A Comparison of Four Methods for Detecting Differential Item Functioning in Ordered Response Items
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Item bias is a major threat to measurement validity. Methods for detecting differential item functioning (DIF) are now commonly used to identify potentially biased items. DIF detection methods for dichotomous items are well developed, but those for ordinal items are less well developed. In this article, the authors compare four methods for detecting DIF in ordinal items: the Mantel, generalized Mantel-Haenszel (GMH), logistic discriminant function analysis (LDFA), and unconstrained cumulative logits ordinal logistic regression (UCLOLR). Factors varied include type of DIF, group ability differences, studied item discrimination, skewness in ability distributions, and sample size ratio. All procedures had good Type I error control as well as high power for detecting uniform DIF. However, the Mantel could not detect nonuniform DIF, and the LDFA also performed poorly in detecting nonuniform DIF, particularly when item discrimination was high. The UCLOLR and GMH performed extremely well under conditions simulated in this study. Implications for research and practice are discussed.
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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 | 0.017 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it