Moderated DIF 1 Running head: MODERATED DDF METHODOLOGY Differential Domain Functioning on the Numeracy Component of the Foundation Skills Assessment: Bringing the Context into Picture by Investigating Sociological / Community Moderated Test and Item
Bibliographic record
Abstract
Moderated DIF 2 The present study introduces and demonstrates a new methodology for item and test bias studies: “moderated differential item functioning (DIF)”. This technique expands the DIF methodology to incorporate contextual and sociological variables as moderating effects of the DIF. Specifically, this paper explores differential domain functioning (DDF) – the focus of interpretation for this test is on the “domain ” rather than the item. This moderated DDF effect is demonstrated on a multiple-choice and constructed-response provincial assessment test that was designed to match a specified mathematics curriculum. Participants were 45,728 grade 4 students, 45,022 grade 7 students, and 43,525 grade 10 students in British Columbia, Canada. The data from these participants was narrowed down to create four contrast groups of communities that reflect differences in contextual variables: rural low-income, rural affluent, urban low-income, and urban affluent. Gender DDF was explored using a general linear statistical model. After statistically matching males and females on their mathematical ability, gender DDF was moderated by the contextual variables. Thus, this “moderation ” approach allows one to investigate the effect of sociological, community-based contextual variables that may help one understand the complex functioning of DIF in large scale testing. In other words, what we are advocating is to take a more “sociological ” and “ecological ” approach to help us understand differences in item and test performance.
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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 | 0.111 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".