I am currently running a multiple regression model using imputed data and have a few questions.
Using SPSS 18. My data appears to be MAR. Listwise deletion of cases leaves me with only 92 cases, multiple imputation leaves 153 cases for analysis. All assumptions met - one variable log transformed. 9 IV's 5 - 5 categorical, 3 scale, 1 interval. DV-scale. Using the enter method of standard multiple regression.
- My DV is the difference of scores between a pre- score and a post score measure, both of these variables are missing a number of cases - should I impute missing values for each of these and then work out the differnce between them to calculate my DV (how do I go about doing this), or can I just impute data for my DV? Which is the most appropriate approach?
- Should I run imputations on transformed data or skewed untransformed data?
- Should I enter all variables into the imputation process, even if they are not missing data, or should I just impute data for the variables missing more than 10% of cases?
I have run the regression on the listwise deleted cases and my IV's account for very little of the variance in my DV, subsequently I have run the regression on a complete file following multiple imputation - The results are very similar, in that my 9 IV's still predict only approx 12% of the variance in my DV, however, now one of my IV'S indicates that it is making a significant contribution (this happens to be a log transformed variable)...
- Should I report original data if there is little difference between my conclusions - i.e my IV's poorly predict the dv, or report the complete data?