使用似然比D^2统计量的题目属性定义方法  

An Item Attribute Specification Method Based On the Likelihood D^2 Statistic

作　　者：喻晓锋[1,2] 罗照盛[1] 高椿雷[1] 李喻骏 王睿[1] 王钰彤 

机构地区：[1]江西师范大学心理学院,南昌330022 [2]亳州师范高等专科学校,亳州236800

出　　处：《心理学报》2015年第3期417-426,共10页Acta Psychologica Sinica

基　　金：国家自然科学基金(31160203;31100756;31360237);国家社会科学基金(12BYY055);教育部人文社会科学研究青年基金项目(13YJC880060);安徽省高校省级优秀青年人才基金重点项目(2013SQRL127ZD);安徽省自然科学研究项目(KJ2010B123;KJ2013B151);高等学校博士学科点专项科研基金(20113604110001);江西省研究生创新专项基金(YC2013-B024);安徽省哲学社会科学规划项目(AHSKY2014D102)资助

摘　　要：题目属性的定义是实施认知诊断评价的关键步骤,通过有丰富经验的领域专家对题目的属性进行定义是当前的主要方法,然而该方法受到许多主观经验因素的影响。寻找客观的题目属性定义或验证方法可以为主观定义过程提供策略支持或对结果进行改进,因此已经引起研究者们的关注。本研究构建了一种简单高效的题目属性定义方法,研究使用似然比D2统计量从作答数据中估计题目属性的方法,实现属性掌握模式、题目参数和题目属性向量的联合估计。模拟研究结果表明,使用似然比D2统计量可以有效地识别题目的属性向量,该方法一方面可以实现新编制题目属性向量的在线估计,另一方面可以验证已经定义的题目属性向量的准确性。The Q-matrix is a very important component of cognitive diagnostic assessments, and it maps attributes to items. Cognitive diagnostic assessments infer the attribute mastery pattern of respondents based on item responses. In a cognitive diagnostic assessment, item responses are observable, whereas respondents’ attribute mastery pattern is potentially, but not immediately observable. The Q-matrix plays the role of a bridge in cognitive diagnostic assessments. Therefore, Q-matrix impacts the reliability and validity of cognitive diagnostic assessments greatly. Research on how the errors of Q-matrix affect parameter estimation and classification accuracy showed that the Q-matrix from experts’ definition or experience was easily affected by experts’ personal judgment, leading to a misspecified Q-matrix. Thus, it is important to find more objective Q-matrix inference methods. This paper was inspired by Liu, Xu and Ying’s （2012） algorithm and the item-data fit statisticG2 in the item response theory framework. Further research on the Q-matrix inference, an online Q-matrix estimation method based on the statisticD2was proposed in the present study. Those items which are the base of the online algorithm are called as base items, and it is assumed that the base items are correctly pre-specified. The online estimation algorithm can jointly estimate item parameters and item attribute vectors in an incrementally manner. In the simulation studies, we considered the DINA model with different Q-matrix （attribute-number is 3, 4 and 5）, different sample size （400, 500, 800 and 1000）, and different number of correct items （8, 9, 10, 11 and 12） in the initial Q-matrix. The attribute mastery pattern of the sample followed a uniform distribution, and the item parameters followed a uniform distribution with interval [0.05, 0.25]. The results indicated that： when the number of base items was not too small, the online estimation algorithm with theD2 statistic could estimate the attribute vectors of rest item

关 键 词：题目属性定义 DINA模型 似然比 在线估计 

分 类 号：B841[哲学宗教—基础心理学]