1.7 Structure of the book
This book consists of three main parts: basics, case studies and extensions. Chapter 2 reviews the history of multiple imputation and introduces the notation and theory. Chapter 3 provides an overview of imputation methods for univariate missing data. Chapter 4 distinguishes three approaches to attack the problem of multivariate missing data. Chapter 5 reviews issues pertaining to the analysis of the imputed datasets.
Chapter 6 discusses practical issues for multivariate missing data. Chapter 7 discusses the problem how to impute for nested data so as to preserve the multilevel structure. Chapter 8 explores the use of multiple imputation to estimate individual causal effects.
Chapters 9–11 contain case studies of the techniques described in the previous chapters. Chapter 9 deals with “problems with the columns,” while Chapter 10 addresses “problems with the rows”. Chapter 11 discusses studies on problems with both rows and columns.
Chapter 12 concludes the main text with a discussion of limitations and pitfalls, reporting guidelines, alternative applications and future extensions.