Virtual useR! 2024

Missing data are ubiquitous, pervasive, and often ignored in statistical analyses. Unfortunately, default methods such as complete case analysis may lead to biased and invalid results. This hands-on tutorial aims to equip data analysts with knowledge and skills to validly handle missing data using the popular R package {mice}. {mice} implements multiple imputation by chained equations, a flexible method for imputing (i.e. filling in) missing entries. The session will combine theoretical insights with hands-on exercises. Attendees will first learn the fundamentals of missing data theory, and then gain practical experience in addressing real-world missing data problems through guided demonstrations and exercises. Attendees are encouraged to bring their own incomplete datasets, to implement and evaluate their newfound skills. By the end of the session, attendees will be able to make informed decisions on how to validly handle missing data in their own data analysis projects.