Bibliographic record
Abstract
Unidimensional item response theory (IRT) models are routinely applied to data that are not strictly unidimensional despite the general consensus that this may lead to inaccurate parameter estimates. The jackknife technique, typically used to remove bias by re-sampling observations, may improve the accuracy of the unidimensional parameter estimates that result from multidimensional data by re-sampling items. This study examined (1) the systematic errors in marginal maximum likelihood (MML) parameter estimates that result from fitting a unidimensional two-parameter logistic IRT model to a test consisting of passage-linked groups of multiple-choice items and (2) the effectiveness of the jackknife in increasing the accuracy of these parameter estimates. Data were simulated according to one-, two-, and ten-dimensional models. Results suggest that the magnitude of bias in the unidimensional MML parameter estimates of passage-linked items depends predominantly on the characteristics of the item. Of the items classified as biased, the bias was more likely to occur in small item and person sample sizes. While the errors in the parameter estimates that arose from person sampling were fairly consistent across dimensionality models, the item sampling errors were not. Specifically, in the multidimensional conditions, the parameter estimates for a particular item varied as a result of the other items included in the calibration. The jackknife did not reduce the multidimensional item bias in the MML estimates.
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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 | 0.256 | 0.731 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".