摘要
相对于参数化的方法,本研究根据题目测量模式关系开发出ICC指标,并提出基于理想得分的ICC指标法进行Q矩阵估计。Monte Carlo模拟研究与实证研究发现(1)基于理想得分ICC指标法估计Q矩阵具有很好的效果,当属性个数越少、基础题个数越多,估计效果越好。(2)相对于以往方法——D^2统计量的方法,ICC-IR法效果更好,并且是一种非参数化的方法,计算简单快捷。(3)实证数据分析表明,ICC-IR法估计的Q矩阵在模型拟合度上也优于D^2统计量方法。
Nowadays,we are not satisfied with a total score from measurement,but hope to get an informative report.As the core of the new generation test theory,Cognitive Diagnosis(CD)attracts more and more people's attention.Since it can reveal the result and form a microscopic perspective,such as individuals'knowledge structures,processing skills and cognitive procedure etc,it would help us to take individualized teaching and promote students'development.Cognitive diagnosis assessments infer the attribute mastery pattern of respondents by item responses based on Q-matrix.The Q-matrix plays the role of a bridge between items and respondents.Many studies have shown that misspecification of the Q-matrix can affect the accuracy of model parameters and result in the misclassification of respondents.In practice,Q-matrix is established by experts.However,different experts may provide different Q-matrices.To avoid the subjectivity from experts in Q-matrix specification and ensure the correct of Q-matrix,researchers are trying to look for objective methods.Nevertheless,existing methods need information from parameter and a large amount of computation.To simplify the method of Q-matrix estimation,this article introduces a new Q-matrix estimation method based on ICC(Item Consistency Criterion).The logic of the method is as follows:if the measurement pattern of the item A is a subset of the item B.The logic of the ICC method is that it is impossible for a person to get"0"score on item A,but to get"1"on item B.Of course,if item A and item 13 have the same measurement pattern,it is impossible that a person gets"1"score on item A,but get"0"on item B(or,the other way around).From this logic we come up with Item Consistency Criterion.In order to improve the effect of ICC method,we come up with ICC-IR(ICC based on ideal response)method.In order to explore the effect of this method,we considered different numbers of participants,different numbers of base items and different Q-matrixes whose attribute numbers are different.The item parameters and attribute mastery pattern of respondents are obeyed a uniform distribution.In addition,we compared with the Likelihood D2 Statistic.The Monte Carlo simulation study and real data study showed that:generally,the ICC-IR method could recover the real Q-matrix with a high rate of success.Compared with the Likelihood D2 Statistic,the ICC-IR method was better.Furthermore,the ICC-IR method was easier to understand and needed less computation.The real data study also showed that the ICC-IR method could estimate the Q-matrix with a high success rate.Besides,without the needs of parameters estimation,the method was not affected by the deviation caused by the misfit between model and data.In a word,the method is simple and effective in Q-matrix Estimation,which is meaningful to the simplification of cognitive diagnosis.
作者
汪大勋
高旭亮
蔡艳
涂冬波
Wang Daxun;Gao Xuiang;Cai Yan;Tu Dongbo(Research Center of Psychological Health Education,School of Psychology,Jiangxi Normal University,Nanchang,33002)
出处
《心理科学》
CSSCI
CSCD
北大核心
2018年第2期466-474,共9页
Journal of Psychological Science
基金
国家自然科学基金(31660278,31760288,31300876,31100756)
江西省高等院校教学改革研究课题(JXJG-15-2-26)
江西省高校人文社科项目(XL1507,XL1508)
江西省社会科学规划项目(17JY12)
江西省教育厅人文社科重点项目(JD17077)
武汉市卫计委支撑课题(WG16C08)的资助.
关键词
认知诊断
Q矩阵
ICC指标
DINA模型
cognitive diagnosis
Q-matrix
Item Consistency Criterion
DINA model
