I have a large dataset with large amounts of missing data.
My data involves particular cognitive tests and I would like to see how they are related to academic attainment controlling for SES and IQ.
I would also like to impute missing values using multiple imputation.
I have been looking at the relationship between the amount of missing data each participant has, and both how well they do on the cognitive tests and in academic achievement. I have found that although there is no relationship between how much missing data one has and how well they do on cognitive tests, there is a relationship between how much missing data they have and how well they do at school, their SES and IQ. This could be for many reasons but I assume this means my data is not missing at random?
I was wondering whether if I ran multiple imputation on this dataset and included the correlated variables in the imputation model whether this would account for the relationship? Or if not, how I might be able to deal with this situation.