Missing Data and Multiple Imputation (Online)

In this talk, Chuck Huber will introduce different kinds of mechanisms that lead to missing data such as missing at random (MAR), missing completely at random (MCAR), and missing not at random (MNAR). He then will demonstrate how to use Stata’s -mi- commands to impute missing data and fit models using the imputed datasets.

Guest Speaker: Chuck Huber

Chuck Huber is Director of Statistical Outreach at StataCorp LLC and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health and at the New York University School of Global Public Health. In addition to working with Stata’s team of software developers, he produces instructional videos for the Stata YouTube channel, writes blog entries, develops online NetCourses, and gives talks about Stata at conferences and universities. Most of his current work is focused on statistical methods used by behavioral and health scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition, and birth defects. Dr. Huber currently teaches survey sampling at NYU and introductory biostatistics at Texas A&M, where he previously taught categorical data analysis, survey data analysis, and statistical genetics.

Registration fees:

• There are no fees associated for this guest talk, but you must register to secure your spot and receive the Zoom link.

For questions regarding this class, please contact Jill Stevens at jill_stevens@unc.edu