PISA 2015 Reading Test Item Parameters Across Language Groups: A measurement Invariance Study with Binary Variables
Bibliographic record
Abstract
Large-scale international assessments, including PISA, might be useful for countries to receive feedback on their education systems. Measurement invariance studies are one of the active research areas for these assessments, especially cross-cultural and linguistic comparability have attracted attention. PISA questions are prepared in the English language, and students from many countries answer the translated form. In this respect, the purpose of our study is to investigate whether there is a measurement invariance problem across native English and non-native English speaker groups in the PISA-2015 reading skills subtest. The study sample included students from Canada, the USA, and the UK as the native speaker group and students from Japan, Thailand, and Turkey as the non-native speaker group. Measurement invariance studies taking into account the binary structure of the data set for these two groups revealed that eight of the twenty-eight items in the PISA-2015 reading skills test had possible limitations in equivalence
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".