Advertisement

Multiphase Designs

  • Richard Valliant
  • Jill A. Dever
  • Frauke Kreuter
Chapter
Part of the Statistics for Social and Behavioral Sciences book series (SSBS, volume 51)

Abstract

Sample designs are developed and estimators are chosen to efficiently fulfill specified analysis plans. Efficiency is generally defined to encompass three primary areas—accurate estimates (bias) with high levels of precision (small standard errors) calculated from data collected with procedures that make economical use of the study funds without exceeding the specified budget (cost). Sections 3.1 and 3.2 and Chap. 15 detail the gains achieved in precision if auxiliary information that is highly associated with the analysis variables can be used. This includes, for example, auxiliary variables used (i) in sampling as a stratification variable or to construct the measure of size for a probability proportional to size (pps) design or (ii) in estimation with a regression (or ratio) estimator. However, what if the only available sampling frame does not have useful auxiliary information? Without the auxiliary information, how might the statistician address concerns that the inflated sample size required for the specified level of precision will exceed the study budget?

Keywords

Nonresponse Bias Auxiliary Information European Social Survey Sample Member Double Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. AAPOR (2011). Standard definitions: Final dispositions of case codes and outcome rates for surveys, 7th edn. Tech. rep., The American Association for Public Opinion Research, Deerfield, IL, URL http://www.aapor.org/pdfs/standarddefs∖_4.pdf
  2. Axinn W.G., Link C.F., Groves R.M. (2011). Responsive survey design, demographic data collection, and models of demographic behavior. Demography 48(3):1127–1149CrossRefGoogle Scholar
  3. Bart J., Earnst S. (2002). Double sampling to estimate density and population trends in birds. The AuK 119(1):36–45Google Scholar
  4. Breivik H., Cherny N., Collett B., de Conno F., Filbet M., Foubert A.J., Cohen R., Dow L. (2009). Cancer-related pain: a pan-European survey of prevalence, treatment, and patient attitudes. Annals of Oncology 20(8):1420–1433CrossRefGoogle Scholar
  5. Bureau of Labor Statistics (2012). American Time Use Survey User’s Guide. URL http://stats.bls.gov/tus/atususersguide.pdf
  6. Casella G., Berger R. (2002). Statistical Inference. Duxbury Press, Pacific Grove CAGoogle Scholar
  7. Cochran W. (1977). Sampling Techniques. John Wiley & Sons, Inc., New YorkMATHGoogle Scholar
  8. Dever J.A., Valliant R. (2010). A comparison of variance estimators for poststratification to estimated control totals. Survey Methodology 36:45–56Google Scholar
  9. Dillman D.A., Smyth J.D., Christian L.M. (2009). Internet, Mail, and Mixed-Mode Surveys: The Tailored Design Method. John Wiley & Sons, Inc., Hoboken, NJGoogle Scholar
  10. Ezzati-Rice T., Rohde F., Greenblatt J. (2008). Sample design of the medical expenditure panel survey household component, 1998–2007. Tech. Rep. Methodology Report No. 22, Agency for Healthcare Research and QualityGoogle Scholar
  11. Fuller W.A. (1998). Replication variance estimation for two-phase samples. Statistica Sinica 8:1153–1164MATHGoogle Scholar
  12. Groves R.M., Heeringa S.G. (2006). Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society, Series A: Statistics in Society 169(3):439–457MathSciNetCrossRefGoogle Scholar
  13. Hansen M.H., Tepping B.J. (1990). Regression estimates in federal welfare quality control programs (C/R: P864-873). Journal of the American Statistical Association 85:856–864CrossRefGoogle Scholar
  14. Hansen M.H., Hurwitz W.H., Madow W.G. (1953a). Sample Survey Methods and Theory, Volume I. John Wiley & Sons, Inc., New YorkMATHGoogle Scholar
  15. Iannacchione V.G., Dever J.A., Bann C.M., Considine K.A., Creel D., Carson C.P., Best H.L., Haley R.W. (2011). Validation of a research case definition of Gulf War illness in the 1991 U.S. military population. Neuroepidemiology 37(2):129–140Google Scholar
  16. Ingels S.J., Pratt D.J., Herget D., Dever J.A., Ottem R., Rogers J., Jin Y., Leinwand S. (2011). High School Longitudinal Study of 2009 (HSLS:09) base-year data file documentation (NCES 2011-328). Tech. rep., National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education, Washington DCGoogle Scholar
  17. Kalton G., Anderson D. (1986). Sampling rare populations. Journal of the Royal Statistical Society A 149:65–82CrossRefGoogle Scholar
  18. Kim J.K., Yu C.L. (2011). Replication variance estimation under two-phase sampling. Survey Methodology 37(1):67–74Google Scholar
  19. Kim J.K., Navarro A., Fuller W.A. (2006). Replication variance estimation for two-phase stratified sampling. Journal of the American Statistical Association 101(473):312–320MathSciNetMATHCrossRefGoogle Scholar
  20. Korn E.L., Graubard B.I. (1999). Analysis of Health Surveys. John Wiley & Sons, New YorkMATHCrossRefGoogle Scholar
  21. Kott P.S. (2006). Using calibration weighting to adjust for nonresponse and coverage errors. Survey Methodology 32(2):133–142Google Scholar
  22. Kott P.S., Stukel D.M. (1997). Can the jackknife be used with a two-phase sample? Survey Methodology 23:81–89Google Scholar
  23. Kreuter F., Couper M., Lyberg L. (2010). The use of paradata to monitor and manage survey data collection. In: Proceedings of the Survey Research Methods Section, American Statistical Association, pp 282–296Google Scholar
  24. Lee H., Kim J.K. (2002). Jackknife variance estimation for two-phase samples with high sampling fractions. In: Proceedings of the Survey Research Methods Section, American Statistical Association, pp 2024–2028Google Scholar
  25. Lepkowski J., Axinn W.G., Kirgis N., West B.T., Ndiaye S.K., Mosher W., Groves R.M. (2010). Use of paradata in a responsive design framework to manage a field data collection. NSFG Survey Methodology Working Papers (10–012), URL http://www.psc.isr.umich.edu/pubs/pdf/ng10-012.pdf
  26. Little R.J.A., Rubin D.B. (2002). Statistical Analysis with Missing Data. John Wiley & Sons, Inc., New JerseyMATHGoogle Scholar
  27. Liu J., Aragon E. (2000). Subsampling strategies in longitudinal surveys. In: Proceedings of the Survey Research Methods Section, American Statistical Association, pp 307–312Google Scholar
  28. Lohr S.L. (1999). Sampling: Design and Analysis. Duxbury Press, Pacific Grove CAMATHGoogle Scholar
  29. Matsuo H., Billiet J., Loosveldt G., Berglund F., Kleven Ø. (2010). Measurement and adjustment of non-response bias based on non-response surveys: The case of Belgium and Norway in the European Social Survey round 3. Survey Research Methods 4:165–178Google Scholar
  30. Neyman J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association 33(201):101–116MATHCrossRefGoogle Scholar
  31. Potter F.J., Iannacchione V.G., Mosher W., Mason R., Kavee J.A. (1998). Sample design, sampling weights, imputation, and variance estimation in the 1995 National Survey of Family Growth. Vital and Health Statistics, National Center for Health Statistics 124(2)Google Scholar
  32. Rao J.N.K. (1973). On double sampling for stratification and analytical surveys (Corr: V60 p669). Biometrika 60:125–133MathSciNetMATHCrossRefGoogle Scholar
  33. Särndal C., Lundström S. (2005). Estimation in Surveys with Nonresponse. John Wiley & Sons, Inc., EnglandMATHCrossRefGoogle Scholar
  34. Särndal C., Swensson B., Wretman J. (1992). Model Assisted Survey Sampling. Springer, New YorkMATHCrossRefGoogle Scholar
  35. Singh A.C., Dever J.A., Iannacchione V.G. (2004). Composite response rates for surveys with nonresponse follow-up. In: Proceedings of the Survey Research Methods Section, American Statistical Association, pp 4343–4350Google Scholar
  36. Singh A.C., Dever J.A., Iannacchione V.G., Chen S. (2005). Efficient estimation of response rates when a small subsample of nonrespondents is selected for follow-up conversion. In: Federal Committee on Statistical Methodology (FCSM) Conference, Arlington, VA, URL http://www.fcsm.gov/05papers/Singh_Iannacchione_etal_VIIB.pdf
  37. Valliant R. (1993). Poststratification and conditional variance estimation. Journal of the American Statistical Association 88:89–96MathSciNetMATHGoogle Scholar
  38. Valliant R. (2004). The effect of multiple weight adjustments on variance estimation. Journal of Official Statistics 20:1–18Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Richard Valliant
    • 1
  • Jill A. Dever
    • 2
  • Frauke Kreuter
    • 3
  1. 1.University of MichiganAnn ArborUSA
  2. 2.RTI InternationalWashington, DCUSA
  3. 3.University of MarylandCollege ParkUSA

Personalised recommendations