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    Construct Validity of the Comprehensive Assessment of Psychopathic Personality - Self-Report Form

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    KAVISH-DISSERTATION-2021.pdf (765.3Kb)
    Date
    2021-07-14
    Author
    Kavish, Nicholas
    0000-0003-3975-4347
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    Abstract
    Psychopathy refers to a constellation of personality traits with significant public health implications. Yet, there remains ongoing debate regarding the optimal measurement of psychopathic traits—what traits to include, whether traits such as antisocial behavior are a symptom or a correlate, and how to best organize our models of psychopathic traits. Seeking to address some of these issues, the Comprehensive Assessment of Psychopathic Personality (CAPP) is a concept map that was developed using a “bottom-up” approach incorporating extensive literature review and consultation with subject matter experts. The concept map consists of 33 symptoms thematically organized into six domains—Attachment, Behavior, Cognitive, Dominance, Emotion, and Self. Recently, the CAPP – Self Report form (CAPP-SR) was developed as a 99 item self-report operationalization of the CAPP concept map, with 3 items per CAPP symptom, but its psychometric properties have not been extensively or independently investigated. The current study evaluated the internal consistency, factorial structure, and construct validity of the CAPP-SR in a large, mixed sample of undergraduate students (n = 700) and Amazon Mechanical Turk workers (n = 238). No organizational structure was superior according to all metrics, but a theoretically supported three factor solution representing behavioral, affective, and interpersonal traits appeared to be the optimal solution. The CAPP-SR demonstrated significant overlap with other pre-existing psychopathy measures and the three-factor solution evinced relatively good convergent and discriminant associations with external criterion. Overall, the CAPP-SR seems to measure a similar construct to, and is best organized in a similar manner to, prior psychopathy measures and it remains unclear whether the new measure captures meaningful information neglected by other models or outperforms them in prediction of important outcomes.
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    https://hdl.handle.net/20.500.11875/3161
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