Missing Data Analysis in Practice

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-StratificationSurvey Weights Alternative to Weighted Analysis Inverse Probability Weighting ImputationGeneration of Plausible Values Hot Deck ImputationModel 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 IssuesRevisiting 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.

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.