15.9. Summary

Most statistical methods assume that the input data is complete and doesn’t include missing values (for example, NA, NaN, Inf). But most datasets in real-world settings contain missing values. Therefore, you must either delete the missing values or replace them with reasonable substitute values before continuing with the desired analyses. Often, statistical packages will provide default methods for handling missing data, but these approaches may not be optimal. Therefore, it’s important that you understand the various approaches available, and the ramifications of using each.

In this chapter, we examined methods for identifying missing values and exploring patterns of missing data. Our goal was to understand the mechanisms that led to the missing data and their possible impact on subsequent analyses. We then reviewed three popular methods for dealing with missing data: a rational approach, listwise deletion, and the use of multiple imputation.