Applied Missing Data Analysis

Second Edition

Craig K. Enders

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August 25, 2022
ISBN 9781462549863
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546 Pages
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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. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website (www.appliedmissingdata.com) includes datasets and analysis examples from the book, up-to-date software information, and other resources.

New to This Edition

This title is part of the Methodology in the Social Sciences Series, edited by Todd D. Little, PhD.


“The book is well written….The author successfully achieved the goal of helping the reader to become familiar with basic concepts in missing data analysis procedures, and to feel comfortable using these procedures in a variety of practical and social science applications. It contains very useful examples and illustrations in the applied social sciences.”

American Statistician (on the first edition)


“The second edition of Applied Missing Data Analysis is a bold, top-to-bottom revision that makes a phenomenal book even better. Enders offers a completely updated treatment, including such important topics as models with continuous and categorical variables, Bayesian missing data approaches, methods for missing not at random processes, and even a guide to how researchers should report their missing data analyses. Beyond the central focus on missing data, I can already hear myself saying, 'Go read Enders's book!' to students and colleagues with questions about how maximum likelihood estimation works, the logic of Markov Chain Monte Carlo methods, and so much more. This book is exemplary teaching that you can hold in your hands. I will recommend it with the greatest enthusiasm to students, faculty, and applied researchers alike for many years to come.”

—Gregory R. Hancock, PhD, Professor and Distinguished Scholar-Teacher, Department of Human Development and Quantitative Methodology, University of Maryland, College Park


“Approaches for dealing with missing data have progressed greatly in the statistical and methodological literatures, and the second edition of this exemplary book thoroughly presents and synthesizes these developments. 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. I learn something new (or better understand something I thought I knew) every time I pick up this book! The presentation of how to report results from a missing data analysis, which gives explicit examples of such reporting for a wide variety of scenarios, is particularly useful. With an abundance of examples, figures, and illustrations to enhance the crystal-clear exposition, this is the 'go-to' book for dealing with missing data in statistical modeling.”

—Donald Hedeker, PhD, Department of Public Health Sciences, University of Chicago


“Thorough, cutting-edge, and far and away the clearest text available on missing data analysis. Written by a world-renowned expert who is a gifted instructor, this book is accessible enough for applied researchers with introductory statistics and regression knowledge, is an outstanding text for a missing data course, or can be used to fill gaps in methodologists’ understanding of the notoriously opaque missing data literature. For researchers who learned 'modern' missing data-handling methods years ago—much has changed. For instance, the second edition will bring you up to speed on how to accommodate missingness in conjunction with non-normal and discrete outcomes, nonlinear and interactive relationships, and multilevel structures; choose among non-model-based versus model-based multiple imputation methods; and conceptualize and implement sensitivity analyses to assess the impact of alternative missing data assumptions. Reading this book feels like being guided by the author through a comprehensive one-on-one workshop. A gift to the field!”

—Sonya K. Sterba, PhD, Professor of Psychology and Director, Quantitative Methods Program, Vanderbilt University


“Simply stated, this is the best textbook available on missing data analysis. The book provides comprehensive coverage, is highly accessible, and is written by one of the experts in the field. The concepts involved in missing data analysis are complex, and it is obvious that Enders takes the 'teaching mission' seriously. The writing is clear, the figures and tables are very helpful in promoting understanding, and the simulations developed for the text are helpful in conveying the strengths and weaknesses of various missing data treatments. The excellent companion website provides important, updated resources for teaching and learning. The software scripts available on the website are very useful for researchers wishing to apply the missing data methods to real data.”

—Keenan A. Pituch, PhD, Edson College of Nursing and Health Innovation, Arizona State University

Table of Contents

1. Introduction to Missing Data

1.1 Chapter Overview

1.2 Missing Data Patterns

1.3 Missing Data Mechanisms

1.4 Diagnosing Missing Data Mechanisms

1.5 Auxiliary Variables

1.6 Analysis Example: Preparing for Missing Data Handling

1.7 Older Missing Data Methods

1.8 Comparing Missing Data Methods via Simulation

1.9 Planned Missing Data

1.10 Power Analyses for Planned Missingness Designs

1.11 Summary and Recommended Readings

2. Maximum Likelihood Estimation

2.1 Chapter Overview

2.2 Probability Distributions versus Likelihood Functions

2.3 The Univariate Normal Distribution

2.4 Estimating Unknown Parameters

2.5 Getting an Analytic Solution

2.6 Estimating Standard Errors

2.7 Information Matrix and Parameter Covariance Matrix

2.8 Alternative Approaches to Estimating Standard Errors

2.9 Iterative Optimization Algorithms

2.10 Linear Regression

2.11 Significance Tests

2.12 Multivariate Normal Data

2.13 Categorical Outcomes: Logistic and Probit Regression

2.14 Summary and Recommended Readings

3. Maximum Likelihood Estimation with Missing Data

3.1 Chapter Overview

3.2 The Multivariate Normal Distribution Revisited

3.3 How Do Incomplete Data Records Help?

3.4 Standard Errors with Incomplete Data

3.5 The Expectation Maximization Algorithm

3.6 Linear Regression

3.7 Significance Testing

3.8 Interaction Effects

3.9 Curvilinear Effects

3.10 Auxiliary Variables

3.11 Categorical Outcomes

3.12 Summary and Recommended Readings

4. Bayesian Estimation

4.1 Chapter Overview

4.2 What Makes Bayesian Statistics Different?

4.3 Conceptual Overview of Bayesian Estimation

4.4 Bayes’ Theorem

4.5 The Univariate Normal Distribution

4.6 MCMC Estimation with the Gibbs Sampler

4.7 Estimating the Mean and Variance with MCMC

4.8 Linear Regression

4.9 Assessing Convergence of the Gibbs Sampler

4.10 Multivariate Normal Data

4.11 Summary and Recommended Readings

5. Bayesian Estimation with Missing Data

5.1 Chapter Overview

5.2 Imputing an Incomplete Outcome Variable

5.3 Linear Regression

5.4 Interaction Effects

5.5 Inspecting Imputations

5.6 The Metropolis–Hastings Algorithm

5.7 Curvilinear Effects

5.8 Auxiliary Variables

5.9 Multivariate Normal Data

5.10 Summary and Recommended Readings

6. Bayesian Estimation for Categorical Variables

6.1 Chapter Overview

6.2 Latent Response Formulation for Categorical Variables

6.3 Regression with a Binary Outcome

6.4 Regression with an Ordinal Outcome

6.5 Binary and Ordinal Predictor Variables

6.6 Latent Response Formulation for Nominal Variables

6.7 Regression with a Nominal Outcome

6.8 Nominal Predictor Variables

6.9 Logistic Regression

6.10 Summary and Recommended Readings

7. Multiple Imputation

7.1 Chapter Overview

7.2 Agnostic versus Model-Based Multiple Imputation

7.3 Joint Model Imputation

7.4 Fully Conditional Specification

7.5 Analyzing Multiply-Imputed Data Sets

7.6 Pooling Parameter Estimates

7.7 Pooling Standard Errors

7.8 Test Statistic and Confidence Intervals

7.9 When Might Multiple Imputation Give Different Answers?

7.10 Interaction and Curvilinear Effects Revisited

7.11 Model-Based Imputation

7.12 Multivariate Significance Tests

7.13 Summary and Recommended Readings

8. Multilevel Missing Data

8.1 Chapter Overview

8.2 Random Intercept Regression Models

8.3 Random Coefficient Models

8.4 Multilevel Interaction Effects

8.5 Three-Level Models

8.6 Multiple Imputation

8.7 Joint Model Imputation

8.8 Fully Conditional Specification Imputation

8.9 Maximum Likelihood Estimation

8.10 Summary and Recommended Readings

9. Missing Not at Random Processes

9.1 Chapter Overview

9.2 Missing Not at Random Processes Revisited

9.3 Major Modeling Frameworks

9.4 Selection Models for Multiple Regression

9.5 Model Comparisons and Individual Influence Diagnostics

9.6 Selection Model Analysis Examples

9.7 Pattern Mixture Models for Multiple Regression

9.8 Pattern Mixture Model Analysis Examples

9.9 Longitudinal Data Analyses

9.10 Diggle–Kenward Selection Model

9.11 Shared Parameter (Random Coefficient) Selection Model

9.12 Random Coefficient Pattern Mixture Models

9.13 Longitudinal Data Analysis Examples

9.14 Summary and Recommended Readings

10. Special Topics and Applications

10.1 Chapter Overview

10.2 Descriptive Summaries, Correlations, and Subgroups

10.3 Non-Normal Predictor Variables

10.4 Non-Normal Outcome Variables

10.5 Mediation and Indirect Effects

10.6 Structural Equation Models

10.7 Scale Scores and Missing Questionnaire Items

10.8 Interactions with Scales

10.9 Longitudinal Data Analyses

10.10 Regression with a Count Outcome

10.11 Power Analyses for Growth Models with Missing Data

10.12 Summary and Recommended Readings

11. Wrap-Up

11.1 Chapter Overview

11.2 Choosing a Missing Data-Handling Procedure

11.3 Software Landscape

11.4 Reporting Results from a Missing Data Analysis

11.5 Final Thoughts and Recommended Readings

Appendix. Data Set Descriptions

Author Index

Subject Index

About the Author


About the Author

Craig K. Enders, PhD, is Professor and Area Chair in Quantitative Psychology in the Department of Psychology at the University of California, Los Angeles. His 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. Dr. Enders also conducts research in the areas of multilevel modeling and structural equation modeling, and is an active member of the Society of Multivariate Experimental Psychology, the American Psychological Association, and the American Educational Research Association.

Audience

Researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science.

Course Use

Serves as a text in graduate-level seminars in missing data, or as a supplement or recommended book in courses on advanced quantitative methods, data management, survey analysis, longitudinal data analysis, structural equation modeling, or multivariate analysis.
Previous editions published by Guilford:

First Edition, © 2010
ISBN: 9781606236390
New to this edition: