Data analytic methods for latent partially ordered classification models
Data analytic methods for latent partially ordered classification models
复制标题
DOI：
10.1111/1467-9876.00272
复制
发表时间：
2002-01-01
影响因子：
1.6
通讯作者：
Tatsuoka, C
中科院分区：
文献类型：
--
作者：
Tatsuoka, C
给出了潜在有限偏序分类模型数据分析的一般框架。当潜在模型复杂时，模型拟合的数据分析验证和实验统计特性的分析对于获得可靠和准确的结果是必不可少的。从认知模型在教育测试中的应用对实证结果进行了分析。事实证明，序贯分析方法可以大大减少进行准确分类所需的测试量。
A general framework is presented for data analysis of latent finite partially ordered classification models. When the latent models are complex, data analytic validation of model fits and of the analysis of the statistical properties of the experiments is essential for obtaining reliable and accurate results. Empirical results are analysed from an application to cognitive modelling in educational testing. It is demonstrated that sequential analytic methods can dramatically reduce the amount of testing that is needed to make accurate classifications.