Jianqin YANG

Biographic Data

ID8916985
NAMEJianqin YANG
GIVEN NAMESJianqin
FAMILY NAMEYANG
SIGNATUREYANG J
AFFILIATIONSBeijing Normal University
VERIFIEDNo
TOTAL WORKS2
TOTAL CITATIONS0
AUTHOR COUNT2
EDITOR COUNT0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2023
H-INDEX0

  • Missing data analysis in cognitive diagnostic models

    Xiaofeng YOU, Jianqin YANG et al.ARTICLEActa Psychologica Sinica2023

    摘要: 认知诊断测评中缺失数据的处理是理论和实际应用者非常关注的研究主题。借鉴随机森林插补法(RFI)不依赖于缺失机制假设的特点, 对已有的RFI方法进行改进, 提出采用个人拟合指标(RCI)确定插补阈值的新方法: 随机森林阈值插补方法(RFTI)。模拟研究表明, RFTI在插补正确率上明显高于RFI方法; 与RFI和EM方法相比, RFTI在被试属性模式判准率和边际判准率上表现出明显优势, 尤其是非随机缺失和混合缺失机制, 以及缺失比例较高的条件下, 其优势更加明显。但对项目参数的估计, RFTI方法与EM方法相比不具有优势

  • A comparison of standard residual methods and a mixture hierarchical model for detecting non-effortful responses

    Yue Liu, Hongyun Liu et al.ARTICLEActa Psychologica Sinica2022

    Assessment datasets contaminated by non-effortful responses may lead to serious consequences if not handled appropriately. Previous research has proposed two different strategies: down-weighting and accommodating. Down-weighting tries to limit the influence of aberrant responses on parameter estimation by reducing their weight. The extreme form of down-weighting is the detection and removal of irregular responses and response times (RTs). The sta…