欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
在认知诊断评估中,构建正确测验Q矩阵十分关键,但比较困难.该文将确定性输入噪音与门模型下3种在线标定方法（极大似然估计方法,边际极大似然估计方法和交差方法）用于测验Q矩阵修正,并与δ方法,γ方法和最小残差平方和方法进行比较.采用模拟研究验证和比较各方法的表现,研究结果显示：边际极大似然估计方法表现良好,交差方法次之;项目所考查的属性数目是影响δ方法和γ方法的表现.
英文摘要：
The Q-matrix plays an important role in establishing the relation between latent attribute patterns and ideal response patterns. In practice,the Q-matrix is difficult to specify correctly in cognitive diagnostic assessment and misspecification of the Q-matrix can seriously affect the accuracy of both item parameter estimates and the classification of examinees. In the study,three on-line calibration methods have been extended to validate Q-matrix,and three related methods including the δ method,the γ method,and the Q-matrix refinement method（ denoted by RSS） have been compared. A simulation study was conducted to investigate the sensitivity of validation methods to four factors（ the distribution of attribute patterns,sample size,the quality of items,and the error rate of q-entries）under the deterministic inputs,noisy ＂and＂gate（ DINA） model. Results show that marginal maximum likelihood method performs best,both in terms of accuracy and robustness.
同期刊论文项目
同项目期刊论文
期刊信息
位置：