Investigating Sources of Differential Item Functioning in International Large-Scale Assessments Using a Confirmatory Approach
Bibliographic record
Abstract
International large-scale assessments of achievement often have a large degree of differential item functioning (DIF) between countries, which can threaten score equivalence and reduce the validity of inferences based on comparisons of group performances. It is important to understand potential sources of DIF to improve the validity of future assessments; however, previous attempts to identify sources of DIF have had variable results. This study had two purposes. The first was to apply a confirmatory approach (Poly-SIBTEST) to investigate sources of DIF typically found in international large-scale assessments: adaptation effects and cognitive loadings of items. We conducted three pairwise DIF analyses on Spanish and English versions of the Progress in International Reading Literacy Study 2001 Reader booklet. Results confirmed that item cognitive loadings were a source of differential functioning favoring both England and the United States when compared against Colombia; however, adaptation effects did not consistently favor one group or the other. The second purpose of this study was to highlight strengths and limitations of Poly-SIBTEST for conducting substantive analyses of differential functioning sources and also to offer suggestions for future directions on this type of methodological research.
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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 | 0.096 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".