Jing Bocheng, Qian Yi, Heitjan Daniel F, Xie Hui
Faculty of Health Sciences, Simon Fraser University.
Division of Marketing & Behavioral Sciences, Sauder School of Business, University of British Columbia.
Psychol Methods. 2023 Nov 16. doi: 10.1037/met0000616.
Data sets with missing observations are common in psychology research. One typically analyzes such data by applying statistical methods that rely on the assumption that the missing observations are missing at random (MAR). This assumption greatly simplifies analysis but is unverifiable from the data at hand, and assuming it incorrectly may lead to bias. Thus we often wish to conduct sensitivity analyses to judge whether conclusions are robust to departures from MAR-that is, whether key findings would hold up even if MAR does not in fact hold. This article describes a class of sensitivity analyses derived from a measure of robustness called the Index of Local Sensitivity to Nonignorability (ISNI). ISNI is straightforward to compute and avoids the estimation of complicated non-MAR missing-data models. The accompanying R package isni implements the method for a range of commonly used regression models; the syntax is simple and similar to that for the regular analysis that assumes MAR. We illustrate the application of the method and software to address the credibility of MAR analyses in a series of analyses of real-world data sets from psychology research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
在心理学研究中，存在缺失观测值的数据集很常见。人们通常通过应用依赖于缺失观测值是随机缺失（MAR）这一假设的统计方法来分析此类数据。这一假设极大地简化了分析，但无法从手头的数据进行验证，错误地假设它可能会导致偏差。因此，我们常常希望进行敏感性分析，以判断结论对于偏离MAR的情况是否稳健——也就是说，即使MAR实际上不成立，关键发现是否仍然成立。本文描述了一类源自一种称为非可忽略性局部敏感性指数（ISNI）的稳健性度量的敏感性分析。ISNI计算简单，避免了对复杂的非MAR缺失数据模型的估计。随附的R包isni为一系列常用回归模型实现了该方法；语法简单，与假设MAR的常规分析类似。我们通过对心理学研究中的一系列真实世界数据集的分析，说明了该方法和软件在解决MAR分析可信度方面的应用。（PsycInfo数据库记录（c）2023美国心理学会，保留所有权利）
