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Activity Number: 282 - New Developments in Small Area Estimation Research at the U.S. Census Bureau
Type: Topic Contributed
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #324400 View Presentation
Title: Multilevel Regression and Poststratification (MRP) for Small Area Estimation: An Application to Estimate Health Insurance Coverage Using Geocoded American Community Survey
Author(s): Xingyou Zhang* and Samuel Szelepka and Blandine Bawawana and Alfred Gottschalck
Companies: U.S. Census Bureau and U.S. Census Bureau and US Census Bureau and U.S. Census Bureau
Keywords: Multilevel Regression and Poststratification (MRP) ; Small area estimation ; Amercian Community Survey (ACS) ; Unit-level Logistic Mixed Model ; SAHIE ; Parametric Bootstrapping
Abstract:

Sociodemographic and health surveys have become routinely geocoded in federal statistical agencies, which means that we could have both individual characteristics of survey respondents from the survey itself but also their geographic context that might have great influence on their individual social, economic and health behaviors. Thus, we are developing and validating an innovative multilevel regression and poststratification (MRP) approach that applies multilevel models to geocoded surveys; takes account for both individual characteristics and area level factors at multiple geographic levels; predicts individual-level social, economic and health outcomes in a multilevel modeling framework; and estimates the geographic distributions of population socioeconomic and health outcomes. We applied this innovative multilevel approach for small area estimation using geocoded American Community Survey (ACS) data. We will demonstrate that MRP provides a flexible statistical linkage and modeling platform that makes full use of geocoded ACS data and available geodemographic data to generate small area estimates of percentages of the population without health insurance coverage.


Authors who are presenting talks have a * after their name.

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