Chapter 18. Advanced methods for missing data
This chapter covers
- Identifying missing data
- Visualizing missing data patterns
- Complete-case analysis
- Multiple imputation of missing data
In previous chapters, we focused on analyzing complete datasets (that is, datasets without missing values). Although doing so helps simplify the presentation of statistical and graphical methods, in the real world, missing data are ubiquitous.
In some ways, the impact of missing data is a subject that most of us want to avoid. Statistics books may not mention it or may limit discussion to a few paragraphs. Statistical packages offer automatic handling of missing data using methods that may not be optimal. Even though most data analyses (at least in social sciences) involve missing data, this topic is rarely mentioned in the methods and results sections of journal articles. Given how often missing values occur, and the degree to which their presence can invalidate study results, it’s fair to say that the subject has received insufficient attention outside of specialized books and courses.