Missing Data Analysis in Practice (Hardcover)

Basic Concepts
Introduction
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
Motivation
Adjustment Cell Method
Response Propensity Model
Example
Impact of Weights on Population Mean Estimates
Post-Stratification
Survey Weights
Alternative to Weighted Analysis
Inverse Probability Weighting

Imputation
Generation of Plausible Values
Hot Deck Imputation
Model Based Imputation
Example
Sequential Regression Imputation

Multiple Imputation
Introduction
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
Introduction
Imputation Model Assumption
Example
Practical Issues
Weighting Methods
Binary Example

Nonignorable Missing Data Mechanisms
Modeling Framework
EM-Algorithm
Inference under Selection Model
Inference under Mixture Model
Example
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.