Higher-order latent trait models for cognitive diagnosis
Higher-order latent trait models for cognitive diagnosis
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DOI：
10.1007/bf02295640
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发表时间：
2004-09-01
期刊：
影响因子：
3
通讯作者：
Douglas, JA
中科院分区：
文献类型：
--
作者：
De la Torre, J;Douglas, JA
提出了高阶潜在特征，用于指定认知诊断模型中二元属性的联合分布。这种方法为高维属性向量的联合分布提供了一个简约的模型，该模型在许多情况下在许多情况下是自然而然的，但是较少信息的项目响应模型将是合理的选择。这种方法源于将属性视为考试性能所需的特定知识，并将这些属性建模为源于类似于项目响应模型的0的广泛定义的潜在特征。通过这种方式，属性结果的联合分布的相对简单模型基于一个合理的模型，用于一般能力和特定知识之间的关系。为选定的响应分布提供了Markov Chain Monte Carlo算法，用于参数估计，并给出了仿真结果，以检查算法的性能以及分类对模型错误指定的敏感性。提供了分数减法数据的分析。
Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling the 0 of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example.