Applied Multiple Imputation. Advantages, Pitfalls, New Developments and Applications in R
Abstract
The book provides an introduction to missing data and multiple imputation for students and applied researchers, features numerous step-by-step tutorials in R with supplementary R code and data sets, and discusses the advantages and pitfalls of multiple imputation, and presents current developments in the field.
Chapters
- Introduction and Basic Concepts pp 1-22
- Missing Data Mechanism and Ignorability pp 23-52
- Missing Data Methods pp 53-83
- Multiple Imputation: Theory pp 85-131
- Multiple Imputation: Application pp. 133-217
- Multiple Imputation: New Developments pp. 219-256
This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice). The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master’s and PhD students with a sound basic knowledge of statistics.