We suggest that clearer distinctions between the two constructs in empirical research could improve our understanding of DT's unique role in the development and treatment of psychopathology. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality. 2025/06, Vol. 93, Issue 3, p567
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:0022-3506
- DOI:10.1111/jopy.12952
- Accession Number:184969374
- Copyright Statement:Copyright of Journal of Personality is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.