Testing the Cultural Differences of School Characteristics with Measurement Invariance
Bibliographic record
Abstract
<p>In this study, it was aimed to model the school characteristics in multivariate structure, and according to this model, aimed to test the invariance of this model across five randomly selected countries and economies from PISA 2012 sample. It is thought that significant differences across group in the context of school characteristics have the potential to explain the effectiveness of schools and educational systems. This study was conducted with correlational model as a basic research. Secondary level analyses were conducted on PISA 2012 School Questionnaire data. To construct “school characteristics model”, whole data from 65 participant countries and economies were considered. One country from each proficiency level and totally 5 countries were randomly selected for the research sample. These countries and economies are Shanghai-China, Korea, Ireland, Turkey and Uruguay. In this way sample was composed of totally 835 schools. Multi-group confirmatory factor analysis was used to test the invariance of school characteristics across countries. According to the results, Shanghai and Uruguay differed from each other and other countries. Across Korea, Ireland and Turkey, School characteristics provide strong invariance. These three cultures were more similar. Main result of this study is that school characteristics cannot be invariant across some cultural groups or sub-groups. In order to provide equal opportunity to all stakeholders of the educational system, and also provide school effectiveness, such kinds of differences are considered carefully.</p>
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 | 0.001 | 0.003 |
| 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".