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