Multiple Imputation

I’m sure I don’t need to explain to you all the problems that occur as a result of missing data.  Anyone who has dealt with missing data—that means everyone who has ever worked with real data—knows about the loss of power and sample size, and the potential bias in your data that comes with listwise deletion.

Listwise deletion is the default method for dealing with missing data in most statistical software packages.  It simply means excluding from the analysis any cases with data missing on any variables involved in the analysis.

A very simple, and in many ways appealing, method devised to (more…)

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Two excellent resources about multiple imputation and missing data:

Joe Schafer’s Multiple Imputation FAQ Page gives more detail about multiple imputation, including a list of references.

Paul Allison’s 2001 book Missing Data is the most readable book on the topic. It gives in-depth information on many good approaches to missing data, including multiple imputation. It is aimed at social science researchers, and best of all, it is very affordable (about $15).

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