Chapter 15. Advanced methods for missing data

In previous chapters, we focused on the analysis of complete datasets (that is, data-sets without missing values). Although doing so has helped 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.

15.1. Steps in dealing with missing data

15.2. Identifying missing values

15.3. Exploring missing values patterns

15.4. Understanding the sources and impact of missing data

15.5. Rational approaches for dealing with incomplete data

15.6. Complete-case analysis (listwise deletion)

15.7. Multiple imputation

15.8. Other approaches to missing data

15.9. Summary