Missing data is another one of those topics that is largely ignored in most introductory texts. Probably part of the reason why this is the case is that many myths about analysis with missing data still abound. Additionally, some of the research into cutting-edge techniques is still relatively new. A more legitimate reason for its absence in introductory texts is that most of the more principle methodologies are fairly complicated, mathematically speaking.
Nevertheless, the incredible ubiquity of problems related to missing data in real-life data analysis necessitates some broaching of the subject. This section serves as a gentle introduction to the subject and one of the more effective techniques for dealing with it.
A common refrain on the subject is something along the lines of the best way to deal with missing data is not to have any. It's true that missing data is a messy subject, and there are a lot of ways to do it wrong. It's important not to take this advice...