Structural Validity of the MACI Psychopathy and Narcissism Scales: Evidence of Multidimensionality and Implications for Use in Research and Screening
Bibliographic record
Abstract
This study investigated the psychometric properties and predictive validity of three self-report scales (the Psychopathy Content Scale, the Psychopathy-16 scale, and the Egotistic scale) derived from the Millon Adolescent Clinical Inventory (MACI) to screen for the presence of psychopathic and narcissistic personality characteristics. Exploratory and confirmatory factor analyses were performed in a sample of 173 clinic-referred adolescents (ages 12-17), results from which suggested that these scales are multidimensional in nature. The Psychopathy Content Scale was best captured by a two-factor structure, with personality-based items loading on one factor and antisocial/impulsive behaviors loading on the second. The most parsimonious solution for the Psychopathy-16 scale was a three-factor model, characterized by callous and egocentric features on the first two factors and antisocial behaviors on the third. The Egotistic scale of the MACI was best represented by three factors, depicting features of self-confidence, exhibitionistic tendencies, and social conceit, respectively. Regression analyses supported the multidimensionality of these scales by showing divergent patterns of association with violent and nonviolent outcomes among the factors that composed the scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".