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

Abstract

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

References

Agency for Healthcare Research and Quality. 2016. “MEPS HC-171 2014 Full Year Consolidated Data File.” Available at: https://meps.ahrq.gov/data_stats/download_data/pufs/h171/h171doc.pdf. (accessed March 2017).

Barron, M., M. Davern, R. Montgomery, X. Tao, K.M. Wolter, W. Zeng, C. Dorell, and C. Black. 2015. “Using Auxiliary Sample Frame Information for Optimum Sampling of Rare Populations.” Journal of Official Statistics 31: 545–557. Doi: https://dx-doi-org.web.bisu.edu.cn/10.1515/JOS-2015-0034.

Bureau of Labor Statistics. (2016, June). ATUS User’s Guide (PDF). Available at: http://www.bls.gov/tus/atususersguide.pdf. (accessed March 2017).

Centers for Disease Control and Prevention. (2016, May 23). Federal Register: The Daily Journal of the United States Government. Available at: https://www.federalregister.gov/articles/2016/05/23/2016-12008/proposed-data-collection-submitted-for-publiccomment-and-recommendations. (accessed March 2017).

Chowdhury, S.R. and R.M. Baskin. 2014. “PPS Subsampling from NHIS to MEPS – Effect on Precision of MEPS Estimates.” In Proceedings of the Section on Survey Research Methods: American Statistical Association, August 2014, 2339–2351. Alexandria, VA: American Statistical Association (CD-ROM).

Durrant, G.B., O. Maslovskaya, and P.W.F. Smith. 2014. “Sequence Analysis as a Tool for Investigating Call Record Data.” Working paper, University of Southampton. Available at: https://eprints.soton.ac.uk/369102/ (accessed March 2017).

Durrant, G.B., O. Maslovskaya, and P.W. Smith. 2015. “Modelling Final Outcome and Length of Call to Improve Efficiency in Call Scheduling.” Journal of Survey Statistics and Methodology 3: 397–424. Doi: https://doi.org/10.1093/jssam/smv008.

Eckman, S., J. Sinibaldi, and A. Montmann-Hertz. 2013. “Can Interviewers Effectively Rate the Likelihood of Cases to Cooperate?” Public Opinion Quarterly 77: 561–573. Doi: https://dx-doi-org.web.bisu.edu.cn/10.1093/poq/nft012.

Ezzati-Rice, T.M., F. Rohde, and J. Greenblatt. 2008. Sample Design of the Medical Expenditure Panel Survey Household Component, 1998–2007, Methodology Report No. 22. March 2008. Rockville, MD: Agency for Healthcare Research and Quality. Available at: https://meps.ahrq.gov/data_files/publications/mr22/mr22.pdf (accessed March 2017).

10.

Fricker, S., T. Yan, and S. Tsai. 2014. “Response Burden: What Predicts it and Who is Burdened Out?” In Proceedings of AAPOR Section: American Statistical Association, August 2014. 4568–4577. Alexandria, VA: American Statistical Association. (CD-ROM).

11.

Groves, R.M. and S.G. Heeringa. 2006. “Responsive Design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs.” Journal of the Royal Statistical Society, Series A 169: 439–457. Doi: https://dx-doi-org.web.bisu.edu.cn/10.1111/j.1467-985X.2006.00423.x.

12.

Groves, R.M., M.D. Mosher, J. Lepkowski, and N.G. Kirgis. 2009. Planning and Development of the Continuous National Survey of Family Growth. National Center for Health Statistics. Vital Health Stat, 1(48). Available at: https://www.cdc.gov/nchs/data/series/sr_01/sr01_048.PDF (accessed March 2007).

13.

Hansen, M.H. and W.N. Hurwitz. 1946. “The Problem of Non-Response in Sample Surveys.” Journal of the American Statistical Association 41: 517–529. Doi: https://dx-doi-org.web.bisu.edu.cn/10.1080/01621459.1946.10501894.

14.

Kreuter, F. 2013. Improving Surveys with Paradata: Analytic Uses of Process Information, edited by Frauke Kreuter. Hoboken, NJ: John Wiley & Sons, Inc.

15.

Kish, L. 1965. Survey Sampling. New York: Wiley.

16.

Lohr, S.L. 2009. Sampling: Design and Analysis. Boston: Richard Stratton.

17.

Luiten, A. and B. Schouten. 2013. “Tailored Fieldwork Design to Increase Representative Household Survey Response: an Experiment in the Survey of Consumer Satisfaction.” Journal of the Royal Statistical Society A 176: 169–189. Doi: https://dx-doi-org.web.bisu.edu.cn/10.1111/j.1467-985X.2012.01080.x.

18.

National Center for Health Statistics, National Health Interview Survey, 2014. Public-use data file and documentation. Available at: http://www.cdc.gov/nchs/nhis/quest_data_related_1997_forward.htm (accessed March 2017).

19.

National Research Council, N. 2008. Using the American Community Survey for the National Science Foundation’s Science and Enginering Workforce Statistics Programs. Washington, DC: National Academies Press. Doi: https://doi.org/10.17226/12244.

20.

National Science Foundation. 2016. National Survey of College Graduates. Available at: http://www.nsf.gov/statistics/srvygrads/#tabs-1 (accessed March 2017).

21.

Neyman, J. 1934. “On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection.” Journal of the Royal Statistical Society 97: 558–606. Doi: https://dx-doi-org.web.bisu.edu.cn/10.2307/2342192.

22.

Wagner, J. 2013. “Using Paradata-Driven Models to Improve Contact Rates in Telephone and Face-to-Face Surveys.” In Improving Surveys with Paradata: Analytic Use of Process Information, edited by F. Kreuter, 145–170. New Jersey: John Wiley and Sons.

23.

West, B.T. 2013. “An Examination of the Quality and Utility of Interviewer Observations in the National Survey of Family Growth.” Journal of the Royal Statistical Society A 176: 211–225. Doi: https://dx-doi-org.web.bisu.edu.cn/10.2307/23355184.

24.

West, B.T. and F. Kreuter. 2013. “Factors Affecting the Accuracy of Interviewer Observations Evidence from the National Survey of Family Growth.” Public Opinion Quarterly 77: 522–548. Doi: https://doi.org/10.1093/poq/nft016.