Performance of SIBTEST When the Percentage of DIF Items is Large
Bibliographic record
Abstract
Differential item functioning (DIF) analyses are used to identify items that operate differently between two groups, after controlling for ability. The Simultaneous Item Bias Test (SIBTEST) is a popular DIF detection method that matches examinees on a true score estimate of ability. However in some testing situations, like test translation and adaptation, the percentage of DIF items can be large. In these situations, the effectiveness of SIBTEST has not been thoroughly evaluated. The problem is addressed in this study. Four variables were manipulated in a simulation study: The amount of DIF on a 40-item test (20%, 40%, and 60% of the items on the test had moderate and large DIF), the direction of DIF (balanced and unbalanced DIF items), sample size (500, 1,000, 1,500, and 2,000 examinees in each group), and ability distribution differences between groups (equal and unequal). Each condition was replicated 100 times to facilitate the computation of the DIF detection rates. The results from the simulation study indicated that SIBTEST yielded adequate DIF detection rates, even when 60% of the items contained DIF, providing DIF was balanced between the reference and focal groups and sample sizes were at least 1,000 examinees per group. SIBTEST also had adequate detection rates in the 20% unbalanced DIF conditions with samples of 1,000 examinees per group. However, SIBTEST had poor detection rates across all 40% and 60% unbalanced DIF conditions. Implications for practice and future directions for research are 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 | 0.025 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".