Best way to handle missing data?
On 28 February 2015 at 07:00, Bonnie Dixon <bmdixon at ucdavis.edu> wrote:
Given that, I am now working on a multiple imputation solution for my problem, using either mice or Amelia, and will post again to the list once I have a working example. (Apparently, I was wrong about mice only being able to impute one variable.) How many imputations are needed? Many sources online indicate that 3-10 is usually enough, and the default in both mice and Amelia is 5.
Others claim 20, and that seems to be more than sufficient for a lot of problems. It will depend on what proportion of your data is missing, and how dependent the outcome is on these. As you generally can't have too many then I would start with say 20 and then try a couple of larger number and if there is no change then 20 was sufficient.