Development and validity of the Ethical Leadership Questionnaire
Bibliographic record
Abstract
Purpose – This study had five objectives: explain the initial steps that led to the construction of the Ethical Leadership Questionnaire (ELQ); analyze the items and verify the ELQ reliability using item response theory (IRT); examine its factorial structure with a confirmatory factor analysis (CFA) and an exploratory structural equation modeling (ESEM) approach; test the item bias of the ELQ; assess the relation between the ELQ dimensions and ethical sensitivity. The paper aims to discuss these issues. Design/methodology/approach – Study 1 and Study 2 involved 200 and 668 respondents, respectively. Step 1 consisted in IRT; Step 2 in CFA and ESEM analysis; Step 3 in invariance of the ELQ items across gender, and Step 4 in structural equation modeling. Findings – Results indicated the presence of the three types of ethic in the resolution of moral dilemmas, validating Starratt's model. The factor structure was gender invariant. Ethic of critique was significantly related to ethical sensitivity. Research limitations/implications – More replications will be needed to fully support the ELQ's validity. Given that the instrument may be used in diverse cultural contexts, invariance across cultures would be warranted. Practical implications – As educational organizations become aware of the crucial need for more ethical leaders, they will need to pay particular attention to the ethic of critique as it appears to play a significant role in the development of ethical sensitivity. Social implications – Results presented in this paper answer a vital need for more ethical skills in educational leadership. Originality/value – The ELQ provides a validated measure of Starratt's conceptual framework and highlights the key role played by ethical sensitivity and the ethic of critique.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".