Multiple imputation of missing data in longitudinal organizational research: A step-by-step guide

Missing data are a common problem in longitudinal studies, and longitudinal designs pose unique challenges for the appropriate handling of missing data in occupational and organizational research. However, state-of-the-art methods such as multiple imputation (MI) are still used only sparingly in longitudinal studies, and the translation of methodological advice into practice can be challenging in these types of designs. The present article aims to guide researchers in the application of MI in longitudinal studies by providing an overview of the techniques and challenges associated with longitudinal MI as well as three step-by-step examples that focus on applications with different levels of complexity (e.g., scale- vs. item-level data) and different strategies for longitudinal MI (e.g., with single- vs. multilevel models in wide- and long-formatted data). In this context, we place a specific emphasis on flexible approaches to MI, which can be used with many different longitudinal designs and analyses, and we provide practical recommendations for common challenges. For each example, we provide extensive materials including the computer code, example data, and annotated versions of each example.