A Monte Carlo Comparison of Item and Person Statistics Based on Item Response Theory versus Classical Test Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the well-known theoretical advantages of item response theory (IRT) over classical test theory (CTT), research examining their empirical properties has failed to reveal consistent, demonstrable differences. Using Monte Carlo techniques with simulated test data, this study examined the behavior of item and person statistics obtained from these two measurement frameworks. The findings suggest IRT- and CTT-based item difficulty and person ability estimates were highly comparable, invariant, and accurate in the test conditions simulated. However, whereas item discrimination estimates based on IRT were accurate across most of the experimental conditions, CTT-based item discrimination estimates proved accurate under some conditions only. Implications of the results of this study for psychometric item analysis and item selection are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it