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Utilising Random Effects Models to Analyse Multiple Mini-Interviews for Prospective Medical Students - From Theory to
Chezko Malachi Peligrino Castro1, Nicola Phillips1, Karen Grant1
1Medical School, Lancaster University Medical School, Lancaster, UK.
Journal of Medical Education and Curricular Development
|February 2, 2026
Summary
Multiple-mini-interviews (MMIs) analysis reveals applicant ability is the largest variance source. A new R Shiny app standardizes MMI scores and provides interviewer feedback, improving medical school admissions.
Area of Science:
- Medical Education
- Psychometrics
- Statistical Modeling
Background:
- Multiple-mini-interviews (MMIs) are standard for medical school admissions.
- Inconsistencies in MMI scoring can arise from interviewer variability and station difficulty.
Purpose of the Study:
- To analyze Multiple-mini-interview (MMI) data using a cumulative probit mixed model.
- To develop a user-friendly R Shiny application for standardizing MMI scores and providing feedback to non-statistical experts.
Main Methods:
- A cumulative probit mixed model was employed to analyze MMI data, accounting for latent sources of variation.
- An R Shiny application was developed to make the statistical methodology accessible for standardizing scores and generating feedback.
Main Results:
- Applicant ability accounted for 22.94% of the variance in MMI scores.
- Interviewer variability contributed 10.79% to the variance, while station difficulty had a minor impact (2.23%).
- Inter-station reliability was acceptable (Cronbach's alpha = 0.7072).
Conclusions:
- The developed statistical method and application offer a robust approach to MMI score analysis.
- The tool facilitates feedback to interviewers and identification of effective assessment stations.
- The methodology can be adapted by other medical institutions for fairer candidate selection.
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