January 30, 2024
The importance of high quality data and data access in creating evidence for policy making. However, data that are systematically missing observations over time or for different groups of people can negatively impact the quality of that evidence. Participants in a recent Executive Certificate class jointly offered by New York University and the University of Maryland expressed a strong interest in building a community of practice around developing practical approaches to address missingness issues in their day to day work with education and workforce data.
This webinar provides a hands-on approach by experts in the field that go into detail of how to address common problems. They will walk participants through a Jupyter Notebook using synthetic data from the state of Kentucky. The notebook will be made available to participants for their reuse after the webinar.
12-12:10 Overview (Frauke Kreuter)
12: 10 -12:25 Modelling missing data (Caro Haensch)
12: 25 – 12:40 Discussion
12:40 – 12:55 Types of missingness (Tian Lou and Xiangyu Ren)
12:55 – 1:00 Break
1:00 -1:15 Discussion
1:15 – 1:30 Real world example (Angie Tombari)
1:30 – 1:45 Discussion
1:45 – 1:55 Close out and additional sources of information (led by Frauke Kreuter)
Moderator:
Frauke Kreuter
Professor, Joint Program in Survey Methodology, Co-Director of the Social Data Science Center, University of Maryland
Professor of Statistics and Data Science, Ludwig-Maximilians-University of Munich
