A Behavior Genetic Investigation of the Relationship Between Leadership and Personality
Bibliographic record
Abstract
Phenotypic research on leadership style has long considered the importance of individual differences in personality when identifying the behaviors associated with good leaders. Although leadership and many personality traits have been separately shown to be heritable, these constructs have not been examined with genetically informative data to identify common sources of heritability in the two domains. A logical extension to current research, therefore, is to examine the extent to which factors of personality are predictive of leadership dimensions and the extent to which unique genetic contributions to the relationship between personality and leadership style may be identified. Adult twin pairs (183 MZ and 64 same-sex DZ) completed the Multifactor Leadership Questionnaire (MLQ) and the Personality Research Form (PRF). Univariate analyses indicated that both leadership factors (transformational and transactional leadership) and all five of the "Big Five" factors (openness, conscientiousness, extraversion, disagreeableness, and neuroticism) were best fit by genetic models. Multivariate genetic analyses suggest that transformational leadership shows a statistically significant positive genetic correlation with conscientiousness, extraversion, and openness to experience. Transactional leadership shows a significant negative genetic correlation with conscientiousness and extraversion, and a significant positive genetic correlation with disagreeableness. These results underscore the importance of conscientiousness and extraversion in predicting leadership style, and illustrate important differences between transformational and transactional leaders.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".