# Missing rates and multiple imputation

Is there a limit which is the least acceptable when using multiple imputation (MI)?

For example can I use MI if the missing values in a variable are the 20% of the cases while and other variables have missing values but not to such a high level?

How much is ‘too much’ missingness therefore depends on how much added variance/uncertainty you are willing to put up with. A useful quantity for you might be the relative efficiency ($RE$) of an MI analysis. This depends on the ‘fraction of missing information’ (not the simple rate of missingness), usually called $\lambda$, and the number of imputations, usually called $m$, as $RE \approx 1/(1+\lambda/m)$.