Penfield Randall D
University of Florida, 1403 Norman Hall, P.O. Box 117047, Gainesville, FL 32611-7047, USA.
J Appl Meas. 2004;5(2):115-28.
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.
部分计分模型（PCM）通常用于通过对一组多值项目的回答来对项目和个体进行参数化。由于PCM不包括区分参数，在某些情况下它可能会与数据存在严重的拟合不足。为了确定模型拟合不足对使用PCM估计人员和项目参数的影响，进行了一项模拟研究，其中根据广义部分计分模型生成数据，并评估所得人员和项目参数估计值的偏差和效率。结果表明，少量的非系统性拟合不足不会导致估计量出现显著的偏差水平或效率损失，但大量的非系统性拟合不足和中等程度的系统性拟合不足会导致估计量出现显著的效率损失和偏差。
