Anyone working with data should be concerned about missing data...or they shouldn't be working with data.

One way to address missing data is multiple imputation. This is a complex topic, and many textbooks and articles have been published about it. Four I can recommend are:

- Applied Missing Data Analysis (Enders)
- Handbook of Missing Data Methodology (Molenberghs et al.)
- Flexible Imputation of Missing Data (van Buuren)
- Outlier Analysis (Aggarwal)

The Enders book is probably the most basic and readable of the ones I've listed.

The Stata 16 reference manual on multiple imputation, linked below, also provides a good overview of this subject, and includes many examples and a glossary of key terms.

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