Asymptotic identifiability of nonparametric item response models
Asymptotic identifiability of nonparametric item response models
复制标题
非参数项目响应模型的渐近可辨识性
DOI：
10.1007/bf02296194
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发表时间：
2001
期刊：
影响因子：
3
通讯作者：
J. Douglas
中科院分区：
文献类型：
--
作者：
J. Douglas
研究了具有非参数指定项目特征曲线的项目反应模型的可辨识性。当只有一组项目特征曲线可能产生项目响应的显性分布时，可以实现严格的可识别性，具有固定的潜在特质分布。当项目特征曲线属于非常一般的类别时，无法实现此属性。然而，对于具有许多项目的评估，研究表明，所有显式分布模型的项目特征曲线彼此非常接近，并且随着项目数量的增加，它们之间的逐点差异在潜在特征的所有值处收敛于零。给出了这种收敛速度的上界。主要结果为非参数项目反应模型的实践提供了理论支持，表明长期评估的模型具有渐近可识别性。
The identifiability of item response models with nonparametrically specified item characteristic curves is considered. Strict identifiability is achieved, with a fixed latent trait distribution, when only a single set of item characteristic curves can possibly generate the manifest distribution of the item responses. When item characteristic curves belong to a very general class, this property cannot be achieved. However, for assessments with many items, it is shown that all models for the manifest distribution have item characteristic curves that are very near one another and pointwise differences between them converge to zero at all values of the latent trait as the number of items increases. An upper bound for the rate at which this convergence takes place is given. The main result provides theoretical support to the practice of nonparametric item response modeling, by showing that models for long assessments have the property of asymptotic identifiability.