Detecting Gender Differences in PISA 2012 Mathematics Test with Differential Item Functioning
Bibliographic record
Abstract
The measurement tool not measuring the specific construct has a validity problem. Individuals based on the results obtained from this type of tool should not be evaluated. The purpose of this study was to examine the differentiated item functioning and item bias of mathematics items in the Programme for International Student Achievement 2012 assessment for gender using two-level hierarchical generalized linear model, logistic regression and experts’ opinions. Also differentiated item functioning sources (anxiety, interest and self-efficacy) at student level were tested. The current study was created under take into account of quantitative and qualitative methods. It was conducted with 1458 students selected from 166 schools of Turkey sample. The results reveal that hierarchical generalized linear models approach is more conservative than logistic regression approach. When the student level variables were added to the model as potential sources, differentiated item functioning did not disappear for the three items. Also half of the experts argued that the items identified as in favor of boys are biased. Statements in the items and the context were given as the reasons for this bias.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".