Does response distortion statistically affect the relations between self-report psychopathy measures and external criteria?
Bibliographic record
Abstract
Given that psychopathy is associated with narcissism, lack of insight, and pathological lying, the assumption that the validity of self-report psychopathy measures is compromised by response distortion has been widespread. We examined the statistical effects (moderation, suppression) of response distortion on the validity of self-report psychopathy measures in the statistical prediction of theoretically relevant external criteria (i.e., interview measures, laboratory tasks) in a large sample of offenders (N = 1,661). We conducted 378 moderation and 378 suppression analyses to examine the response distortion hypothesis. The substantial majority of analyses (97% moderation, 83% suppression) offered no support for this hypothesis. Nevertheless, suppression analyses revealed consistent evidence that controlling for response distortion slightly increased the relations between the fearless dominance and coldheartedness features of psychopathy and maladaptive outcomes. Our findings are largely inconsistent with the popular notion that the validity of self-report psychopathy measures is markedly diminished by response distortion. Further research is necessary to determine whether these findings generalize to other populations or contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".