Login / Signup

Triarchic or septarchic?-Uncovering the Triarchic Psychopathy Measure's (TriPM) structure.

Sandeep RoyColin VizeKasia UziebloJosanne D M van DongenJoshua MillerDonald R LynamInti A BrazilDahlnym YoonAndreas MokrosNicola Susan GrayRobert J SnowdenCraig S Neumann
Published in: Personality disorders (2020)
The Triarchic Psychopathy Measure (TriPM) is based on a 3-dimensional conceptual model, though few studies have directly tested a 3-factor structure. The current study used a large community sample (N = 1,064, 53% males, Mage = 34) to test the structure of the TriPM via exploratory and confirmatory factor analysis, along with 4 community replication samples from North American and Europe (Ns = 511-603, 33-49% males) and 1 European male offender sample (N = 150). Three of these samples were also used to model the correlations between relevant external correlates and the original TriPM factors versus emergent factors to examine the cost of misspecifying TriPM structure. The model analyses did not support a 3-factor model (comparative fit index = .76, root mean square error of approximation = .08), revealing a number of items with limited statistical information, but uncovered a 7-factor structure (comparative fit index = .92, root mean square error of approximation = .04). From the majority of Boldness, Meanness, and Disinhibition scale items, respectively, emerged 3 factors reflecting Positive Self-Image, Leadership, and Stress Immunity; 2 factors tapping Callousness and Enjoy Hurting; and 2 factors involving Trait Impulsivity and Overt Antisociality. Further, the Enjoy Hurting and Overt Antisociality factors were more strongly correlated with one another than with the other scales from their home domains (Callousness and Impulsivity). All 7 emergent factors were differentially associated with the external correlates, suggesting that the 3 original TriPM factors do not optimally represent the conceptual model underlying the TriPM. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Keyphrases
  • healthcare
  • mental health
  • emergency department
  • deep learning
  • obsessive compulsive disorder
  • social media
  • deep brain stimulation
  • data analysis