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Utilising Random Effects Models to Analyse Multiple Mini-Interviews for Prospective Medical Students - From Theory to

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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.

Keywords:
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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.