Tutorial: Assessing the Impact of Nonignorable Missingness on Regression Analysis Using Index of Local Sensitivity to Nonignorability

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.