title: Predictive validity of the Dundee multiple mini-interview.
abstract: BACKGROUND
  The multiple mini-interview (MMI) is the primary admissions tool used to assess non-cognitive skills at Dundee Medical School. Although the MMI shows promise, more research is required to demonstrate its transferability and predictive validity, for instance, relative to other UK pre-admissions measures.
  
  
  METHODS
  Applicants were selected for interview based on a combination of measures derived from the Universities and Colleges Admissions Service (UCAS) form (academic achievement, medical experience, non-academic achievement and references) and the UK Clinical Aptitude Test (UKCAT) in 2009 and 2010. Candidates were selected into medical school according to a weighted combination of the UKCAT, the UCAS form and MMI scores. Examination scores were matched for 140 and 128 first- and second-year students, respectively, who took the 2009 MMIs, and 150 first-year students who took the 2010 MMIs. Pearson's correlations were used to test the relationships between pre-admission variables, examination scores and demographic variables, namely gender and age. Statistically significant correlations were adjusted for range restrictions and were used to select variables for multiple linear regression analysis to predict examination scores.
  
  
  RESULTS
  Statistically significant correlations ranged from 0.18 to 0.34 and 0.23 to 0.50 unrestricted. Multiple regression confirmed that MMIs remained the most consistent predictor of medical school assessments. No scores derived from the UCAS form correlated significantly with examination scores.
  
  
  CONCLUSIONS
  This study reports positive findings from the largest undergraduate sample to date. The MMI was the most consistent predictor of success in early years at medical school across two separate cohorts. UKCAT and UCAS forms showed minimal or no predictive ability. Further research in this area appears worthwhile, with longitudinal studies, replication of results from other medical schools and more detailed analysis of knowledge, skills and attitudinal outcome markers.
authors.affiliation: Division of Clinical and Population Sciences and Education, University of Dundee, Dundee, UK. ahusbands@dundee.ac.uk
authors.forename: Adrian
authors.initials: A
authors.lastname: Husbands
authors.forename: Jonathan
authors.initials: J
authors.lastname: Dowell
mesh_terms: D001077:Aptitude Tests; D003099:College Admission Test / Q000706:statistics & numerical data*; D004504:Education, Medical, Undergraduate / Q000706:statistics & numerical data*; D004521:Educational Measurement / Q000379:methods* / Q000592:standards; D006801:Humans; D007407:Interviews as Topic / Q000592:standards*; D011237:Predictive Value of Tests; D012577:Schools, Medical