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Christopher Ward

NORC at the University of Chicago



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Felicia LeClere

NORC at the University of Chicago



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Kari Carris

NORC at the University of Chicago



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Stephen Cohen

National Science Foundation



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Dean M. Resnick

National Opinion Research Center at the University of Chicago



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Sjoblom Micah

NORC



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Jennifer Vanicek

Survey Director, NORC at the University of Chicago, Chicago, IL



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Ying Li

NORC at the University of Chicago



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76 – Paradata for Adaptive Survey Designs and Other Applications

Implementation of Adaptive Design on the Medicare Current Beneficiary Survey

Sponsor: Government Statistics Section
Keywords: Medicare, r-indicators, bootstrapping, variance estimation, paradata, responsive design

Christopher Ward

NORC at the University of Chicago

Felicia LeClere

NORC at the University of Chicago

Kari Carris

NORC at the University of Chicago

Stephen Cohen

National Science Foundation

Dean M. Resnick

National Opinion Research Center at the University of Chicago

Sjoblom Micah

NORC

Jennifer Vanicek

Survey Director, NORC at the University of Chicago, Chicago, IL

Ying Li

NORC at the University of Chicago

In light of the steady decline in response rates across a range of surveys, interest has grown among survey researchers to establish methods of measuring representativeness of the collected sample. On the Medicare Current Beneficiary Survey (MCBS), we have implemented an adaptive survey design to ensure that the survey respondents are representative of the sample population. The MCBS is a continuous, multipurpose survey of a nationally representative sample of the Medicare population, conducted by the Centers for Medicare & Medicaid Services (CMS) through a contract with NORC. In this presentation, we review key measures used in adaptive design and describe our findings based on data collected during Fall 2017. We also compare this period to the same period in 2016, provide displays that help analysts interpret R-indicators, and discuss interventions proposed to respond to these measures. We discuss the use of R-indicators that measure both the representativeness to the sample population as a whole and also R-indicators for specific variables that are important. We propose practical data collection interventions as a means of implementing real-time adaptive design.

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