Confirmatory Factors Analysis of School Administrators’ Digital Era Leadership in the East Coast Southern sub - region of Thailand
Bibliographic record
Abstract
This research aimed to study the components of digital age leadership of secondary school administrators in the southern Gulf of Thailand provinces. The sample consisted of 300 administrators of secondary schools in the southern Gulf of Thailand provinces. The sample group used a proportionate stratified sampling method. The research tools were semi-structured interviews and questionnaires on digital age leadership of secondary school administrators in the southern Gulf of Thailand provinces. The statistics used for data analysis was confirmatory factor analysis (CFA). The research results found that the components of digital leadership of secondary school administrators in the southern provinces of the Gulf of Thailand consisted of six components, including: 1) technological competence (four indicators), 2) vision (three indicators), 3) digital organizational culture (three indicators), 4) teamwork (six indicators), 5) innovation (four indicators), and 6) personnel development (three indicators). When examining the consistency of the model by considering the value of Chi-square/df = 2.290, GFI = 0.916 RMSEA = 0.066, RMR = 0.014, CFI = 0.974, it showed that the model of digital leadership components is consistent with the empirical data.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".