  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.
