While the MI package will automatically combine estimates and standard errors from analysis models across datasets, the formulas are not themselves very complicated (I hope Ken Scheve doesn't take that as any knock against the usefulness of those procedures). Run your analysis, or compute your means, or whatever parameters you are interested in, in each dataset, then average these to get your quantity of interest. For the standard errors in these estimates, the formula has a second term. You need to average the standard errors, and also add uncertainty if the different datasets are predicting different model parameters. The formula is in: http://gking.harvard.edu/files/evil.pdf in equation (3). It is the same formula used in model averaging, only here the model weights are equal, since each imputed dataset is equally valid. regards, James Honaker On Sun, 30 Nov 2003, paul and penny wrote: - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia