Dark Triad, Dyad, or Core? A Psychometric Evaluation of the Short Dark Triad (SD3) Across Three Countries
Bibliographic record
Abstract
Abstract Since its introduction to personality psychology literature in 2002, the study of Dark Triad personality traits has gained traction across nations. However, there exists theoretical debate regarding the empirical distinctiveness of traits. Moreover, despite universal study across countries, the Short Dark Triad (SD3) lacks validation for use in all populations. The objective of this study was to scrutinise SD3 performance across three nations, including the United Kingdom (n = 617), Canada (n = 263), and Russia (n = 1048). Specifically, factor structure and item-person functioning of the SD3 was assessed across samples. Exploratory structural equation modelling designated that a three-factor bifactor solution provided superior data-fit. In this model, SD3 items loaded on a general factor, in addition to loading on Machiavellianism, narcissism, and psychopathy dimensions. This enabled scrutiny of the degree to which SD3 items reflected a shared general dimension vs. individual subfactors. Further analyses revealed that the general factor did not possess sufficient variance to disqualify the SD3 as multidimensional. Rasch analyses focusing on the three subscales supported unidimensionality and satisfactory item fit. However, inadequate reliability existed, and items exhibited differential item functioning across nations. Although the SD3 can be considered a valid tool for capturing Dark Triad traits across countries, concerns relating to reliability and DIF suggested that revising SD3 items would enhance measurement precision.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".