Item analysis and differential item functioning of a brief conduct problem screen.
Bibliographic record
Abstract
Research has shown that boys display higher levels of childhood conduct problems than girls, and Black children display higher levels than White children, but few studies have tested for scalar equivalence of conduct problems across gender and race. The authors conducted a 2-parameter item response theory (IRT) model to examine item characteristics of the Authority Acceptance scale from the Teacher Observation of Classroom Adaptation-Revised (AA-TOCA-R; L. Larsson-Werthamer, S. G. Kellam, & L. Wheeler, 1991) in 8,820 kindergarten children and estimated the degree of differential item functioning (DIF) by gender and race/urban status. The mean level of latent conduct problems was best represented by behaviors such as being stubborn, breaking rules, and being disobedient, whereas breaking things and taking others' property best represented the construct at one standard deviation above the mean. DIF by gender was detected, such that at equivalent levels of latent conduct problems, males received more endorsements of overt behaviors from teachers, whereas females received more endorsements of nonphysical behaviors. Moreover, overt behaviors were better discriminators of latent conduct problems for males, and nonphysical behaviors were better discriminators of latent conduct problems for females. Differences across race/urban status were not found to be conceptually meaningful. The authors' analyses also suggest that the item scaling of the AA-TOCA-R may be best represented by 5e categories instead of 6. These findings provide support for the use of IRT modeling to examine item characteristics of conduct problem scales and DIF to test for scalar equivalence across diverse subpopulations.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".