A Structural Equation Model of Factors Affecting Effective Academic Affairs Administration of Secondary Schools in Northeast of Thailand
Bibliographic record
Abstract
The purposes of this research were 1) to develop components and indicators of effective academic affairs administration of secondary schools, 2) to examine the congruence of the developed models of the components and indicators of effective academic affairs administration of secondary schools, and 3) to determine the congruence of a structural equation model of factors affecting effective academic affairs administration of secondary schools in the Northeast of Thailand with the empirical data. The scope of the research was 1,205 government secondary schools in northeastern Thailand. The population included the 1,205 directors/deputy directors of academic affairs. Stratified random sampling was administered to draw 400 sample informants. A 5-point rating scale questionnaire was developed to collect data from the sample informants. The reliability of the questionnaire was 0.98. Statistics for data analysis included frequency, percentage, mean, S.D., Confirmatory Factor Analysis, and Structural Equation Model (SEM). The results were as follows: 1) The effective academic affairs administration was consisted of 4 components and 16 indicators; academic affairs effectiveness with 5 indicators, academic leadership with 5 indicators, teacher competence with 3 indicators, and public participation with 3 indicators. 2) The developed models of the 4 components and 16 indicators were consistent with the empirical data. The factor loadings of these models ranged between 0.43 – 1.00. They were interpreted as acceptable as the range was higher than 0.30. 3) The developed structural equation model of the factors affecting effective academic affairs administration of the secondary schools in northeastern Thailand was congruent with the empirical data.
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.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".