approaches. If you’re interested in the multiple imputation approach to missing data, I recommend the following resources:

■The multiple imputation FAQ page (www.stat.psu.edu/~jls/mifaq.html)

■Articles by Van Buuren and Groothuis-Oudshoorn (2010) and Yu-Sung, Gelman, Hill, and Yajima (2010)

■Amelia II: A Program for Missing Data (http://gking.harvard.edu/amelia)

Each can help to reinforce and extend your understanding of this important, but underutilized, methodology.

18.8 Other approaches to missing data

R supports several other approaches for dealing with missing data. Although not as broadly applicable as the methods described thus far, the packages described in table 18.2 offer functions that can be useful in specialized circumstances.

Table 18.2 Specialized methods for dealing with missing data

Finally, there are two methods for dealing with missing data that are still in use but should be considered obsolete: pairwise deletion and simple imputation.

18.8.1Pairwise deletion

Pairwise deletion is often considered an alternative to listwise deletion when working with datasets that are missing values. In pairwise deletion, observations are deleted only if they’re missing data for the variables involved in a specific analysis. Consider the following code: