Multimethod assessment of psychopathy in relation to factors of internalizing and externalizing from the Personality Assessment Inventory: The impact of method variance and suppressor effects.
Bibliographic record
Abstract
Research to date has revealed divergent relations across factors of psychopathy measures with criteria of internalizing (INT; anxiety, depression) and externalizing (EXT; antisocial behavior, substance use). However, failure to account for method variance and suppressor effects has obscured the consistency of these findings across distinct measures of psychopathy. Using a large correctional sample, the current study employed a multimethod approach to psychopathy assessment (self-report, interview and file review) to explore convergent and discriminant relations between factors of psychopathy measures and latent criteria of INT and EXT derived from the Personality Assessment Inventory (Morey, 2007). Consistent with prediction, scores on the affective-interpersonal factor of psychopathy were negatively associated with INT and negligibly related to EXT, whereas scores on the social deviance factor exhibited positive associations (moderate and large, respectively) with both INT and EXT. Notably, associations were highly comparable across the psychopathy measures when accounting for method variance (in the case of EXT) and when assessing for suppressor effects (in the case of INT). Findings are discussed in terms of implications for clinical assessment and evaluation of the validity of interpretations drawn from scores on psychopathy measures.
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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 | 0.021 | 0.043 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".