title: The BioMedical Admissions Test for medical student selection: issues of fairness and bias.
abstract: BACKGROUND
  The BioMedical Admissions Test (BMAT) forms part of the undergraduate medical admission process at the University of Cambridge. The fairness of admissions tests is an important issue.
  
  
  OBJECTIVE
  Aims were to investigate the relationships between applicants' background variables and BMAT scores, whether they were offered a place or rejected and, for those admitted, performance on the first year course examinations.
  
  
  METHODS
  Multilevel regression models were employed with data from three combined applicant cohorts. Admission rates for different groups were investigated with and without controlling for BMAT performance. The fairness of the BMAT was investigated by determining, for those admitted, whether scores predicted examination performance equitably.
  
  
  RESULTS
  Despite some differences in applicants' BMAT performance (e.g. by school type and gender), BMAT scores predicted mean examination marks equitably for all background variables considered. The probability of achieving a 1st class examination result, however, was slightly under-predicted for those admitted from schools and colleges entering relatively few applicants. Not all differences in admission rates were accounted for by BMAT performance. However, the test constitutes only one part of a compensatory admission system in which other factors, such as interview performance, are important considerations.
  
  
  CONCLUSIONS
  Results are in support of the equity of the BMAT.
authors.affiliation: Research Division, Cambridge Assessment, UK. emery.j@cambridgeassessment.org.uk
authors.forename: Joanne L
authors.initials: JL
authors.lastname: Emery
authors.forename: John F
authors.initials: JF
authors.lastname: Bell
authors.forename: Carmen L
authors.initials: CL
authors.lastname: Vidal Rodeiro
mesh_terms: D015510:Clinical Medicine / Q000193:education*; D003099:College Admission Test / Q000706:statistics & numerical data; D004739:England; D005260:Female; D006801:Humans; D008297:Male; D012044:Regression Analysis; D012577:Schools, Medical; D015983:Selection Bias; D013337:Students, Medical / Q000523:psychology