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Using Linked Survey Paradata to Improve Sampling Strategies in the Medical Expenditure Panel Survey

Lisa B. Mirel
  • Office of Analysis and Epidemiology, National Center for Health Statistics, Centers for Disease Control and Prevention, 3311 Toledo Road, Hyattsville, MD 20782, United States of America
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/ Sadeq R. Chowdhury
  • Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 5600 Fishers Lane, Rockville, MD 20857, United States of America
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Published Online: 2017-06-12 | DOI: https://doi.org/10.1515/jos-2017-0018


Using paradata from a prior survey that is linked to a new survey can help a survey organization develop more effective sampling strategies. One example of this type of linkage or subsampling is between the National Health Interview Survey (NHIS) and the Medical Expenditure Panel Survey (MEPS). MEPS is a nationally representative sample of the U.S. civilian, noninstitutionalized population based on a complex multi-stage sample design. Each year a new sample is drawn as a subsample of households from the prior year’s NHIS. The main objective of this article is to examine how paradata from a prior survey can be used in developing a sampling scheme in a subsequent survey. A framework for optimal allocation of the sample in substrata formed for this purpose is presented and evaluated for the relative effectiveness of alternative substratification schemes. The framework is applied, using real MEPS data, to illustrate how utilizing paradata from the linked survey offers the possibility of making improvements to the sampling scheme for the subsequent survey. The improvements aim to reduce the data collection costs while maintaining or increasing effective responding sample sizes and response rates for a harder to reach population.

Keywords: Sampling; response propensity; paradata; Medical Expenditure Panel Survey; National Health Interview Survey; interviewer observations

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About the article

Received: 2016-01-01

Revised: 2017-03-01

Accepted: 2017-04-01

Published Online: 2017-06-12

Published in Print: 2017-06-01

Citation Information: Journal of Official Statistics, Volume 33, Issue 2, Pages 367–383, ISSN (Online) 2001-7367, DOI: https://doi.org/10.1515/jos-2017-0018.

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© 2017 Lisa B. Mirel et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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