Applied missing data analysis

Bibliographic Information

Applied missing data analysis

Craig K. Enders

(Methodology in the social sciences)

Guilford Press, c2022

2nd ed

  • : hardcover

Available at  / 1 libraries

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Note

Revised edition of the author's Applied missing data analysis, c2010

Includes bibliographical references (p. 493-517) and indexes

Description and Table of Contents

Description

*The most accessible guide to a tough methodological issue, fully updated: more than 85% new material includes 5 new chapters plus new topics. *Includes the latest developments, a retooled organization, and dozens of new datasets. *Translates cutting-edge procedures into language accessible to anyone doing behavioral or social research. *For graduate students and applied researchers; of special interest in the U.S., the Netherlands, Japan, South Korea, and China.

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

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