Methodologies for Investigating Item- and Test-Level Measurement Equivalence in International Large-Scale Assessments
Bibliographic record
Abstract
In this study, the Canadian English and French versions of the Problem-Solving Measure of the Programme for International Student Assessment 2003 were examined to investigate their degree of measurement comparability at the item- and test-levels. Three methods of differential item functioning (DIF) were compared: parametric and nonparametric item response theory and ordinal logistic regression. Corresponding derivations of these three DIF methods were investigated at the test-level to examine both differential test functioning (DTF) and the correspondence between findings at the item-level with those at the test-level. Item-level findings suggested consistency in DIF detection across methods; however, differences in effect sizes of DIF were found by each method. Test-level results revealed a high degree of consistency across DTF methods. Discrepancies were found between item- and test-level comparability analyses. Item-level analyses suggested moderate to low degrees of comparability, whereas test-level findings suggested a higher degree of comparability. Findings also indicated the direction of DIF was mixed as some DIF items favored English-speaking students and others favored French-speaking students, suggesting that DIF cancellation may explain why item-level incomparability was not detected at the test-level.
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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 | 0.252 | 0.610 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".