The impact of model misfit on partial credit model parameter estimates

The partial credit model (PCM) is commonly employed to parameterize items and individuals using responses to a set of polytomous items. Because the PCM does not include a discrimination parameter, it may encounter substantial lack of fit to the data in certain situations. To determine the impact of model misfit on the estimation of person and item parameters using the PCM, a simulation study was conducted in which data were generated according to the generalized partial credit model, and the bias and efficiency of the resulting person and item parameter estimates were assessed. The results suggest that small amounts of unsystematic misfit do not lead to dramatic levels of bias or loss of efficiency of the estimators, but large levels of unsystematic misfit and moderate levels of systematic misfit result in substantial loss of efficiency and bias of the estimators.