I have some questions that I hope someone can answer for me. Apologies in advance for the quantity.
I have realcom up and working with mlwin. I am running a 2 level model on cross sectional data. My exposure of interest is a level 2 variable measured using different approaches. Thus, I would like to run the model separately with each of these analogous level 2 variables. I would also like to look at the interaction between these exposure measures and another level 2 variable. My questions are as follows:
I am unclear on the process for utilising realcom in the above situation. Do I create an imputed file using all of the different exposure variables and then construct and run each model and then subsequently run the model again using the same imputed file? Or, do I need to create a separate imputed file for each model?
If I have run the analysis with the imputed file and I would like to adjust the model (to for example look for a cross level interaction) do I adjust it and run the model and then run the model with the imputed file each time I make an adjustment?
If I have set up a model that includes dummy variables, when I set up the file to be exported to realcom do I include only the ‘base’ variable that the dummys are constructed from?
I would like to ‘centre’ some of my variables. Do I need to include the ‘centred’ versions in the file that is exported to realcom, or can I use the ‘base’ variable?
I am unclear how to account for interaction terms in the imputation? Do I need to include anything specific in the file to be exported to realcom and do anything special in realcom?
Lastly, I have run the model with and without the imputed realcom file. In the case without the imputed file the mlwin output reports a number of individuals that is less than the number in the data set. This number roughly corresponds to the number I would expect if the cases with missing data are dropped. I have subsequently run the model with the imputation file from realcom. The number of individuals shown in the output does not change (though the beta co-efficients and the standard errors do). I don’t understand this. My expectation is that because the missing variables have been imputed that the number of individuals should now be the same as the full number of cases in the data set.
Many thanks in advance
Shane
