Analysis Examples

Chapter 1: Introduction to Missing Data

The examples from Chapter 1 illustrate steps to prepare for a missing data analysis, including identifying correlates of missingness and effective auxiliary variables. The examples also illustrate simulation-based power analyses for planned 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

The examples from Chapter 2 illustrate complete-data maximum likelihood estimation for different regression models and multivariate normal data. The files also include custom R scripts that hand-code FIML estimators for multivariate normal data and a single-mediator model.

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

Chapter 3 illustrates maximum likelihood estimation with incomplete data. The chapter begins with estimation for multivariate normal data and progresses to newer factored regression approaches that disassemble a model into multiple parts that leverage different types of distributions. Factorization paves the way for estimating interactions and nonlinear effects with missing data, and the analysis examples illustrate this approach. The files also include custom R scripts that hand-code FIML estimators for bivariate normal data and a regression model.

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

The examples from Chapter 4 illustrate complete-data Bayesian estimation for linear regression models and multivariate normal data. The files also include custom R scripts that hand-code MCMC estimators for univariate normal data and a regression model.

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

Chapter 5 illustrates Bayesian estimation with incomplete data. The chapter begins with newer factored regression approaches that disassemble a model into multiple parts that leverage different types of distributions. Factorization paves the way for estimating interactions and nonlinear effects with missing data, and the analysis examples illustrate this approach. The chapter concludes with Bayesian missing data handling for multivariate normal data. The files also include custom R scripts that hand-code MCMC estimators for a regression model 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

Factored regression specifications are especially well-suited for variables with mixed response types, and Chapter 6 describes Bayesian estimation and missing data handling for binary, ordinal, and multicategorical nominal variables. The examples illustrate analyses with categorical predictors and outcomes, and the files also include custom R scripts that hand-code MCMC estimators for binary and ordinal probit models.

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

The examples in Chapter 7 describe two predominant and classic multiple imputation frameworks, joint model imputation and fully conditional specification. The chapter also illustrates newer model-based imputation methods based on factored regression specifications, highlighting multipleimputation for a moderated regression analysis with an interaction effect.

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

The emergence of missing data handling methods for multilevel models is an important recent development. The examples in Chapter 8 illustrate Bayesian estimation and multiple imputation for two- and three-level regression models with various real-world complexities. The chapter concludes with FIML estimation for a two-level random intercept model.

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

Chapter 9 illustrates two major modeling frameworks for missing not at random processes, selection models and pattern mixture models. Both approaches introduce a model that describes the occurrence of missing data, albeit in different ways. The analysis examples apply these frameworks to regression models and single-level and multilevel longitudinal growth models.

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

The data analysis examples in Chapter 10 illustrate specialized topics, advanced applications, and practical issues. Examples in this section highlight use cases that differentiate FIML, Bayesian estimation, and multiple imputation.

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
Example 10.11 – Growth Model Power Analyses with Missing Data

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Analysis Examples from the First Edition

Download an archive of all data sets and analysis scripts from the first edition of Applied Missing Data Analysis.

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