PD26-15 An Introduction to Missing Data Analyses for Educational Research

Session Type: Professional Development Course

Abstract

This course provides foundational knowledge about missing data analysis. The course content includes missing data assumptions, Markov Chain Monte Carlo (MCMC) estimation, data imputation, incomplete categorical variables, and interaction effects. Participants will learn how to describe key assumptions regarding the reasons for missingness, compare and contract Bayesian and Frequentist statistical paradigms, understand and describe the process for MCMC, and write a script that applies MCMC estimation to a variety of models. The course includes a mixture of lecture and demonstrations. The attendees will be provided with the following materials: lecture slides built around analysis examples from a real educational data set; free statistical analysis software, Blimp, developed by the instructors; a 100+ page white paper that provides details about a range of missing data topics; and a 250+ page annotated software tutorial guide that provides step-by-step instructions for 20 common statistical analyses. The target audience includes graduate students, professors, and research professionals who use, but do not specialize in, quantitative methods. To maximize accessibility, the only prerequisite is a working knowledge of statistical concepts from a typical first-year graduate statistics sequence, in particular multiple regression. The course instructors co-develop the free software application Blimp, available at www.appliedmissingdata.com/blimp.

Sub Unit

• Professional Development and Training Committee