McCourt School of Public Policy
Massive Data Institute
Massive Data Institute

Data Sampling & Measurement

Understanding the representativeness of different subpopulations is complex when developing nationally representative surveys, but it is even more challenging when building samples from publicly shared user posts. MDI researchers work to understand how to reweight samples to make them more representative of populations of interest. They also develop standards for constructing different types of variables and determining the reliability of different types of measurements. This is particularly important when developing measurements using big data sources, e.g. social media, newspapers, mobile phones, satellite images, etc. Representative samples and reliable measurements are necessary for developing evidence-based public policy. 

Place-based indicators project: This project seeks to advance easy access to high quality location-based indicators that use administrative data, federal statistical data, private data, open data, and everything in between.

S3MC: This project focuses on developing methods for using social media data to better understand public opinion as it relates to politics, the economy, and parenting. It also considers the role misinformation plays in public perceptions.

Mosaic: This project links survey and social media data to investigate ways to improve measurement of public opinion as it relates to vaccines, the economy, and K-12 education.

Faculty

Michael Bailey

Professor
Colonel William J. Walsh Professor of American Government, Department of Government and McCourt School of Public Policy

Shweta Bansal

Professor

Leticia Bode

Professor

Sandeep Dahiya

Crnkovich Family Business of Health Chair

Robin Dillon-Merrill

Professor of Operations and Analytics and Vice Dean of Faculty

Nada Eissa

Associate Professor

Jonathan Ladd

Associate Professor

Day Manoli

Associate Professor

Carole Gresenz

Dean, McCourt School of Public Policy (SLE)

Rebecca Ryan

Professor

Lisa Singh

Professor | Chair, Department of Computer Science