A Validity Study Of Expert Judgment Procedures For Setting Cutoff Scores On High-Stakes Credentialing Examinations Using Cluster Analysis
Bibliographic record
Abstract
This study compares an expert judgment process--minimal performance levels (MPL) using the Nedelsky and Ebel procedures--for setting cutoff scores for pass/fail on licensure examinations with an empirical approach--cluster analysis. Data from all three components of the Canadian Standard Assessment in Optometry (CSAO) examinations (knowledge, clinical judgment, and clinical skills) from 243 candidates were obtained. Results indicate that for the written components of the exams employing the Nedelsky method of MPL setting, there was a mean agreement of pass/fail of 81% with the cluster analysis approach on pass/fail categorization. For the performance exams using the Ebel method, the mean agreement of pass/fail with the cluster analysis was 93%. Thus the subjective approaches to setting cutoff scores (i.e., expert judgment methods) converge with the objective method (i.e., cluster analysis) of classifying test takers in the same categories.
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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 | 0.176 | 0.534 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".