On this point Frank, I would suggest that any information you generate via your imputation process is dependent on the other information you have available in the imputation model. For example, if I wanted to impute a variable that would be used as a predictor in regression, and I have a lot of salient information about my respondents (associated attitudes, demographrics etc) that helped me predict this variable accurately, than my imputation process offers a substantial amount to my final estimation of that value. That does not differ when you are imputing a nominal or ordinal variable. Gary has tools available in amelia to evaluate the models performance. Using the density comparisons and overimpute offers this validation. Linking overimpute to the the datamining literature, you could look at the use of 'holdout cases' as a method of model validation that they typically use. Thanks Paul - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia