Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art
Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art
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DOI：
10.1080/15366360802490866
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发表时间：
2008-01-01
影响因子：
1
通讯作者：
Templin, Jonathan L.
中科院分区：
文献类型：
--
作者：
Rupp, Andre A.;Templin, Jonathan L.
诊断分类模型（DCM）经常被心理测量学家推广为重要的建模替代方案，用于在基于多个假设潜在技能的情况下分析应答数据的多变量分类。本文建立了DCM空间的定义边界，回顾了该空间内的核心DCM，并与其他潜在变量模型的定义特征进行了比较和对比。比较DCM的模型包括无限制潜类模型、多维因素分析模型和多维项目反应理论模型。既要注意模型结构的统计考虑，也要注意模型使用的实质性考虑。
Diagnostic classification models (DCM) are frequently promoted by psychometricians as important modelling alternatives for analyzing response data in situations where multivariate classifications of respondents are made on the basis of multiple postulated latent skills. In this review paper, a definitional boundary of the space of DCM is developed, core DCM within this space are reviewed, and their defining features are compared and contrasted with those of other latent variable models. The models to which DCM are compared include unrestricted latent class models, multidimensional factor analysis models, and multidimensional item response theory models. Attention is paid to both statistical considerations of model structure, as well as substantive considerations of model use.