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NORC at the University Chicago
NORC’s scientific approach to administrative and big data sources enhances data value.

NORC has been at the forefront of using administrative and big data in our research. We have experience with a wide range of administrative data sources ranging from government records (e.g., tax, crime, property, government assistance, etc.) and commercial data sources (e.g., voter lists, school records, financial transactions) to health care claims and physician records. Our researchers also have experience with a variety of big data sources, including social media and search, mobile device data, satellite imagery and GPS, and environmental or other types of sensor data. 

Our research has focused on the strengths and limitations of these data sources, including the value of understanding how the data are created to help assess their usefulness. Our methodologists have developed approaches to evaluate the relevance, coverage, and availability of different data sources to determine which sources are best for a particular research project. Our researchers have worked extensively with health care claims and physicians’ records and understand the nuances of bringing together different data sources to provide coverage of different populations (e.g., Medicare, Medicaid, and private insurance), the cost of securing data from various sources, and have developed techniques to overcome the challenges with storing, transforming, and analyzing the data. We routinely process commercial data sources that are often combined with survey or other administrative and big data sources for research.

We also have conducted research on approaches and techniques for ingesting, cleaning, transforming, and linking data from different sources. For example, our statisticians have presented and published research on approaches for linking records across data sources, including probabilistic vs. deterministic methods, linkage routine optimization for accuracy and speed, and methods to estimate linkage error. NORC statisticians, methodologists, and data scientists have compared different approaches for harmonizing data across sources including evaluating measurement differences across sources and deciding how to best represent the measure in the final combined data.

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Using & Enhancing Administrative & Big Data Experts

Highlighted Projects

Inflation Reduction Act’s Impact on Prescription Drug Spending

An estimate of savings for Medicare beneficiaries taking cancer drugs

Client:

Patients for Affordable Drugs

Qatar National Education Data System

Creating a system for measuring the impact of nationwide education reforms

Client:

Government of Qatar

Access to Justice Design & Testing Program

A new data collection effort measuring and reporting access to justice for civil legal needs

Client:

Bureau of Justice Statistics

Expanding Hearing Loss Education & Awareness

The nation’s first ever state- and county-level hearing loss prevalence estimates

Client:

Centers for Disease Control and Prevention

Value of Hospice

The most statistically grounded comparative assessment of hospice spending to date

Client:

National Association for Home Care & Hospice and National Hospice and Palliative Care Organization

Dementia DataHub: Visualizing Diagnosed Dementia in Medicare

The nation’s first data system reporting cases of people with Alzheimer’s disease and related dementias

Client:

National Institute on Aging

Healthcare Cost and Utilization Project (HCUP)

The nation’s most comprehensive source of hospital care data

Client:

Agency for Healthcare Research and Quality

USDA Economic Research Service Secure Data Enclave

Providing secure hosting, management, and access to restricted-use data about the U.S. food supply

Client:

Economic Research Service of the U.S. Department of Agriculture (USDA)

Data Concierge Models for a National Secure Data Service

Developing novel models and tools to assist federal data users

Client:

National Center for Science and Engineering Statistics

Data Usage Platform as a Federal Data Asset

User experience research and prototyping for a federal data usage platform

Client:

National Center for Science and Engineering Statistics

Artificial Intelligence for Enhancing Data Quality, Standardization & Integration

Applying innovative data science methods to create datasets that support evidence-based decision-making

Client:

National Center for Science and Engineering Statistics

Linking Parent & Statistical Agency Data

Linking NCSES SED and NSF PI data to inform future linkages between a statistical agency and its parent agency

Client:

National Center for Science and Engineering Statistics