Missing Data Analysis in Practice

By Trivellore Raghunathan

© 2015 – Chapman and Hall/CRC

210 pages | 18 B/W Illus.

Purchasing Options:
Hardback: 9781482211924
pub: 2015-10-28
US Dollars$79.95

e–Inspection Copy

About the Book

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.


"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

Table of Contents

Basic Concepts


Definition of Missing Values

Missing Data Pattern

Missing Data Mechanism

Problems with Complete-Case Analysis

Analysis Approaches

Basic Statistical Concepts

A Chuckle or Two

Weighting Methods


Adjustment Cell Method

Response Propensity Model


Impact of Weights on Population Mean Estimates


Survey Weights

Alternative to Weighted Analysis

Inverse Probability Weighting


Generation of Plausible Values

Hot Deck Imputation

Model Based Imputation


Sequential Regression Imputation

Multiple Imputation


Basic Combining Rule

Multivariate Hypothesis Testing

Combining Test Statistics

Basic Theory of Multiple Imputation

Extended Combining Rules

Some Practical Issues

Revisiting Examples

Example: St. Louis Risk Research Project

Regression Analysis

General Observations

Revisiting St. Louis Risk Research Example

Analysis of Variance

Survival Analysis Example

Longitudinal Analysis with Missing Values


Imputation Model Assumption


Practical Issues

Weighting Methods

Binary Example

Nonignorable Missing Data Mechanisms

Modeling Framework


Inference under Selection Model

Inference under Mixture Model


Practical Considerations

Other Applications

Measurement Error

Combining Information from Multiple Data Sources

Bayesian Inference from Finite Population

Causal Inference

Disclosure Limitation

Other Topics

Uncongeniality and Multiple Imputation

Multiple Imputation for Complex Surveys

Missing Values by Design

Replication Method for Variance Estimation

Final Thoughts



Bibliographic Notes and Exercises appear at the end of each chapter.

About the Author

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.

About the Series

Chapman & Hall/CRC Interdisciplinary Statistics

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General