An Investigation of the Generalizability of Medical School Grades

Teach Learn Med. 2016 Jul-Sep;28(3):279-85. doi: 10.1080/10401334.2016.1154859. Epub 2016 Apr 19.

Abstract

Construct/Background: Medical school grades are currently unstandardized, and their level of reliability is unknown. This means their usefulness for reporting on student achievement is also not well documented. This study investigates grade reliability within 1 medical school.

Approach: Generalizability analyses are conducted on grades awarded. Grades from didactic and clerkship-based courses were treated as 2 levels of a fixed facet within a univariate mixed model. Grades from within the 2 levels (didactic and clerkship) were also entered in a multivariate generalizability study.

Results: Grades from didactic courses were shown to produce a highly reliable mean score (G = .79) when averaged over as few as 5 courses. Although the universe score correlation between didactic and clerkship courses was high (r = .80), the clerkship courses required almost twice as many grades to reach a comparable level of reliability. When grades were converted to a Pass/Fail metric, almost all information contained in the grades was lost.

Conclusions: Although it has been suggested that the imprecision of medical school grades precludes their use as a reliable indicator of student achievement, these results suggest otherwise. While it is true that a Pass/Fail system of grading provides very little information about a student's level of performance, a multi-tiered grading system was shown to be a highly reliable indicator of student achievement within the medical school. Although grades awarded during the first 2 didactic years appear to be more reliable than clerkship grades, both yield useful information about student performance within the medical college.

Keywords: generalizability theory; grading assessment.

MeSH terms

  • Achievement
  • Education, Medical / standards*
  • Educational Measurement / standards*
  • Humans
  • Iowa
  • Models, Statistical
  • Reproducibility of Results