Psychometric properties of the successful school leadership survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study extends research on one of the most frequently cited school leadership frameworks by examining the psychometric properties of the instrument designed to assess many of the practices included in that framework. Design/methodology/approach Using data collected from 1,401 teachers the study examined the instrument’s measurement invariance, score reliabilities, as well as construct and predictive validities. Polytomous latent trait models (Many-Facet Rasch model), scale and principal component analysis using second-order Confirmatory Factor Analysis, and Structural Equation Modeling (SEM)-Path modelling were used for these purposes. Findings Findings report levels of score reliability and valid score inferences. Results concerning the predictive validity of the instrument indicate a complex set of relations among the domains of leadership practices measured by the instrument, variables selected as mediators of leaders’ influence, and their direct and indirect effects on student learning. Research limitations/implications This study provides researchers with a reliable and valid instrument for use in their future research. Data for the study were provided by elementary teachers in one US state. The extent to which results of the instrument are valid across different cultural and organizational settings remains to be determined. Practical implications Leadership developers may find the instrument useful for assessing the strengths and weaknesses of those participating in their programs while leaders themselves many find the instrument useful for self-diagnosis. Originality/value This study contributes to the development of school leadership measures by including Rasch modeling among the methods used for examining the instrument’s psychometric properties.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 it