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

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