Detection of cultural and linguistic differential item functioning in reading assessment
Bibliographic record
Abstract
This study aims to determine whether differential item functioning (DIF) occurs in the PISA 2018 reading assessment and, if so, to explore which factors, such as linguistic elements, achievement goals, and perceived reading instructions as cultural elements, contribute most significantly to its occurrence. The United States was set as the reference group, and comparisons were made with Canada, Singapore, and South Korea. Item response theory-likelihood ratio (IRT-LR), logistic regression, and Rasch Tree analyses were utilized to identify DIF. Multiple methods consistently showed that item CR551Q06 exhibited DIF. The Rasch Tree analysis revealed that linguistic rather than cultural differences were the primary contributors to DIF. Interestingly, no DIF was detected using the Rasch Tree method in the comparisons between the United States and Canada and between the United States and Singapore, in contrast to the IRT-LR and logistic regression results. The analysis highlighted translation issues as a major source of bias, suggesting that careful adaptation of assessments is crucial to reducing DIF. These findings challenge assumptions about cultural differences in educational outcomes and emphasize the need for further research using varied DIF detection methods in different cultural contexts.
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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 | 0.035 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".