Hello I guess that is a common problem when imputing data, but I am rather confused by it. The availability (= not-NA-ness) of my survey data looks like this (-> screenshot). It shows the number of non-NA entries for each variable in the data frame. Areas A and C have rather complete answer sets. B is white because these questions have only been asked conditional on answers given beforehand (in A). But a part of the white area (in D) -could- be imputed. How does the imputation process look like? What to do with B? I could think of two variants that are both more or less unclear to me: A) Should I cut the dataframe and impute in two steps? But how do I then reintegrate the results from the first imputation? i) remove B+D, impute A+C ii) reinsert B/D and impute D. B) How do I specify the process for one MI step without cutting? Thank you very much for your thoughts on this. Best regards, Marcus -- Marcus M. Dapp | PhD student | ETH Zurich | www.ib.ethz.ch/people/mdapp Prof. Thomas Bernauer, International Relations | www.ib.ethz.ch On the shoulders of giants? http://science.creativecommons.org