Multiple imputation of missing covariate values in multilevel models with random slopes: A cautionary note

TL;DR

This paper examines the challenges and limitations of using multiple imputation for missing covariate data in multilevel models with random slopes, highlighting current software constraints and potential alternatives.

Contribution

It provides a critical analysis of multiple imputation in complex multilevel models with random slopes, including simulation results and practical recommendations.

Findings

MI recovers most parameters but struggles with slope variation.

MI provides reasonable estimates even with small samples or assumption violations.

Listwise deletion can be a viable alternative when slope variance preservation is crucial.

Abstract

Multiple imputation (MI) has become one of the main procedures used to treat missing data, but the guidelines from the methodological literature are not easily transferred to multilevel research. For models including random slopes, proper MI can be difficult, especially when the covariate values are partially missing. In the present article, we discuss applications of MI in multilevel random-coefficient models, theoretical challenges posed by slope variation, and the current limitations of standard MI software. Our findings from three simulation studies suggest that (a) MI is able to recover most parameters, but is currently not well suited to capture slope variation entirely when covariate values are missing; (b) MI offers reasonable estimates for most parameters, even in smaller samples or when its assumptions are not met; and (c) listwise deletion can be an alternative worth considering when preserving the slope variance is particularly important.