Antagonistic Narcissism in Women With Borderline Personality Disorder Assessed by Direct and Indirect Measures.
Published In: Journal of Personality Disorders, 2026, v. 40, n. 1. P. 16 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Wülfing, Philipp; Fatfouta, Ramzi; Krämer, Nikolaus; Lammers, Claas-Hinrich; Spitzer, Carsten 3 of 3
Abstract
Antagonistic narcissism (AN), involving devaluation, aggression, and impulsivity, is a key feature of narcissism and overlaps with borderline personality disorder (BPD). This study compared explicit (self-reported) and implicit (indirectly measured) AN in women with BPD and a matched clinical control (CC) group and examined associations with aggression, interpersonal problems, and emotion dysregulation. Fifty-one women with BPD and 51 CC participants completed the AN Implicit Association Test (AN-IAT), the Narcissistic Admiration and Rivalry Questionnaire (NARQ), and clinical assessments. Group differences were analyzed using Welch's t tests with Bonferroni-Holm correction; associations were examined using Spearman's correlations. BPD participants showed higher AN-IAT scores, but no group differences on NARQ Rivalry. AN-IAT correlated weakly with emotion dysregulation; NARQ Rivalry correlated moderately with aggression and interpersonal agency. Findings suggest that implicit, but not explicit, AN distinguishes women with BPD from CCs, highlighting the role of automatic antagonistic processes in BPD. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality Disorders. 2026/02, Vol. 40, Issue 1, p16
- Document Type:Article
- Subject Area:Communication and Mass Media
- Publication Date:2026
- ISSN:0885-579X
- DOI:10.1521/pedi.2026.40.1.16
- Accession Number:191632276
- Copyright Statement:Copyright of Journal of Personality Disorders is the property of Guilford Publications Inc. 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.)
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