# Handling missing data with Amelia

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So, what if you have data, but some of the observations are missing? Many statistical techniques assume no missingness, so we might want to “fill in” or rectangularize our data, by replacing missing observations with plausible substitutes. There are many ways of going about this, but one of the most robust and accessible is through the Amelia package.

Today’s Gist applies multiple imputation to some sample ANES survey data, and compares listwise-deleted regression results to results pooled from the same regression run on ten imputed data sets. Amelia makes this imputation, modeling, and recombination straightforward, and I’ve thrown in a nice coefficient plot (using position_dodge!) to illustrate the differences between missing data approaches.

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