Dear Impute list--I was hoping that I could get your thoughts on the following two questions related to multiple imputation (MI). 1) Do you know of any way to combine R-squared from a multiple regression using MI? I don't know what the SE would be to combine and test using Rubin's rules. Would one have to do a multiparameter inference testing whether all the regression coefficients simultaneously differ from zero? 2) I have noticed from MI practice that the degrees of freedom (df) after combination following MI can be quite different (often much larger) than the sample N. I can see from the formula for df that it depends on a few things, such as the rate of missing information, the number of imputations, etc. In order to report dfs in a manuscript I am working on that uses MI, a colleague of mine recommended looking into the use of adjusted df from a paper in the 90s by Bernard and Rubin (correct?) rather than df based on the sample size. Would you agree with this suggestion? Would you suggest an alternative approach? I tried to find the Bernard and Rubin paper, but I have been unsuccessful. Thank you for your time and your thoughts on these issues. J-P Jean-Philippe Laurenceau, Ph.D. Department of Psychology University of Delaware 218 Wolf Hall Newark, DE 19716-2577 Voice: (302) 831-2309 Fax: (302) 831-3645 [EMAIL PROTECTED]
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