Polytomous scoring correction and its effect on the model fit: A case of item response theory analysis utilizing R

Abstract

In item response theory, the number of response categories used in polytomous scoring has an effect on the fit of the model used. When the initial scoring model yields unsatisfactory estimates, corrections to the initial scoring model need to be made. This exploratory descriptive study used response data from Take Home Exam (THE) participants in the Statistical Methods I course organized by the Open University, Indonesia, in 2022. The stages of data analysis include coding the rater’s score; analyzing frequency; analyze the fit of the model based on graded, partial, and generalized partial credit models; analyze the characteristic response function (CRF) curve; scoring correction (rescaling); and re-analyze the fit of the model. The fit of the model is based on the chi-square test and the root mean square error of approximation (RMSEA). All model fit analyzes were performed by using R. The results revealed that scoring corrections had an effect on model fit and that the partial credit model (PCM) produced the best item parameter estimates. All results and their implications for practice and future research are discussed.