The Presence and Impact of Local Item Dependence on Objective Structured Clinical Examinations Scores and the Potential Use of the Polytomous, Many-Facet Rasch Model
Bibliographic record
Abstract
OBJECTIVE: The purpose of this research project was to extend the research on the robustness of the dichotomous Rasch model to violations of the local independence assumption to the polytomous many-facet Rasch model (MFRM). Candidate scores from oral examinations and objective structured clinical examinations (OSCEs) have been shown to contain variance due to rater error/bias. If the MFRM is robust to local item dependence (LID), then the MFRM could theoretically be applied to medical OSCEs. METHODS: Five OSCEs were used in the study: 3 chiropractic licensure OSCEs and 2 nursing licensure OSCEs. Items were assigned to split-halves based on common stimulus. Split-half correlations were compared with Spearman-Brown estimates of reliability based on Cronbach alpha with all items contributing. Two- and 3-facet MFRM analyses were performed, first with individual items contributing and second with station totals contributing. Correlations were estimated between the 2 MFRM estimates. RESULTS: Cronbach alpha estimates with all items contributing were all very high (>.87). Spearman-Brown estimates were all considerably higher than split-half correlations. Correlations between MFRM by items and by stations were all very high (>.993). CONCLUSIONS: The research project provided evidence that OSCEs violate the local item independence assumption. The project also showed that the MFRM is quite robust to such violations. The authors recommend that the MFRM be applied to OSCEs by station totals for estimates of candidate ability, and by items for item performance measures and quality control programs.
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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 | 0.168 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".