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Updated: Jun 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Validating a multiple mini-interview question bank assessing entry-level reasoning skills in candidates for
Chris Roberts1, Nathan Zoanetti, Imogene Rothnie
1Office of Postgraduate Medical Education, University of Sydney, Sydney, New South Wales, Australia. bylundlc@mskcc.org
Context:
The multiple mini-interview (MMI) was initially designed to test non-cognitive characteristics related to professionalism in entry-level students. However, it may be testing cognitive reasoning skills. Candidates to medical and dental schools come from diverse backgrounds and it is important for the validity and fairness of the MMI that these background factors do not impact on their scores.
Methods:
A suite of advanced psychometric techniques drawn from item response theory (IRT) was used to validate an MMI question bank in order to establish the conceptual equivalence of the questions. Bias against candidate subgroups of equal ability was investigated using differential item functioning (DIF) analysis.
Results:
All 39 questions had a good fit to the IRT model. Of the 195 checklist items, none were found to have significant DIF after visual inspection of expected score curves, consideration of the number of applicants per category, and evaluation of the magnitude of the DIF parameter estimates.
Conclusions:
The question bank contains items that have been studied carefully in terms of model fit and DIF. Questions appear to measure a cognitive unidimensional construct, 'entry-level reasoning skills in professionalism', as suggested by goodness-of-fit statistics. The lack of items exhibiting DIF is encouraging in a contemporary high-stakes admission setting where candidates of diverse personal, cultural and academic backgrounds are assessed by common means. This IRT approach has potential to provide assessment designers with a quality control procedure that extends to the level of checklist items.
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