Prevalence of Gender DIF in Mixed Format High School Exit Examinations.
Bibliographic record
Abstract
The primary purpose of this study was to identify potential sources of gender differential item bias (DIF) in a high school exit examination composed of both selected-response and constructed-response items in the content areas of English, social studies, mathematics, and biology. A secondary purpose was to determine the agreement between the polytomous differential item functioning (DIF) detection methods, the Generalized Mantel-Haenszel (GMH) approach and Poly-Simultaneous Item Bias (Poly-SIB), and their counterparts, the Mantel-Haenszel procedure (MH) and SIB. Data were from four different Alberta Education Diploma Examinations for June and January 1998. The numbers of students that completed each form ranged from 2,328 to 3,386. Results indicate that both GMH and Poly-SIB were comparable to their dichotomous counterparts, MH and SIB, although there were slight differences between MH and GMH. Results about gender DIF support some hypotheses and not others. Males did not outperform females on geometry and mathematical problem solving items. Although more than 50 mathematics items were analyzed, only 8 dichotomous items were flagged. None of the gridded response items were flagged for DIF, and references to stereotypical male or female activities were not identified as DIF items or did not consistently favor one group or the other. While the majority of the dichotomous items favored males, all of the polytomous items favored females. These findings suggest that there may be an item-by-format interaction where females perform better on constructed response items even in measures of quantitative ability. The paper discusses some areas for future research. (Contains 10 tables, 1 figure, and 34 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document. U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION /17 CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. 0 Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. 1 PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".