Applied Missing Data
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APPLIED MISSING DATA
ANALYSIS EXAMPLES
Chapter 1: Introduction to Missing Data
Example 1.6 – Identifying Auxiliary Variables
Example 1.10 – Power Analyses for Planned Missingness Designs
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Chapter 2: Maximum Likelihood Estimation
Example 2.10 – Multiple Regression
Example 2.12 – Means, Covariances, and Correlations
Example 2.13 – Probit and Logistic Regression
Custom R programs illustrating FIML coding
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Chapter 3: Maximum Likelihood Estimation with Missing Data
Example 3.2 – Means, Covariances, and Correlations
Example 3.6 – Multiple Regression
Example 3.8 – Moderated Regression with an Interaction
Example 3.9 – Curvilinear Regression
Example 3.10 – FIML with Auxiliary Variables
Example 3.11 – Probit and Logistic Regression
Custom R programs illustrating FIML coding
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Chapter 4: Bayesian Estimation
Example 4.8 – Multiple Regression
Example 4.10 – Means, Covariances, and Correlations
Custom R programs illustrating MCMC coding
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Chapter 5: Bayesian Estimation with Missing Data
Example 5.3 – Multiple Regression
Example 5.4 – Moderated Regression with an Interaction
Example 5.7 – Curvilinear Regression
Example 5.8 – Bayes Estimation with Auxiliary Variables
Example 5.9 – Means, Covariances, and Correlations
Custom R programs illustrating MCMC coding
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Chapter 6: Bayesian Estimation for Categorical Variables
Example 6.3 – Binary Probit Regression
Example 6.4 – Ordinal Probit Regression
Example 6.5 – Regression with Binary Predictors
Example 6.7 – Regression with a Multicategorical Outcome
Example 6.8 – Regression with Multicategorical Predictors
Example 6.9 – Binary Logistic Regression
Custom R programs illustrating MCMC coding
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Chapter 7: Multiple Imputation
Example 7.3 – Joint Model Multiple Imputation
Example 7.4 – Fully Conditional Specification Multiple Imputation
Example 7.11 – Model–Based Imputation for an Interaction Effect
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Chapter 8: Multilevel Missing Data
Example 8.2 – Regression with Random Intercepts
Example 8.3 – Regression with Random Slopes
Example 8.4 – Regression with a Cross-Level Interaction
Example 8.5 – Three–Level Regression with an Interaction
Example 8.7a – Joint Model Multiple Imputation
Example 8.7b – Joint Model Imputation w Random Covariances
Example 8.8 – Fully Conditional Specification Multiple Imputation
Example 8.9 – FIML Regression with Random Intercepts
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Chapter 9: Missing Not at Random Processes
Example 9.6 – Selection Model for Regression
Example 9.8 – Pattern Mixture Models for Regression
Example 9.13a – Diggle–Kenward Selection Growth Model
Example 9.13b – Shared Parameter Growth Model
Example 9.13c – Hedeker–Gibbons Pattern Mixture Growth Model
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Chapter 10: Special Topics and Applications
Example 10.2 – Descriptives, Correlations, and Subgroups
Example 10.3 – Non-Normal Predictor Variables
Example 10.4 – Non-Normal Outcome Variables
Example 10.5 – Mediation and Indirect Effects
Example 10.6 – Structural Equation Models
Example 10.7 – Scale Scores and Missing Questionnaire Items
Example 10.8 – Interactions with Scales
Example 10.9 – Longitudinal Data Analyses
Example 10.10 – Regression with a Count Outcome (and Zero-Inflated Count)
Example 10.11 – Growth Model Power Analyses with Missing Data
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Analysis Examples from the First Edition
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