© 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.
"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.