Yup, wrong answer, unless the statistic is linear in all the missing data, i.e., this could only work in your case if the only varible with missingness is y. ANd even then, all the standard errors and tests are wrong. Not a very successful path to follow.
On Fri, 25 Jul 2003, Steve Peck wrote: > Assuming a set of 20 continuous variables, > are there specific reasons for *not* combining the > results of 5 MI data sets before doing regression analyses > (e.g., by computing value estimates by averaging across > the 5 values per variable) instead of combing the parameter > estimates that are generated from each of the 5 models run > seperately)? > > thanks, > Steve > > -- Donald B. Rubin John L. Loeb Professor of Statistics Chairman Department of Statistics Harvard University Cambridge MA 02138 Tel: 617-495-5498 Fax: 617-496-8057
