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Implementing statistical equating for MRCP(UK) Parts 1 and 2.

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The MRCP(UK) exam successfully implemented statistical equating, improving predictive validity. Concerns about increased pass rates for non-UK graduates were unfounded, likely due to genuine candidate ability increases.

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Area of Science:

  • Medical Education
  • Psychometrics
  • Assessment

Background:

  • The MRCP(UK) exam transitioned from a hybrid Angoff/Hofstee method to Item Response Theory (IRT)-based statistical equating for its Part 1 and Part 2 examinations.
  • This change aimed to standardize assessments using UK graduates as the reference group.

Purpose of the Study:

  • To evaluate the implementation of statistical equating in the MRCP(UK) exams.
  • To investigate potential increases in pass rates among non-UK candidates.
  • To assess changes in examination predictive validity post-implementation.

Main Methods:

  • Analysis of MRCP(UK) Part 1 examination data from 2003-2013 and Part 2 data from 2005-2013.
  • Examination of pass rates, Differential Item Functioning (DIF), and predictive validity before and after the introduction of statistical equating.

Main Results:

  • Part 1 pass rates remained stable post-equating but showed increased annual variation.
  • Pass rates for non-UK graduates appeared to increase, but this was not linked to DIF changes and likely predated equating.
  • Statistical modeling indicated a year-on-year increase in pass rates for non-UK graduates in both parts, possibly due to genuine ability gains.
  • The predictive validity of Part 1 for Part 2 improved with statistical equating compared to the previous method.

Conclusions:

  • Statistical equating was successfully implemented in MRCP(UK) written exams, enhancing predictive validity.
  • Concerns regarding artefactual increases in non-UK candidate pass rates were not substantiated.
  • Observed pass rate changes are likely attributable to genuine increases in candidate ability, not solely the equating method.
  • IRT-based statistical equating offers a robust, theoretically sound, and efficient standard-setting method superior to judgmental techniques.