Multiple data imputation to use in model

I have a followup question for multiple data imputation. So I been reading a bit about this method and I am still confused. So lets say I have m=5 datasets from multiple imputation. I extract the five data files and run them through an indexing model. So for my example, I need the missing data to be computed into one single variable output. The model calculates from three imputed variables to produce a single indexing measure. I use R for imputation and take the extracted data to Matlab (to be run through the model). Do I need to do the indexing for each single m=5 datasets?

So how would I pool the data together at the end? If I have only one variable from three imputed variables? Do I even need to do the pooling? Would justifying my process in data variable prediction and convergence as being sufficient without pooling?

If there is a reading I can look into that would be very helpful. I am so confused at this step. Any help will be appreciated.

• Could you say a bit more about what you mean by your "indexing model"? Does the output of the "indexing model" include some measure of the variance of the result? – EdM Mar 25 '16 at 21:02
• I am running the water indexing model and my data is purely numeric. They are just measures of water parameters. The index output does not measure variance. I will run a trend analysis at the end. Can't I just run the variance for all the datasets prior before I use them? Do I need to use all the datasets and can I just select one out of five based on the closeness of it to the original dataset? – Sasha Mar 25 '16 at 21:11
• You have to use all of your datasets when you do multiple imputation. You combine analysis results across the five datasets. See the section entitled: "Combining Analysis Results": csos.jhu.edu/contact/staff/jwayman_pub/… – StatsStudent Mar 25 '16 at 21:37

As with any multiple imputation, it is important to consider the details of the imputation methods carefully, as the defaults used in packages like mice in R might not be appropriate for your particular application. If you perform imputations appropriately, the differences among the indices determined from the individual imputations will provide information about the uncertainty introduced by the imputations, even though each index value itself has no associated error.