An Exploratory Strategy to Identify and Define Sources of Differential Item Functioning
Bibliographic record
Abstract
The sources of differential item functioning (DIF) items are usually identified through a qualitative content review by a panel of experts. However, the differential functioning for some DIF items might have been caused by reasons outside of the experts' experiences, leading to the sources for these DIF items possibly being misidentified. Quantitative methods can help to provide useful information, such as the DIF status and the number of sources of the DIF, which in turn help the item review and revision process to be more efficient and precise. However, the current quantitative methods assume all possible sources should be known in advance and collected to accompany the item response data, which is not always the case in reality. To this end, an exploratory strategy, combined with the MIMIC (multiple-indicator multiple-cause) method, that can be used to identify and name new sources of DIF is proposed in this study. The performance of this strategy was investigated through simulation. The results showed that when a set of DIF-free items can be correctly identified to define the main dimension, the proposed exploratory MIMIC method can accurately recover a number of possible sources of DIF and the items that belong to each. A real data analysis was also implemented to demonstrate how this strategy can be used in reality. The results and findings of this study are further discussed.
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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 | 0.044 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".