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).

中文翻译：

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教程：使用不可忽略的局部敏感性指数评估不可忽略的缺失对回归分析的影响。

缺少观察的数据集在心理学研究中很常见。人们通常通过应用统计方法来分析此类数据，这些方法依赖于缺失观测值随机缺失 (MAR) 的假设。这种假设极大地简化了分析，但无法从手头的数据中验证，并且错误的假设可能会导致偏差。因此，我们经常希望进行敏感性分析来判断结论对于偏离 MAR 是否稳健，即即使 MAR 实际上不成立，关键发现是否会成立。本文介绍了一类敏感性分析，该分析源自称为不可忽略局部敏感性指数 (ISNI) 的鲁棒性度量。ISNI 计算简单，避免了复杂的非 MAR 缺失数据模型的估计。随附的 R 包 isni 实现了一系列常用回归模型的方法；语法很简单，类似于假设 MAR 的常规分析。我们展示了该方法和软件的应用，以解决心理学研究中真实世界数据集的一系列分析中 MAR 分析的可信度。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。