Age and gender differences in narcissism: A comprehensive study across eight measures and over 250,000 participants.
Bibliographic record
Abstract
, Version IV, Narcissistic Admiration and Rivalry Questionnaire-Short Form, Single-Item Narcissism Scale, and brief version of the Pathological Narcissism Inventory). The findings of Study 1 (N = 5,736) revealed heterogeneity in how strongly the measures are correlated. Some instruments loaded clearly on one of the three factors proposed by previous research (i.e., Neuroticism, Extraversion, Antagonism), while others cross-loaded across factors and in distinct ways. Cross-sectional analyses using each measure and meta-analytic results across all measures (Study 2) with a total sample of 270,029 participants suggest consistent linear age effects (random effects meta-analytic effect of r = -.104), with narcissism being highest in young adulthood. Consistent gender differences also emerged (random effects meta-analytic effect was -.079), such that men scored higher in narcissism than women. Quadratic age effects and Age × Gender effects were generally very small and inconsistent. We conclude that despite the various conceptualizations of narcissism, age and gender differences are generalizable across the eight measures used in the present study. However, their size varied based on the instrument used. We discuss the sources of this heterogeneity and the potential mechanisms for age and gender differences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".