Indicators of Inspirational Leadership for Primary School Principals: Developing and Testing the Structural Relationship Model
Bibliographic record
Abstract
This study aimed to examine the consistency of the structural relationship model in which developed from related theories, previous studies, and empirical data, respectively. The study also investigated factor loading of main components, subcomponents, and indicators. The population in this study was primary school principals under Office of the Basic Education Commission in Thailand. Collected data were used by multi-stage random sampling to get 660 samples. The data were analyzed by using statistical application and AMOS program. The results were consistent with hypothesis. The model of which developed from related theories and previous studies were consistent with empirical data based on the following values, e.g. relative Chi-square (CMIN/DF), Root Mean Square Error of Approximation (RMSEA), Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI), Comparative Fit Index (CFI), and Normed Fit Index (NFI). Both first and second order confirmatory factors were also analyzed. Fator loading of main components was 0.90-1.47 which was higher than 0.70. Factor loading of sub-components was 0.73-2.13. Floading of indicators was 0.74-2.77 which was higher than 0.30, respectively.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".