Events

SoDa Symposium: “Dealing with Missing Data in Education/Workforce Analysis”

Event Start Date: Tuesday, January 30, 2024 - 12:00 pm

Event End Date: Tuesday, January 30, 2024 - 2:00 pm

Location: Virtual / Zoom EST


UMD students, faculty, staff, alumni, and friends—join us for the next SoDa Symposium!

Description:

High quality data and data access are key to creating evidence for policy making; however, data that is 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 who will discuss 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.

Schedule:

12:00–12:10 Overview (Frauke Kreuter)

12:10–12:25 Types of missingness (Tian Lou and Xiangyu Ren)

12:25–12:40 Discussion

12:40–12:55 Modelling missing data (Caro Haensch)

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, UMD

Additional Information:

Please contact ischoolevents@umd.edu at least one week prior to the event to request disability accommodations. In all situations, a good faith effort (up until the time of the event) will be made to provide accommodations.

Speaker(s): Frauke Kreuter (Professor, Joint Program in Survey Methodology; Co-Director of the Social Data Science Center, UMD), Tian Lou, Caro Haensch, Angie Tombari

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