Illustrating the Use of Nonparametric Regression to Assess Differential Item and Bundle Functioning Among Multiple Groups
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Bibliographic record
Abstract
The purpose of this article is to illustrate the use of nonparametric regression with kernel smoothing (Ramsay, 1991), as implemented with the computer program TESTGRAF (Ramsay, 2000), to investigate differential item or bundle functioning among multiple groups. Nonparametric regression is a flexible procedure used to estimate and display the relation between the probability that examinees with a given proficiency level will choose different options to multiple-choice items. The unit of analysis can be an item or a bundle of items. It can also be used to detect differential performance across two or more groups of examinees matched on overall proficiency. We present three examples to illustrate how nonparametric regression can be applied to multilingual, multicultural data to study group differences.
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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 | 0.003 | 0.343 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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