The Impact of Model Misfit on Partial Credit Model Parameter Estimates

Randall D. Penfield

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The Impact of Model Misfit on Partial Credit Model Parameter Estimates

机译：模型不匹配对部分信用模型参数估计的影响

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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.

机译：通常使用部分信用模型（PCM）通过对一组多项目的响应来对项目和个人进行参数化。由于PCM不包含判别参数，因此在某些情况下可能会严重缺乏对数据的适应性。为了确定模型失配对使用PCM进行人员和项目参数估计的影响，进行了仿真研究，其中根据广义局部信用模型生成了数据，并得出了所得人员和项目参数估计的偏差和效率被评估。结果表明，少量的非系统失配不会导致估计器的严重偏差或效率下降，但是大量的非系统失配和中等程度的系统失配会导致估计器的效率和偏差大幅下降。

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《Journal of applied measurement》 |2004年第2期|p.115-128|共14页
作者
Randall D. Penfield;
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University of Florida 1403 Norman Hall, P.O. Box 117047, Gainesville, FL 32611-7047;

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正文语种 eng
中图分类一般工业技术;
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The Impact of Model Misfit on Partial Credit Model Parameter Estimates

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