Accessing Restricted Data at the University of Maryland Federal Statistical Research Data Center
When |
Sep 21, 2018
from 12:00 PM to 01:30 PM |
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Where | 1101 Morrill Hall |
Contact Name | Jennifer Doiron |
Contact Phone | 301-405-6403 |
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About the Presentation
The University of Maryland Federal Statistical Research Data Center (RDC) is a new and valuable campus data resource for accessing and using restricted-use datasets from a variety of federal agencies. In this workshop, we will discuss how UMD researchers can use the RDC and leverage existing resources for work with restricted data. We will cover (a) the process of developing a proposal and applying for access; (b) the universe of relevant data that can be accessed in the RDC (including data from NCHS, Census, AHRQ, BLS); (c) applying for and receiving Special Sworn Status; and (d) examples of potential research projects using RDC data. Researchers with interests in using the RDC as well as those with active RDC projects are encouraged to attend.
About the Speakers
Andrew Fenelon: Dr. Andrew Fenelon is an Assistant Professor in the Department of Health Services Administration in the School of Public Health at University of Maryland, College Park. His main research interests focus on health disparities, population health, health policy, and methods.
Michel Boudreaux: Michel is an Assistant Professor in the Department of Health Services Administration in the School of Public Health, University of Maryland. Dr. Boudreaux conducts research in interrelated areas of health policy. He has extensive experience using Census and NCHS data products in the RDC system.
Dr. Quynh Nguyen: Quynh is an Assistant Professor, Epidemiology and Biostatistics, Her main interest is as a social epidemiologist focusing on contextual and economic factors as they relate to health. She has extensive experience using numerous national and international population-based health surveys to examine social and economic predictors of health, and to quantify national and international patterns in health disparities.