Leadership: Validation of a Self-Report Scale
Bibliographic record
Abstract
The aim of this paper was to propose and test the factor structure of a new self-report questionnaire on leadership. A sample of 373 school principals in the Province of Quebec, Canada completed the initial 46-item version of the questionnaire. In order to obtain a questionnaire of minimal length, a four-step procedure was retained. First, items analysis was performed using Classical Test Theory. Second, Rasch analysis was used to identify non-fitting or overlapping items. Third, a confirmatory factor analysis (CFA) using structural equation modelling was performed on the 21 remaining items to verify the factor structure of the scale. Results show that the model with a single third-order dimension (leadership), two second-order dimensions (transactional and transformational leadership), and one first-order dimension (laissez-faire leadership) provides a good fit to the data. Finally, invariance of factor structure was assessed with a second sample of 222 vice-principals in the Province of Quebec, Canada. This model is in agreement with the theoretical model developed by Bass (1985), upon which the questionnaire is based.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".