Multiple Imputation of Missing Data for Multilevel Models

Multiple imputation (MI) is one of the principled methods for dealing with missing data. In addition, multilevel models have become a standard tool for analyzing the nested data structures that result when lower level units (e.g., employees) are nested within higher level collectives (e.g., work groups). When applying MI to multilevel data, it is important that the imputation model takes the multilevel structure into account. In the present paper, based on theoretical arguments and computer simulations, we provide guidance using MI in the context of several classes of multilevel models, including models with random intercepts, random slopes, cross-level interactions (CLIs), and missing data in categorical and group-level variables. Our findings suggest that, oftentimes, several approaches to MI provide an effective treatment of missing data in multilevel research. Yet we also note that the current implementations of MI still have room for improvement when handling missing data in explanatory variables in models with random slopes and CLIs. We identify areas for future research and provide recommendations for research practice along with a number of step-by-step examples for the statistical software R.