A comparative evaluation of methods of adjusting GPA for differences in grade assignment practices.
Numerous methods have been proposed for constructing an adjusted grade point average (adjusted-GPA) that controls for differences in grading standards across college courses and departments. Compared to the raw GPA, adjusted-GPA measures are generally more predictable from preadmissions variables, such as standardized tests and high school achievement. Relative rankings of students on adjusted-GPA measures are also more consistent with their relative standings within courses. This study compared the performance of 4 polytomous IRT and 3 linear models for constructing adjusted-GPA measures. Unlike previous studies, the regression weights of predictor variables and the course parameter estimates used to compute adjusted-GPA were cross-validated. Adjusted-GPA retained noticeable advantages over raw GPA on cross-validation. The largest advantages were seen in the multiple correlation of adjusted-GPA with preadmission variables, when adjusted-GPA was constructed with the rating scale and partial credit IRT models. The cross-validity of adjusted-GPA was the weakest with the graded response model.
Lei
Pui-Wa
PW
Department of Educational and School Psychology and Special Education, 201 CEDAR Building, The Pennsylvania State University, University Park, PA 16802, USA. puiwa@psu.edu
Bassiri
Dina
D
Schulz
E Matthew
EM
D002138:Calibration; D004521:Educational Measurement / Q000592:standards* / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D014495:Universities