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Psychometric properties of the multiple mini-interview used for medical admissions: findings from generalizability and Rasch analyses

Psychometric properties of the multiple mini-interview used for medical admissions: findings from... The multiple mini-interview (MMI) has become an increasingly popular admissions method for selecting prospective students into professional programs (e.g., medical school). The MMI uses a series of short, labour intensive simulation stations and scenario interviews to more effectively assess applicants’ non-cognitive qualities such as empathy, critical thinking, integrity, and communication. MMI data from 455 medical school applicants were analyzed using: (1) Generalizability Theory to estimate the generalizability of the MMI and identify sources of error; and (2) the Many-Facet Rasch Model, to identify misfitting examinees, items and raters. Consistent with previous research, our results support the reliability of MMI process. However, it appears that the non-cognitive qualities are not being measured as unique constructs across stations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Health Sciences Education Springer Journals

Psychometric properties of the multiple mini-interview used for medical admissions: findings from generalizability and Rasch analyses

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Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer Science+Business Media Dordrecht
Subject
Education; Medical Education
ISSN
1382-4996
eISSN
1573-1677
DOI
10.1007/s10459-013-9463-7
pmid
23709188
Publisher site
See Article on Publisher Site

Abstract

The multiple mini-interview (MMI) has become an increasingly popular admissions method for selecting prospective students into professional programs (e.g., medical school). The MMI uses a series of short, labour intensive simulation stations and scenario interviews to more effectively assess applicants’ non-cognitive qualities such as empathy, critical thinking, integrity, and communication. MMI data from 455 medical school applicants were analyzed using: (1) Generalizability Theory to estimate the generalizability of the MMI and identify sources of error; and (2) the Many-Facet Rasch Model, to identify misfitting examinees, items and raters. Consistent with previous research, our results support the reliability of MMI process. However, it appears that the non-cognitive qualities are not being measured as unique constructs across stations.

Journal

Advances in Health Sciences EducationSpringer Journals

Published: May 25, 2013

References