Missing Data and Multiple Imputation (MICE)

What you'll build

• A complete multiple-imputation workflow on a dataset with missing values

What you'll learn

• Why dropping missing rows quietly biases your results
• Mechanisms of missingness: MCAR, MAR, and MNAR
• Multiple imputation with the mice package
• Pooling results correctly across imputations

About Greg

Associate Professor and Director of the Center for Data Science and Consulting at Loyola University Chicago, with research spanning statistical disclosure control, statistical genetics, and statistics in sports.