An examination of the factorial validity of a transformational coaching leadership inventory
Bibliographic record
Abstract
The importance of coaching leadership is well established within the sport psychology literature (e.g., Gould et al., 2002). Avolio (1999) advocates leaders adopt a full range of leadership behaviours consisting of both transactional and transformational behaviours. With the exception of a small number of studies, most sport research to date has focused on the transactional component of leadership (Gomes et al., 2006). This may be due to the paucity of a sport specific transformational leadership measurement tool. The Differentiated Transformational Leadership Inventory (DTLI; Hardy et al., 2010) has shown promise as a valid and reliable measure of transformational leadership in a military context and has been recently used in sport (Callow et al., 2009). In particular, the DTLI has been used to examine athlete leadership but has not been used with regards to coaching leadership. Therefore, the purpose of the present study was to test the factorial validity of the DTLI in relation to coaching behaviours. The inventory was completed by 199 varsity athletes (67 males, 128 females: Mage = 20.40; SD = 1.91). Confirmatory factor analysis revealed that modifications to the DTLI were necessary in order to achieve acceptable levels of fit (e.g., RMSEA below .08, CFI and TLI above .90). Results indicated a five factor structure of transformational leadership provided the best model fit. Specific issues related to assessing coaching leadership behaviours are discussed.Acknowledgments: SSHRC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".