Welcome to the companion website for Applied Missing Data Analysis.  In addition to the data sets and analysis examples from the book, the website houses a variety of additional analysis examples, training materials and papers, and custom macro programs.  Check back frequently, as I test and update the analysis scripts as software updates become available.

Click here for the full table of contents, and visit Guilford Publications for additional information about or to purchase Applied Missing Data Analysis.

NEW! Blimp 2.1 is now available with exciting new features.

  • Bayesian estimation of single-level, multilevel (up to three levels), and multiple group regression models with complete or incomplete data
  • Posterior summaries of all model parameters from Bayesian estimation (posterior mean, median, standard deviation, and credible interval)
  • Fully conditional specification multiple imputation for single-level, multilevel (up to three levels), and multiple group regression models
  • Missing data handling for normal, binary, ordinal, or nominal variables
  • Automatic dummy coding for nominal variables
  • New simplified scripting language and redesigned output
  • New graphical interface with automatic updates when new features become available
  • New graphical engine that creates trace plots for all model parameters
  • Rights and Sterba variance explained effect sizes for multilevel models
  • Bayesian estimation for interactive and polynomial effects with complete or missing data
  • Bayesian estimation with grand mean centering (all models) and group mean centering (two- and three-level models)
  • Post-hoc probing of interaction effects with continuous or categorical moderators
  • Bayesian estimation of conditional effects (simple intercepts and slopes) in regression models with interaction effects
  • Discrete and latent imputations for binary, ordinal, and nominal variables
  • Fully conditional specification or Bayesian estimation with level-2 and level-3 cluster means modeled as latent variables
  • Contextual effects models with latent group means or manifest group means
  • Interaction effects with latent group means
  • Bayesian estimates of random intercepts and slopes for multilevel modeling diagnostics
  • Various algorithmic and interface enhancements (e.g., random starting values, options for saving various estimates and output)
Questions or suggestions? Email Craig Enders