AModel Fit Statistic for Generalized Partial Credit Model Tie

Investigating the fit of a parametric model is an important part of the measurement process when implementing item response theory (IRT), but research examining it is limited. A general nonparametric approach for detecting model misfit, introduced by J. Douglas and A. S. Cohen (2001), has exhibited promising results for the two-parameter logistic model and Samejima s graded response model. This study extends this approach to test the fit of generalized partial credit model (GPCM). The empirical Type I error rate and power of the proposed method are assessed for various test lengths, sample sizes, and type of assessment. Overall, the proposed fit statistic per-formed well under the studied conditions in that the Type I error rate was not inflated and the power was acceptable, especially for moderate to large sample sizes. A further advantage of the nonparametric approach is that it provides a convenient graphical display of possible misfit