Pan Yanfang, Zhan Peida
Department of Psychology, Zhejiang Normal University, Jinhua, China.
Front Psychol. 2020 Jun 3;11:1051. doi: 10.3389/fpsyg.2020.01051. eCollection 2020.
Missing data are hard to avoid, or even inevitable, in longitudinal learning diagnosis and other longitudinal studies. Sample attrition is one of the most common missing patterns in practice, which refers to students dropping out before the end of the study and not returning. This brief research aims to examine the impact of a common type of sample attrition, namely, individual-level random attrition, on longitudinal learning diagnosis through a simulation study. The results indicate that (1) the recovery of all model parameters decreases with the increase of attrition rate; (2) comparatively speaking, the attrition rate has the greatest influence on diagnostic accuracy, and the least influence on general ability; and (3) a sufficient number of items is one of the necessary conditions to counteract the negative impact of sample attrition.
在纵向学习诊断和其他纵向研究中，缺失数据难以避免，甚至不可避免。样本流失是实践中最常见的缺失模式之一，它指的是学生在研究结束前退出且不再返回。本简要研究旨在通过模拟研究检验一种常见的样本流失类型，即个体层面的随机流失，对纵向学习诊断的影响。结果表明：（1）所有模型参数的恢复程度随流失率的增加而降低；（2）相对而言，流失率对诊断准确性影响最大，对一般能力影响最小；（3）足够数量的项目是抵消样本流失负面影响的必要条件之一。
