Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem.

This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field.

This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

part 1|1 pages


chapter 1|26 pages


chapter 2|34 pages

Multiple imputation

chapter 3|41 pages

Univariate missing data

chapter 5|21 pages

Analysis of imputed data

part 2|1 pages

Advanced techniques

chapter 6|34 pages

Imputation in practice

chapter 7|44 pages

Multilevel multiple imputation

chapter 8|15 pages

Individual causal effects

part 3|1 pages

Case studies

chapter 9|36 pages

Measurement issues

chapter 10|15 pages


chapter 11|25 pages

Longitudinal data Failure

part 4|1 pages


chapter 12|12 pages