Item Parameter Recovery, Standard Error Estimates, and Fit Statistics of the Winsteps Program for the Family of Rasch Models

Abstract

This study investigates item parameter recovery, standard error estimates, and fit statistics yielded by the WINSTEPS program under the Rasch model and the rating scale model through Monte Carlo simulations. The independent variables were item response model, test length, and sample size. WINSTEPS yielded practically unbiased estimates for the difficulty parameters under the Rasch model and the overall difficulty parameters under the rating scale model. However, the estimates for the intersection parameters under the rating scale model were substantially biased, especially for short tests. The standard errors of the overall difficulties and intersection parameters were slightly underestimated. The cube root-transformed weighted and unweighted item fit statistics did not follow the standard normal distribution in that their empirical sampling variances were much smaller than the expected value of unity. Correction procedures were proposed to make them follow approximately the standard normal distribution so that the usual critical ranges at the α nominal level could be used to screen the misfitting items.