Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.
The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.
Table of Contents
Basic Concepts. Weighting Methods. Imputation. Multiple Imputation. Regression Analysis. Longitudinal Analysis with Missing Values. Nonignorable Missing Data Mechanisms. Other Applications. Other Topics. Bibliography. Index.
Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a range of statistical and public health journals. His research interests include applied regression analysis, linear models, design of experiments, sample survey methods, and Bayesian inference.
"… This book describes, both in simple and technical terms, several easy-to-implement methods, discusses the underlying assumptions of each method clearly and provides means for assessing these assumptions supplemented with practical implementations. … In short, the author has written an interesting and highly valuable book, which intends to serve the need of graduate students and statistical practitioners, working, for instance, with data from sample surveys or from longitudinal studies or from survival studies."
—Apostolos Batsidis, in the Zentralblatt MATH, February 2018
"… This monograph presents a very readable introduction to methods for dealing with missing values in survey data. Its focus is firmly on practical aspects, leaving the underlying theory for further reading and study of the many references, which include groundbreaking work by Donald Rubin.
The first four chapters are an effective exercise in convincing the reader that some of the simple-minded approaches are deficient. The narrative motivates methods that have integrity, developing them step by step to address the identified deficiencies. The arguments presented are illustrated on examples and survey programs. They promote multiple imputation as a general method and elaborate the conditions under which they are appropriate. … Every chapter is concluded with biographical notes and a set of exercises, some of which can be developed into substantial projects. Altogether, a wealth of experience, wisdom, insight and sage advice is packed into a thin volume."
—Nicholas T. Longford, in Mathematical Reviews Clippings, November 2017
"… The presentation in Missing Data Analysis in Practice has the feel of well-honed lecture material … It should be understood that a text that barely clears 200 pages is not going to cover the entirety of what specialists need to know to become expert on the topic. But as an overview of the field,