Differentiating Abnormal, Normal, and Ideal Personality Profiles in Multidimensional Spaces
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8033
comunitat-uji-handle3:10234/8636
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https://doi.org/10.1027/1614-0001/a000395 |
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Title
Differentiating Abnormal, Normal, and Ideal Personality Profiles in Multidimensional SpacesAuthor (s)
Date
2023-03-15Publisher
HogrefeISSN
1614-0001; 2151-2299Bibliographic citation
Gutiérrez, F., Peri, J. M., Aluja, A., Baillés, E., Sureda, B., Gutiérrez-Zotes, A., ... & Rodríguez, M. (2023). Differentiating abnormal, normal, and ideal personality profiles in multidimensional spaces. Journal of Individual Differences.Type
info:eu-repo/semantics/articlePublisher version
https://econtent.hogrefe.com/doi/full/10.1027/1614-0001/a000395Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Current dimensional taxonomies of personality disorder (PD) establish that intense traits do not suffice to diagnose a disorder, and additional constructs reflecting dysfunction are required. However, traits appear ... [+]
Current dimensional taxonomies of personality disorder (PD) establish that intense traits do not suffice to diagnose a disorder, and additional constructs reflecting dysfunction are required. However, traits appear able to predict maladaptation by themselves, which might avoid duplications and simplify diagnosis. On the other hand, if trait-based diagnoses are feasible, it is the whole personality profile that should be considered, rather than individual traits. This takes us into multidimensional spaces, which have their own particular – but poorly understood – logic. The present study examines how profile-level differences between normal and disordered subjects can be used for diagnosis. The Dimensional Assessment of Personality Pathology – Basic Questionnaire (DAPP-BQ) and the Personality Inventory for DSM-5 (PID-5) were administered to a community and a clinical sample each (total n = 1,925 and 3,543 respectively). Intense traits proved to be common in the general population, so empirically-based thresholds are indispensable not to take as abnormal what is at most unideal. Profile-level parameters such as Euclidean and Mahalanobis distances outperformed individual traits in predicting mental problems and equaled the performance of published measures of dysfunction or severity. Personality profiles can play a more central role in identifying disorders than is currently acknowledged, provided that adequate metrics are used. [-]
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- PSB_Articles [1315]