Missing data in experiments: Challenges and solutions.
Missing data in experiments: Challenges and solutions.
复制标题
实验中缺失数据：挑战和解决方案。
作者：
Robin Gomila;Chelsey S Clark
缺失数据是实验数据集的一个共同特征。心理学研究人员用来处理缺失的标准方法依赖于不切实际的假设，使随机分配程序无效，并对效应大小进行偏倚估计。我们描述了实验数据集中通常遇到的不同类型的缺失数据，并讨论了它们各自如何影响研究人员的因果推断。在本教程中，我们提供了处理每类缺失的具体指导方针，重点介绍了2种做出现实假设的方法：（a）针对轻度缺失实例的逆概率加权（IPW），以及（B）针对严重缺失实例的双采样和边界。在回顾了这些方法提高研究人员估计效应量准确性的原因后，我们提供了研究人员可以在自己的分析中使用的R代码行。（PsycInfo数据库记录（c）2020阿帕，保留所有权利）。
Missing data is a common feature of experimental datasets. Standard methods used by psychology researchers to handle missingness rely on unrealistic assumptions, invalidate random assignment procedures, and bias estimates of effect sizes. We describe different classes of missing data typically encountered in experimental datasets, and we discuss how each of them impacts researchers' causal inferences. In this tutorial, we provide concrete guidelines for handling each class of missingness, focusing on 2 methods that make realistic assumptions: (a) inverse probability weighting (IPW) for mild instances of missingness, and (b) double sampling and bounds for severe instances of missingness. After reviewing the reasons why these methods increase the accuracy of researchers' estimates of effect sizes, we provide lines of R code that researchers may use in their own analyses. (PsycInfo Database Record (c) 2020 APA, all rights reserved).