Selection Criteria of Appropriate Methods Between Covariance-Based, Partial Least Squares, and Generalized Structured Component Analysis in Structural Modeling
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
This study employs covariance-based (CB), partial least squares (PLS), and Generalized Structured Component Analysis (GSCA) to model the relationships between Participatory, Transparent, and Accountable School Management (PS), Teacher Competence and Performance (KG), Learning Quality and Relevance (MR), and Learning Achievement (CP) using National Assessment (AN) data from 833 senior secondary schools (SMA) in Indonesia.CP is measured at the school level in terms of numeracy, literacy, and character, while MR is positioned as a mediating variable linking PS and KG to CP.Because the indicator data deviate from multivariate normality, the CB model is estimated with a robust MLR estimator, while PLS and GSCA are treated as component-based alternatives.In all three SEM frameworks, PS exhibits a strong and significant effect on MR, KG shows a positive but relatively small effect on MR, and MR demonstrates a moderate and significant effect on CP.The R for MR is high, whereas the R for CP is moderate, indicating that factors outside the model also influence learning outcomes.Substantively, the findings underscore the strategic role of school management and classroom learning quality, while methodologically, they offer empirical insights into the application of CB, PLS, and GSCA to non-normally distributed 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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it