title: Using item response theory to explore the psychometric properties of extended matching questions examination in undergraduate medical education.
abstract: BACKGROUND
  As assessment has been shown to direct learning, it is critical that the examinations developed to test clinical competence in medical undergraduates are valid and reliable. The use of extended matching questions (EMQ) has been advocated to overcome some of the criticisms of using multiple-choice questions to test factual and applied knowledge.
  
  
  METHODS
  We analysed the results from the Extended Matching Questions Examination taken by 4th year undergraduate medical students in the academic year 2001 to 2002. Rasch analysis was used to examine whether the set of questions used in the examination mapped on to a unidimensional scale, the degree of difficulty of questions within and between the various medical and surgical specialties and the pattern of responses within individual questions to assess the impact of the distractor options.
  
  
  RESULTS
  Analysis of a subset of items and of the full examination demonstrated internal construct validity and the absence of bias on the majority of questions. Three main patterns of response selection were identified.
  
  
  CONCLUSIONS
  Modern psychometric methods based upon the work of Rasch provide a useful approach to the calibration and analysis of EMQ undergraduate medical assessments. The approach allows for a formal test of the unidimensionality of the questions and thus the validity of the summed score. Given the metric calibration which follows fit to the model, it also allows for the establishment of items banks to facilitate continuity and equity in exam standards.
authors.affiliation: Academic Unit of Musculoskeletal and Rehabilitation Medicine, University of Leeds, UK. B.Bhakta@leeds.ac.uk
authors.forename: Bipin
authors.initials: B
authors.lastname: Bhakta
authors.forename: Alan
authors.initials: A
authors.lastname: Tennant
authors.forename: Mike
authors.initials: M
authors.lastname: Horton
authors.forename: Gemma
authors.initials: G
authors.lastname: Lawton
authors.forename: David
authors.initials: D
authors.lastname: Andrich
mesh_terms: D000328:Adult; D000465:Algorithms; D002138:Calibration; D002983:Clinical Competence / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D004501:Education, Medical; D004504:Education, Medical, Undergraduate / Q000379:methods / Q000592:standards*; D004521:Educational Measurement / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D008511:Medicine / Q000145:classification; D015233:Models, Statistical; D011336:Probability; D011594:Psychometrics; D012577:Schools, Medical; D013038:Specialization; D013043:Specialties, Surgical / Q000145:classification / Q000193:education*; D014904:Western Australia