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The Reliability and Validity of the Personality Inventory for DSM-5-Short Form (PID-5-SF) in Turkish Adolescents.

  • Published In: Journal of Personality Disorders, 2026, v. 40, n. 1. P. 48 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Coşkun, Fatma; Akça, Ömer Faruk; Bilgiç, Ayhan; Sharp, Carla 3 of 3

Abstract

The 100-item short form of the Personality Inventory for DSM-5 (PID-5-SF) has been translated into various languages and validated across cultures and age groups, but research on adolescents is limited. This study evaluated the psychometric properties of the Turkish version (PID-5-SF-TR) in a sample of 349 adolescents (181 community, 168 clinical). Participants also completed the Personality Belief Questionnaire-Short Form (PBQ-SF) and the Big Five Inventory (BFI). Clinical participants had higher facet scores than community participants. Exploratory factor analysis revealed a five-factor structure for both groups. Cronbach's α coefficients ranged from 0.72 to 0.86 in the clinical group and from 0.73 to 0.87 in the community group. PID-5-SF-TR domains correlated significantly with related BFI dimensions, except for Openness and Psychoticism. Corresponding PBQ-SF and PID-5-SF-TR subscales were significantly correlated. Test-retest reliability over 5 months showed coefficients ranging from r = 0.30 to r = 0.67. Findings support the reliability and validity of using the PID-5-SF-TR when diagnosingTurkish adolescents. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Personality Disorders. 2026/02, Vol. 40, Issue 1, p48
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:0885-579X
  • DOI:10.1521/pedi.2026.40.1.48
  • Accession Number:191632278
  • 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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