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I am using instrumental variables and I have missing data. In r, I don't believe you can use the MICE package with the AER package. Therefore, I am going to impute with either the mean or median values. My variable is heavily skewed, so I am incline to use the median value. Do researchers use the median-imputation? I am more familiar with researchers using mean-imputation.

Is there an alternative to imputation with the mean and median value? If you can use the AER and MICE packages together, could someone point me to code that uses it?

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    $\begingroup$ Single imputation of the type you intend to use will result in seriously underestimated standard errors of your final parameter estimates, and false significance. I would only use that approach if the proportion of incomplete observations is tiny. $\endgroup$ – Frank Harrell Aug 14 '15 at 18:15
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The hard part about multiple imputation is the imputation, which mice can do even if for some reason you can't use the additional pooling functionality it provides. A quick look at the AER package suggests that it is mostly data, with examples using many functions like lm that certainly are compatible with mice.

If you do find a particular analysis function that is not compatible with the pooling functions of mice, proceed as follows, following the general procedure for multiple imputation:

  1. Do your multiple imputations with mice and keep each separate complete imputed data set.

  2. Perform your analysis separately on each of the imputed data sets.

  3. Based on your understanding of the nature of the analysis method and the goals of your study, pool the results of the separate analyses appropriately. This can be as simple as taking means and variances of coefficients over the multiple imputation analyses.

This process is much better than relying on single imputations, and may have the side effect of helping you learn more about how the particular analysis method in question works.

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