Chen Lihan, Savalei Victoria
Psychology Department, University of British Columbia, Vancouver, BC, Canada.
Front Psychol. 2021 Aug 26;12:667802. doi: 10.3389/fpsyg.2021.667802. eCollection 2021.
In missing data analysis, the reporting of missing rates is insufficient for the readers to determine the impact of missing data on the efficiency of parameter estimates. A more diagnostic measure, the fraction of missing information (FMI), shows how the standard errors of parameter estimates increase from the information loss due to ignorable missing data. FMI is well-known in the multiple imputation literature (Rubin, 1987), but it has only been more recently developed for full information maximum likelihood (Savalei and Rhemtulla, 2012). Sample FMI estimates using this approach have since then been made accessible as part of the package (Rosseel, 2012) in the statistical programming language. However, the properties of FMI estimates at finite sample sizes have not been the subject of comprehensive investigation. In this paper, we present a simulation study on the properties of three sample FMI estimates from FIML in two common models in psychology, regression and two-factor analysis. We summarize the performance of these FMI estimates and make recommendations on their application.
在缺失数据分析中，缺失率的报告不足以让读者确定缺失数据对参数估计效率的影响。一种更具诊断性的度量，即缺失信息比例（FMI），显示了由于可忽略的缺失数据导致的信息损失如何使参数估计的标准误差增加。FMI在多重填补文献中是众所周知的（鲁宾，1987年），但直到最近才针对完全信息最大似然法进行了开发（萨瓦莱伊和雷姆图拉，2012年）。从那时起，使用这种方法的样本FMI估计值已作为统计编程语言中的软件包（罗斯塞尔，2012年）的一部分可供使用。然而，有限样本量下FMI估计值的性质尚未成为全面研究的主题。在本文中，我们针对心理学中两种常见模型（回归和双因素分析）的FIML的三种样本FMI估计值的性质进行了模拟研究。我们总结了这些FMI估计值的性能，并对其应用提出了建议。
