I am working with my resident survey data (n=8356) including 59 items, most of which are ordinal variables scored from 1 to 7, and others are continuous variables (e.g., age, residency length).
However, all items have some missing values, ranging from 40 to 200, which follow an arbitrarily missing pattern. This leaves me with 6001 complete observations but more than 2000 incomplete records. How can I impute the missing values in this situation?
I realize that I could use single or multiple imputation method, while it seems that both require variables without any missing value, which is not the case in my data.
Additionally, I want to generate some composite variables based on the individual items and then run my analysis model, so how to get the combined estimates if I use multiple imputation method such as chained equations?
Thanks for any help.