My methodological research program has focused on developing, evaluating, and applying modern statistical methods for addressing missing data, principally multiple imputation and Bayesian estimation. My goal has been to identify the domains in which these procedures produce adequate results and to develop new methods in domains in which they are not adequate. With support from Institute of Educational Sciences awards, the bulk of my recent methodological work is devoted to the development of a statistical software application called Blimp. Blimp is an all-purpose data analysis and latent variable modeling program that harnesses the flexible power of Markov chain Monte Carlo (MCMC) estimation. The development team’s goal is to democratize state-of-the art missing data methods by integrating them into a free, user-friendly application that requires minimal scripting and no deep-level knowledge about Bayesian statistics.