Selection Criteria of Appropriate Methods Between Covariance-Based, Partial Least Squares, and Generalized Structured Component Analysis in Structural Modeling
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 R² for MR is high, whereas the R² 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.
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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 | 0.147 | 0.413 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".