I imputed the missing data using predictive mean matching through the MICE package in R, I did 5 cycles for each imputation.

**My question is how do I choose which cycle to use for the whole dataset?**

I know that for each variable it should be as close as possible to the original mean, however say I choose the 5th iteration for one variable- as I can see the new mean is the closest to the original mean- that same iteration may not be the best one to choose for the other 39 variables.

**So do I have to do separate imputations for each of the 40 variables on 40 separate datasets, export those datasets and then combine them all at the end?**

**Or is it safe to just choose any of the iterations and apply it to all 40 variables in one go?**

Disclaimer I have a very basic stats training and am coming from the field of Linguistics not stats, so I'm in way over my head.

Note: I can't pool the data into a model because this specific dataset only contains my dependent variables. (They are in a different format to my independent variables, so I need to first impute the data and then average each variable, input those averages into another dataset with the predictor variables, and then finally run a model with those averages and my predictor variables