DOES NUMBER OF LEVEL-2 UNITS IN MULTILEVEL STRUCTURAL EQUATION MODELING MATTER?
Bibliographic record
Abstract
How to determine the number of level-2 units in multilevel structural equation modeling (MSEM) as a standard applied to nested or hierarchical data structure was still unknown. This research used Canada data in the large database “Programme for International Student Assessment 2003”(PISA 2003) to check the model-fit indexes and parameters stability in our proposed empirical example processed by MSEM under different numbers of level-2 units. Our proposed example model was first be handled to fit Canada data (26884 students, 948 schools), and then the stabilities of the estimated parameters in the example model under 120, 240, 360, 480, 600, 720, 840 level-2 units were compared. Level-1 units in each school less than10 students will be crossed out in advance. Besides, intraclass correlations of all variables were controlled in a specified range in different numbers of level-2 units. Finally, we found the ratio of the number of level-2 units relative to the number of estimated parameters of between-level in the multilevel model were 8: 1.
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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 | 0.155 | 0.458 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".