Missing Data: A Gentle Introduction
by Patrick E. McKnight, Katherine M. McKnight, Souraya Sidani, and Aurelio Jose Figueredo
Published by Guilford
While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study’s conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed—such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures—the book also explains how to make sound decisions about their use.
Data and Source Code
Most of the datasets come directly from the R statistical package, however, some data were either generated by us or were downloaded from the internet. We chose to post all the data from the book since no data were copyrighted. Additionally, we have some R code examples so interested readers can learn to use R for their missing data problems.
Our original intent was to publish an annotated bibliography as an appendix. An annotated bibliography would allow researchers and students to survey the available literature and search for literature that suits their abilities and tastes. That idea seemed reasonable except for the fact that the missing data literature seems to be growing at a rapid rate - far more rapid than our ability to revise the book. So we decided to publish the appendix online and update the records as the literature changed. You may find the latest version of the annotated bibliography here. If you find a glaring omission, please feel free to contact us and we will update the file as soon as humanly possible.