© 2015 – Chapman and Hall/CRC
210 pages | 18 B/W Illus.
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.
"This is a short book covering some traditional methods of handling missing data points. The issue of missing data is a fact of life and happens frequently and in most datasets. Therefore, it is of great importance to take time and study the options or methods available for handing such points. … The book presents several issues related to missing data points along with examples using actual or simulated data to demonstrate the concepts. It mainly covers traditional approaches to handling missing data points. The datasets and codes used in the book are available online. … It is a good source for the researchers and also suitable for a course on missing data points. … Examples and exercises are very helpful to the readers."
—Morteza Marzjarani, Saginaw Valley State University (retired), in Technometrics, October 2016
"…provides a practical approach to applying methods for exploring the sensitivity of results to missing data. Each chapter includes interesting examples and exercises using real data to explore the topics covered…takes as its focus the problems data analysts encounter with missing data, and thus approaches each missing data strategy from this perspective. … unique in its serious treatment of these methods…a welcome addition to the literature on strategies for examining the sensitivity of results to missing data…provides accessible, practical advice to data analysts facing the ubiquitous problem of missing observations…The examples and exercises are also based on real data sets and illustrate the problems many data analysts encounter."
—Terri D. Pigott, Loyola University Chicago, in the Journal of Biopharmaceutical Statistics, September 2016
"This is a book for practitioners who want to know what methods are available, what missing data mechanisms they assume and who want insight on how the choice of method may affect the result. The theory is described using heuristics rather than mathematical rigour. There is exemplary care and attention to the almost inevitable mismatch between theory and the practical context."
—John H. Maindonald, International Statistical Review, 2016
"With a firm command of incomplete-data methodology and a smooth narrative, Missing Data Analysis in Practice provides an up-to-date overview of the field. Applied researchers will appreciate the book’s guidance on pragmatic issues like selecting the number of imputations, using transformations, and including the outcome when imputing missing covariates. Attention to finite-population estimation makes the book a valuable bridge between design-based and model-based perspectives. And with extensions to areas like longitudinal analysis, survival analysis, and disclosure avoidance, statisticians will find that the book complements the classic text by Little and Rubin."
—Tom Belin, Department of Biostatistics, UCLA
"If you don’t know missing data, you don’t know data. This slim and readable book covers a range of concepts and techniques for handling missing data in applied statistics."
—Andrew Gelman, Professor of Statistics and Political Science, Columbia University
Definition of Missing Values
Missing Data Pattern
Missing Data Mechanism
Problems with Complete-Case Analysis
Basic Statistical Concepts
A Chuckle or Two
Adjustment Cell Method
Response Propensity Model
Impact of Weights on Population Mean Estimates
Alternative to Weighted Analysis
Inverse Probability Weighting
Generation of Plausible Values
Hot Deck Imputation
Model Based Imputation
Sequential Regression Imputation
Basic Combining Rule
Multivariate Hypothesis Testing
Combining Test Statistics
Basic Theory of Multiple Imputation
Extended Combining Rules
Some Practical Issues
Example: St. Louis Risk Research Project
Revisiting St. Louis Risk Research Example
Analysis of Variance
Survival Analysis Example
Longitudinal Analysis with Missing Values
Imputation Model Assumption
Nonignorable Missing Data Mechanisms
Inference under Selection Model
Inference under Mixture Model
Combining Information from Multiple Data Sources
Bayesian Inference from Finite Population
Uncongeniality and Multiple Imputation
Multiple Imputation for Complex Surveys
Missing Values by Design
Replication Method for Variance Estimation
Bibliographic Notes and Exercises appear at the end of each chapter.