Differential Item Functioning Results May Change Depending On How An Item Is Scored: An Illustration With The Center For Epidemiologic Studies Depression Scale
Why this work is in the frame
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Bibliographic record
Abstract
The present study investigated potentially biased scale items on the Center for Epidemiologic Studies Depression (CES-D). The 20-item CES-D was scored using two binary methods (presence and persistence) and one ordinal method. Gender differential item functioning (DIF) was explored using Zumbo’s OLR method with corresponding logistic regression effect size estimator with all three scoring methods. Gender DIF was found with the CES-D item “crying” for the ordinal and presence methods of scoring. The persistence scoring method identified two DIF items (effort and hopeful); however, this scoring method appears to be of limited use due to low variability on some items. Overall, the results indicate that the scoring method has an effect on DIF; thus, DIF is a property of the item, scoring method, and purpose of the instrument.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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