18 Advanced methods for missing data

This chapter covers

  • Identifying missing data
  • Visualizing missing-data patterns
  • Deleting missing values
  • Imputing missing values

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 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 the social sciences) involve missing ...

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