I have an experiment wherein respondents were tested in two time points. However, respondents were tested at t1 and t2 OR t1 and t3 OR t1 and t4. Hence, data is missing at t2,t3,and t4 for 3/4 of respondents. I meet the missing at random assumption, but is there anything else I need to consider before I start up the imputation machine?
Instead of imputing something that seems almost like a planned missingness design (which would fit the MCAR assumption), I would take a look growth curves. These actually adequately handle data that are either missing completely at random or missing at random using maximum likelihood estimation.