心理学报2016，Vol.48Issue(3)：318-330,13.DOI:10.3724/SP.J.1041.2016.00318
重参数化的多分属性诊断分类模型及其判准率影响因素
Factors affecting the classification accuracy of reparametrized diagnostic classification models for expert-defined polytomous attributes
摘要
Abstract
Diagnostic classification assessment (DCA) utilizes latent class models to provide fine-grained information about students’ strengths and weaknesses in the learning process. In the past decades, extensive research has been conducted in the area of DCA and many statistical models based on a probabilistic approach have been proposed. At present, several diagnostic classification models (DCMs) for dichotomous attributes exist, which include the deterministic inputs, noisy “and” gate (DINA; Junker & Sijtsma, 2001); the deterministic inputs, noisy “or” gate (DINO; Templin & Henson, 2006); and the linear logistic model (LLM; Maris, 1999). In contrast, only a few DCMs can be used to deal with the polytomous attributes, such as the model based on the ordered-category attribute coding (OCAC; Karelitz, 2004), and the polytomous generalized DINA (pG-DINA; Chen & de la Torre, 2013). <br> Polytomous attributes, particularly those defined as part of the test development process, can provide additional diagnostic information. The present research proposes three reparametrized reduced models of pG-DINA (Chen & de la Torre, 2013), which include the reparametrized polytomous attributes DINA (RPa-DINA), the reparametrized polytomous attributes DINO (RPa-DINO), and the reparametrized polytomous attributes LLM (RPa-LLM). Furthermore, to better understand the classification accuracy of the new models, the impact of 6 factors was investigated, namely, the number of polytomous attributes, the highest level of polytomous attributes, the correlations among polytomous attributes, the hierarchical structure, the sample size, and the number of items. Results of the simulation study indicated that: <br> (1) more polytomous attributes led to lower classification. Their effects, in descending order, were the RPa-LLM, the RPa-DINO, and the RPa-DINA. Less than 5 polytomous attributes used in empirical research is suggested; <br> (2) for the number of attribute levels, more levels resulted in worse performance. Less than 4 levels within one attribute used in empirical research is suggested; <br> (3) the higher the correlations among polytomous attributes, the higher the classification accuracy would be; <br> (4) different hierarchical structure had different influences on the classification accuracy. No matter what structure we had, the performance of RPa-DINA was quite well behaved. However, other 2 models, especially the RPa-DINO, were recommended for the analysis of response data from independent hierarchical structure; <br> (5) the sample size has little impact on the classification accuracy; and <br> (6) the number of items was inversely proportional to the classification accuracy.关键词
认知诊断/多分认知属性/多分Q矩阵/诊断分类模型/DINA/DINO/LLMKey words
cognitive diagnosis/polytomous attribute/polytomous Q matrix/diagnostic classification models/DINA/DINO/LLM分类
社会科学引用本文复制引用
詹沛达,边玉芳,王立君..重参数化的多分属性诊断分类模型及其判准率影响因素[J].心理学报,2016,48(3):318-330,13.基金项目
全国教育科学规划教育部重点课题(主观题的多分属性认知诊断模型开发及其在物理测验中的应用)，课题批准号：DBA150236 （主观题的多分属性认知诊断模型开发及其在物理测验中的应用）
