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NORC at the University Chicago

NORC uses advanced analytical procedures throughout our projects to generate insight from data and provides clients with innovative products to enhance data quality.

NORC conducts research to evaluate different techniques and enhance our statistical modeling, machine learning, and causal inference approaches. For example, we research multilevel modeling to evaluate approaches for analyzing complex relationships. NORC statisticians are at the forefront of research small area modeling techniques, including how best to combine information from different surveys and data sources in estimation and different modeling approaches to create the small area estimates.

Our quantitative social scientists have compared different causal inference approaches for program evaluation to analyze experimental and observational data, such as regression discontinuity designs, modern propensity score matching and weighting methods, advanced difference-in-differences designs, and synthetic control methods. Lastly, our geographers and data scientists research best practices in spatial visualization and cartographical applications to draw geographical insights from data, as well as research on a variety of spatial analytical techniques to obtain fine-grained estimates of economic and social behavior, and to link social survey data with environmental and other types of data.

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Advanced Quantitative Methods Experts

Highlighted Projects

NDWA Labs Methodology Review

Methodological consultation for one of the largest surveys of Spanish-speaking domestic workers in the U.S.

Funder:

NDWA Labs

Understanding Historically Marginalized & Minoritized Communities in the Forgotten Middle

A deep dive into disparities among middle-income older adults’ finances, housing, and health status

Client:

The SCAN Foundation

The Forgotten Middle in 2033

An analysis of middle-income seniors and ability to afford housing and health care

Client:

The SCAN Foundation