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A Generalized Model With Internal Restrictions on Item Difficulty for Polytomous Items

机译:对多项目项的项目难度有内部限制的广义模型

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摘要

In this study, the authors extend the standard item response model with internal restrictions on item difficulty (MIRID) to fit polytomous items using cumulative logits and adjacent-category logits. Moreover, the new model incorporates discrimination parameters and is rooted in a multilevel framework. It is a nonlinear mixed model so that existing parameter estimation procedures and computer packages for nonlinear mixed models can be directly adopted to estimate the parameters. Through simulations, it was found that the SAS NLMIXED procedure could recover the parameters fairly well and produce appropriate standard errors, except when the two-parameter adjacent-category logits MIRID was fit to data with a small sample size and a short test length; overall, cumulative logits yielded a better parameter recovery than adjacent-category logits. A real data set about guilt was analyzed with gender as a Level 2 predictor to illustrate applications and applications of the new model. Further model generalization is discussed.
机译:在这项研究中,作者扩展了对项目难度具有内部限制的标准项目响应模型(MIRID),以使用累积对数和相邻类别对数来拟合多项目。此外,新模型结合了歧视参数,并植根于多级框架。它是一种非线性混合模型,因此可以直接采用现有的参数估计程序和非线性混合模型的计算机软件包来估计参数。通过仿真发现,SAS NLMIXED程序可以很好地恢复参数并产生适当的标准误差,除非两参数相邻类别的Logit MIRID适用于样本量小且测试长度短的数据。总体而言,与相邻类别的Logit相比,累积Logit的参数恢复效果更好。分析了有关内的真实数据集,并将性别作为第2级预测因子,以说明新模型的应用和应用。讨论了进一步的模型概括。

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