An investigation of the associations between personality and athlete leadership behaviours
Bibliographic record
Abstract
Athlete leadership is defined as an athlete occupying a formal or informal leadership role within a team and influences team members to achieve a common objective (Loughead, 2017, p. 58). The study of athlete leadership is guided by numerous theoretical models such as the multidimensional model of leadership (Chelladurai, 2007). According to these models, personality will influence the behaviour of the leader. Very little research in sport has examined the relationship between personality and leader behaviour. This is unfortunate because personality traits are a precondition for leadership, providing the potential for leadership (Andersen, 2006).To better understand athlete leader behaviours, this study examined the relationship between personality and leadership behaviours of athlete leaders. Athlete leaders (N = 101) rated their agreement on the Big Five Inventory (John & Srivastava, 1999), and rated the frequency of their own leadership behaviours assessed by the Differentiated Transformational Leadership Inventory (Callow et al., 2009) and Leadership Scale for Sports (Chelladurai & Saleh, 1980). Model fit indices from the path analysis support our model (?2/df = 1.10, CFI = .99, NFI = .92, RMSEA = .03). Further, the results showed the personality dimensions of openness to experience, extraversion, and conscientiousness predicted most of the leadership behaviours, while the other two personality dimensions (i.e., neuroticism, agreeableness) predicted at least one leadership behaviour. These findings provide evidence that personality is positively related to athlete leadership behaviours. The results indicate that athlete leader's personality should be taken into account when delivering any type of athlete leadership development program.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".