Binary Logistic Regression Analysis for Detecting Differential Item Functioning
Why this work is in the frame
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Bibliographic record
Abstract
The authors analyze the effectiveness of the R 2 and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing was used were compared with the rejection rates obtained when statistical testing was combined with an effect size measure based on recommended cutoff criteria. The manipulated variables were sample size, impact between groups, percentage of DIF items in the test, and amount of DIF. The results showed that false-positive rates were higher when applying only the statistical test than when an effect size decision rule was used in combination with a statistical test. Type I error rates were affected by the number of test items with DIF, as well as by the magnitude of the DIF. With respect to power, when a statistical test was used in conjunction with effect size criteria to determine whether an item exhibited a meaningful magnitude of DIF, the delta log odds ratio effect size measure performed better than R 2 . Power was affected by the percentage of DIF items in the test and also by sample size. The study highlights the importance of using an effect size measure to avoid false identification.
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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 | 0.007 | 0.087 |
| 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) | 0.001 | 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 it