Monday, April 22 at 11:00am to 12:00pm
Seeley G. Mudd Building (SGM), 460
Los Angeles, CA 90089
Craig Enders, Ph.D.
Professor of Psychology, Quantitative Program
Author, Applied Missing Data Analysis (2010)
A Model-Based Imputation Procedure for Regression Models with Interactions, Random Coefficients, and Non-Linear Terms
Despite the broad appeal of missing data handling approaches that assume a missing at random (MAR) mechanism (e.g., multiple imputation and maximum likelihood estimation), some very common analysis models in the behavioral science literature are known to cause bias-inducing problems for these approaches. Regression models with
incomplete interactive or polynomial effects are a particularly important example because they are among the most common analyses in behavioral science research applications. In the context of single-level regression, fully Bayesian (model- based) imputation approaches have shown great promise with these popular analysis models. This talk will introduce attendees to model-based imputation for moderated regression analyses, including extensions of the procedure to multilevel models with random coefficients and non-linear terms.