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]
