How to handle missing (both random & non-random) data?

General
1

I am working with a small sample (n=60) but some of the variables have random missingness while others have non-random missingness.

My questions are:

  1. Can I use multiple imputations to handle both random and non-random missingness? If not, how should I handle the non-random missingness?
  2. Can I use multiple imputations in my data at all given my very small size? I have about ~15% missing data.

Any advice or R-code demonstration are greatly appreciated.

2

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