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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.



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

Bibliography

Index

Bibliographic Notes and Exercises appear at the end of each chapter