Detection of Differential Item Functioning in the Generalized Full-Information Item Bifactor Analysis Model
Bibliographic record
Abstract
In the field of psychometrics, there has been an increase in interest concerning the evaluation of fairness in standardized tests for all groups of participants. One possible feature of standardized tests is a group of testlets that may or may not contain differential item functioning (DIF) favorable to one group of participants over another. A testlet is a cluster of items that share a common stimulus. In this dissertation, a DIF detection method useful for testlet based data was developed and tested for accuracy and efficiency. The proposed model is an extension of the generalized full-information item bifactor analysis model. Unlike other IRT-based DIF detection models, the proposed model is capable of evaluating locally dependent test items and their potential impact on the DIF estimates. This assures the new capability of the bifactor DIF detection method that was not evident in previous methods. Item parameters were estimated using a maximum likelihood estimation (MLE) method producing expected a posteriori (EAP) scores. Using the restrictions of a bifactor model, the dimensionality of integration can be analytically reduced and the efficiency can be increased. Following prior research regarding DIF on a PISA dataset, the proposed DIF model was applied to mathematics items of the Program for International Student Assessment (PISA) 2009 dataset to confirm the utility of the model. After the meaning of results to the PISA research community is conveyed, a simulation study was conducted to provide concrete evidence of the model's utility. Finally, limitations of this study from computational and practical standpoints were discussed, as well as directions for further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.102 | 0.183 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".