Analysis of Sources of Latent Class Differential Item Functioning in International Assessments
Bibliographic record
Abstract
In this study, we investigated differential item functioning (DIF) and its sources using a latent class (LC) modeling approach. Potential sources of LC DIF related to instruction and teacher-related variables were investigated using substantive and three statistical approaches: descriptive discriminant function, multinomial logistic regression, and multilevel multinomial logistic regression analyses. Results revealed that differential response patterns, as indicated by identification of LCs, were most strongly associated with student achievement levels and teacher-related variables rather than manifest characteristics such as gender, test language, and country, which are the focus of typical measurement comparability research. Findings from this study have important implications for measurement comparability and validity research. Evidence of within-group heterogeneity in the test data structure suggests that the identification of DIF and its sources may not apply to all examinees in the group and that measurement incomparability may be greater among groups that are not defined by manifest variables such as gender and ethnicity. Results suggest that alternative variables that may be more closely related to the investigated construct should be examined when conducting measurement comparability research.
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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 | 0.049 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".