Katie Wilson

Session: Short Course 1
Title: Introduction to Missing Data Methods for Observational Data
Abstract: Missing data are common in many disciplines including the social sciences, and therefore understanding the impact of missing data on estimation and inference as well as the strengths and weaknesses of approaches to handling missing data is crucial. This course aims to provide participants with the knowledge and tools to assess and mitigate the impact of missing data in their research. We will begin with an overview of common missing data mechanisms and traditional methods to handling missing data before discussing more contemporary approaches including multiple imputation. Methods will be illustrated using R. This short course is targeted towards individuals with little or no prior experience with modern missing data methods. Prior introductory applied statistical coursework (SOC 504-505-506 or equivalent) and experience using regression methods to analyze data (e.g., linear regression, logistic regression) is important background for this course.

Katie Wilson is an Assistant Teaching Professor in the Department of Biostatistics at the University of Washington. Katie Wilson's website.