Impact of simple substitution methods for missing data on Classical test theory difficulty and discrimination
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
In Classical test theory, difficulty (p) and discrimination (d) are two item coefficients that are widely used to analyze and validate items in educational testing. However, test items are usually affected by missing data (MD), and little is known about the effect of methods for handling MD on these two coefficients. The current study compares several simple substitution (imputation) strategies for dichotomous items to better understand their impact on item difficulty and discrimination. We conducted a simulation study, followed by the analysis of a real data set of test items from a language test. Based on the root mean square errors (RMSE), person mean (PM) is the best overall replacement method for difficulty p and discrimination d. However, the analysis of bias coefficients and the analysis of real data show many similarities between most of the methods investigated to compute p while multiple imputation (MI) and complete cases (CC) seem to be the least biased methods to compute d.
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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 | 0.056 | 0.431 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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