Applied Missing Data Analysis

Second Edition

Craig K. Enders

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August 25, 2022
ISBN 9781462549863
Price: $75.00
546 Pages
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The new edition will be published August 25, 2022. If you need this title before then, please see the previous edition.
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