Measuring the dark triad: a meta-analytical SEM study of two prominent short scales
Bibliographic record
Abstract
This research examines the factor structure and psychometric properties of two well-known Dark Triad personality trait questionnaires: the Short Dark Triad (SD3) and the Dirty Dozen (DD). By analyzing data from 11 (SD3) and 5 (DD) carefully selected studies in the United States and Canada, this meta-analysis uncovers unexpected correlations among questionnaire items, challenging existing assumptions. The study employs a two-stage structural equation modeling approach to evaluate various measurement models. Conventional models, such as the correlated factor and orthogonal bifactor models, fail to explain the irregular correlations. For Dirty Dozen items, a bifactor-(S·I-1) model is more suitable than the orthogonal bifactor model, significantly affecting interpretation. On the other hand, the complex structure of the SD3 necessitates item revision to enhance reliability, discriminant validity, and predictive validity. These findings emphasize the need for refining and clarifying concepts in item revision. Furthermore, the research highlights the overlap between Machiavellianism and psychopathy, particularly in relation to revenge-related items, suggesting the need for differentiation between these traits or the identification of distinct core characteristics.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".