The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students.


Chapter 1: Introduction to Missing Data
Chapter 2: Maximum Likelihood Estimation
Chapter 3: Maximum Likelihood Estimation With Missing Data
Chapter 4: Bayesian Estimation
Chapter 5: Bayesian Estimation with Missing Data Imputation
Chapter 6: Bayesian Estimation for Categorical Variables
Chapter 7: Multiple Imputation
Chapter 8: Multilevel Missing Data
Chapter 9: Missing Not at Random Processes
Chapter 10: Special Topics and Applications
Chapter 11: Wrap-Up


"The second edition of Applied Missing Data Analysis is a bold, top-to-bottom revision that makes a phenomenal book even better. This book is exemplary teaching that you can hold in your hands."

Gregory Hancock, PhD

University of Maryland

"The book makes sophisticated statistics amazingly accessible and offers a great deal to a wide audience, including statisticians, data analysts, substantive researchers, and quantitative students "

Donald Hedeker, PhD

University of Chicago

"Simply stated, this is the best textbook available on missing data analysis. The excellent companion website provides important, updated resources for teaching and learning."

keenan A. Pituch, PhD

Arizona State University

"Thorough, cutting-edge, and far and away the clearest text available on missing data analysis. Reading this book feels like being guided by the author through a comprehensive one-on-one workshop. A gift to the field!"

Sonya Sterba, PhD

Vanderbilt University

Get to know

The Author

Craig K. Enders, PhD
Craig Enders is a Professor and the Area Chair of Quantitative Psychology in the Department of Psychology at University of California—Los Angeles. Dr. Enders’ primary research focus is on analytic issues related to missing data analyses, and he leads the research team responsible for developing the Blimp software application for missing data analyses. He also conducts research in the areas of multilevel modeling and structural equation modeling and is an active member of SMEP, APA, and AERA.