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Author ORCID Identifier

https://orcid.org/0000-0002-6518-3843

Date Available

6-29-2026

Year of Publication

2026

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Arts and Sciences

Department/School/Program

Psychology

Faculty

Thomas Widiger

Faculty

Michael Bardo

Abstract

Personality disorders (PDs) have historically been conferred using a categorical diagnostic framework (i.e., present versus absent). However, recent research supports a transition toward dimensional models of PDs wherein personality traits are measured along a continuum. Some dimensional models contain personality domains only, whereas other models contain both personality domains and facets of each of those domains. The leading models of each type are the PD model in the eleventh edition of the International Classification of Diseases (ICD-11) and the Alternative Model of Personality Disorders (AMPD) in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), respectively. The ICD-11 presents only five broad domains, whereas the DSM-5 AMPD includes five broad domains as well as 25 more specific facets underlying each domain. Although the field is moving increasingly toward a dimensional diagnostic framework, little research to date has 1) explored whether the addition of facets explains unique variance in personality pathology beyond domains, or 2) compared the relative balance between fit and simplicity for both types of models. A total of N = 435 participants were recruited from CloudResearch and asked to complete measures of personality domains, facets, and disorders. Hierarchical regression models with domains in Step 1 and facets in Step 2 were used to address the first aim. Hierarchical regression results indicated that facets explained unique variance in personality pathology beyond domains in more than one model for 9 out of 10 traditional PDs across 2 measures of PDs, indicating robustness of findings. To compare the models’ relative fit, the Akaike Information Criteria (AIC) for linear regression models including only domains and linear regression models including only facets were compared. Results indicated stronger support for facet-based models than domain-based models in terms of their balance between fit and simplicity for all 10 traditional PDs and both measures of PDs, again indicating robustness of findings. Taken together, these results suggest that concerns about the perceived complexity of facet-based dimensional models may be unfounded. The addition of facets to dimensional models of PDs explains unique variance beyond domains and maximizes the balance between fit and simplicity compared to models based only on domains.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2026.330

Archival?

Archival

Funding Information

Personal funding from Dr. Thomas Widiger, 2025

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