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)

中文翻译：

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实验中缺失的数据：挑战和解决方案。

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以及效应大小的偏差估计。我们描述了实验数据集中通常遇到的不同类别的缺失数据，并讨论了它们中的每一个如何影响研究人员的因果推理。在本教程中，我们提供了处理每类缺失的具体指南，重点介绍了两种做出现实假设的方法：(a) 轻度缺失实例的逆概率加权 (IPW)，以及 (b) 严重实例的双采样和界限的缺失。在回顾了这些方法提高研究人员对效应大小估计准确性的原因之后，我们提供了研究人员可以在他们自己的分析中使用的 R 代码行。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）