Looking for some suggestions here - I've got NES data (2000 Presidential election to be exact) merged with crime rate data (county level) from the FBI. I'm imputing 48 variables (10 fully observed) of which two are missing @ 50% (n = 1768). I have already transformed all the variables to be as close to normal as possible a'la the suggestions in Honaker, etal (2001), I'm using a ridge prior of 6, and I've increased the _AMsn global to 100. Despite all this Amelia still crashes during stage 3 (Importance Sampling) and reports the error message : There are insufficient valid draws from the approximating distribution so the program will end. This error may occur for a number of reasons including severe missingness and data which badly violates the assumptions of the imputation model. The user should be able correct the problem by increasing the draws from the approximating distribution (see _AMsn global); by transforming the variables to more closely meet the model's distributional assumptions; by using or increasing the strength of the prior; by using the t-distribution for the approximating distribution (see _AMst global); and/or adjusting the _AMsfac global. Press any key to exit Currently active call: RNDISMP [154] Now, I don't think the problem is the severe missingness because I was able to get Amelia to run on this data before I merged the crime data into the set (note: this was before I learned that scales, etc. require both the scale and it's components to be entered - thus the old dataset only @ 20 variables in it.) My question is, is it worth my time to keep increasing the _AMsn global (how high should you go?) or should I just bite the bullet and start playing with the _AMst and _AMsfac globals? If I change these, how much am I compromising the robustness of my imputation model? Matthew Vile, University of New Orleans