Missing data is a ubiquitous problem in applied statistical research. This workshop, part of the Statistics for Non-Statisticians series, provides a broad overview of tools and methods for addressing missing data in quantitative research. Participants will gain practical skills to identify, evaluate, and implement appropriate strategies for handling missing data in their own work.

What you'll learn:

• How to describe and identify missing data mechanisms
• Potential pitfalls of common imputation techniques
• How to perform and evaluate stochastic imputation methods (MICE and Amelia)
• How to utilize inverse probability weighting (IPW) to construct doubly-robust estimators

Audience: All Yale affiliates conducting or planning to conduct quantitative research

Prerequisites: Participants should come prepared to work through hands-on examples in R. Working knowledge of statistical programming in R and tidyverse are strongly recommended. If you are interested in participating, but would like to refresh your R skills, please contact us at statlab@yale.edu.

Statistics for Non-Statisticians is a workshop series designed to introduce researchers to practical statistical tools and methods for addressing common challenges in applied quantitative research.