Multiple imputation of multilevel missing data: An introduction to the R package pan

TL;DR

This paper introduces the R package pan for multiple imputation of multilevel missing data, making advanced statistical methods more accessible to researchers through practical examples and integration with user-friendly tools.

Contribution

It provides an accessible introduction to using pan for multilevel data imputation, including integration with mitml for easier application in research.

Findings

Demonstrated use of pan and mitml with empirical examples

Discussed limitations and possibilities of pan in multilevel research

Showed how to integrate pan with other software tools

Abstract

The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss its possibilities and limitations in accommodating typical questions in multilevel research. In order to make pan more accessible to applied researchers, we make use of the mitml package, which provides a user-friendly interface to the pan package and several tools for managing and analyzing multiply imputed data sets. We illustrate the use of pan and mitml with two empirical examples that represent common applications of multilevel models, and we discuss how these procedures may be used in conjunction with other software.