Validation of the Narcissistic Admiration and Rivalry Questionnaire Short Scale (NARQ-S) in convenience and representative samples.
Bibliographic record
Abstract
Due to increased empirical interest in narcissism across the social sciences, there is a need for inventories that can be administered quickly while also reliably measuring both the agentic and antagonistic aspects of grandiose narcissism. In this study, we sought to validate the factor structure, provide representative descriptive data and reliability estimates, assess the reliability across the trait spectrum, and examine the nomological network of the short version of the Narcissistic Admiration and Rivalry Questionnaire (NARQ-S; Back et al., 2013). We used data from a large convenience sample (total N = 11,937) as well as data from a large representative sample (total N = 4,433) that included responses to other narcissism measures as well as related constructs, including the other Dark Triad traits, Big Five personality traits, and self-esteem. Confirmatory factor analysis and item response theory were used to validate the factor structure and estimate the reliability across the latent trait spectrum, respectively. Results suggest that the NARQ-S shows a robust factor structure and is a reliable and valid short measure of the agentic and antagonistic aspects of grandiose narcissism. We also discuss future directions and applications of the NARQ-S as a short and comprehensive measure of grandiose narcissism. (PsycINFO Database Record
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".