认知诊断缺失数据处理方法的比较:零替换、多重插补与极大似然估计法  

Comparison of missing data handling methods in cognitive diagnosis:Zero replacement,multiple imputation and maximum likelihood estimation

作　　者：宋枝璘 郭磊 郑天鹏[3] SONG Zhilin;GUO Lei;ZHENG Tianpeng(Faculty of Psychology,Southwest University,Chongqing 400715,China;Southwest University Branch,Collaborative Innovation Center of Assessment toward Basic Education Quality,Chongqing 400715,China;Collaborative Innovation Center of Assessment for Basic Education Quality(CICA-BEQ)at Beijing Normal University,Beijing 100088,China)

机构地区：[1]西南大学心理学部 [2]中国基础教育质量监测协同创新中心西南大学分中心,重庆400715 [3]北京师范大学中国基础教育质量监测协同创新中心,北京100088

出　　处：《心理学报》2022年第4期426-440,I0002-I0005,共19页Acta Psychologica Sinica

基　　金：国家自然科学基金青年项目(31900793);北京师范大学中国基础教育质量监测协同创新中心重大成果培育性项目(2019-06-023-BZPK01);中央高校基本科研业务费专项资金(SWU2109222)资助。

摘　　要：数据缺失在测验中经常发生,认知诊断评估也不例外,数据缺失会导致诊断结果的偏差。首先,通过模拟研究在多种实验条件下比较了常用的缺失数据处理方法。结果表明:(1)缺失数据导致估计精确性下降,随着人数与题目数量减少、缺失率增大、题目质量降低,所有方法的PCCR均下降,Bias绝对值和RMSE均上升。(2)估计题目参数时,EM法表现最好,其次是MI,FIML和ZR法表现不稳定。(3)估计被试知识状态时,EM和FIML表现最好,MI和ZR表现不稳定。其次,在PISA2015实证数据中进一步探索了不同方法的表现。综合模拟和实证研究结果,推荐选用EM或FIML法进行缺失数据处理。The problem of missing data is common in research,and there is no exception for cognitive diagnostic assessment(CDA).Some studies have revealed that both the presence of missing values and the selection of different missing data processing methods would affect the results of CDA.Therefore,it is necessary to attach more attention to the problem in CDA and choose appropriate methods to deal with it.Although the problem in CDA has been explored before,previous studies did not consider multiple imputation(MI)and full information maximum likelihood(FIML),which are widely used in the field of missing data analysis.Moreover,previous studies neglected the comparison using empirical data and saturation models such as GDINA model.In summary,the main purpose of this study are to introduce MI and FIML into CDA,thus making a comprehensive comparison of different missing data handling methods,and further putting forward suggestions for handling missing data in practice.Simulation study considered six factors:(1)Sample size:200 participants,400 participants,and 1000 participants;(2)Test length:15 test items and 30 test items;(3)Quality of items:high quality,medium quality,and low quality;(4)Missing data mechanisms:missing completely at random(MCAR),missing at random(MAR),and missing not at random(MNAR);(5)Missing rate:10%,20%,and 30%;(6)Missing data handling methods:zero replacement(ZR),MI-CART,MI-PMM,MI-LOGREG.BOOT,Expectation-Maximization algorithm(EM),and FIML.The GDINA model was used,and the analysis process was realized by the GDINA package in R software.Secondly,the PISA 2015 computer-based mathematics data were applied to compare the practical value of the proposed methods.The results of simulation study revealed that:(1)Missing data results in a decrease in estimation accuracy.The absolute value of Bias and RMSE both increased and PCCR values of all methods decreased as the sample size,test length and the quality of the items decreased and the missing rate increased;(2)When estimating item parameters,EM performed best,f

关 键 词：认知诊断 GDINA 模型 缺失数据 多重插补 极大似然估计 

分 类 号：B841[哲学宗教—基础心理学]