Abstract
If the sampling frame does not have useful auxiliary information to reduce variances, multiphase sampling can be used. This chapter defines this type of sample design and gives real-life examples of multiphase designs. We examine the components needed to develop both base and analysis weights. Methods to determine overall sample size and allocation to phases are given. The chapter concludes with a discussion of software available for sample selection and analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
Sampling rates are typically set to limit the variation in the base weights and to limit the burden placed on the participating schools as measured by the student sample size.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
The cancer treatment centers under this design are treated as the first-stage strata for point and variance estimation because all and not a sample of centers are included in the study. As an aside, mathematical modelers would label this “cancer treatment variable” a fixed effect. If a subset of centers were randomly chosen, then these first-stage clusters (PSUs) usually would be modeled as random effects.
- 11.
- 12.
- 13.
- 14.
References
AAPOR. (2016b). Standard definitions: Final dispositions of case codes and outcome rates for surveys, 9th edn. Tech. rep., The American Association for Public Opinion Research, Deerfield, IL, URL http://www.aapor.org/AAPOR_Main/media/publications/Standard-Definitions20169theditionfinal.pdf
Axinn W. G., Link C. F., Groves R. M. (2011). Responsive survey design, demographic data collection, and models of demographic behavior. Demography 48(3):1127–1149.
Bart J., Earnst S. (2002). Double sampling to estimate density and population trends in birds. The Auk 119(1):36–45.
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):1420–1433.
Bureau of Labor Statistics. (2017). American Time Use Survey User’s Guide, URL http://stats.bls.gov/tus/atususersguide.pdf
Callegaro M., Baker R., Bethlehem J., Göritz A., Krosnick J., Lavrakas P. (eds) (2014). Online Panel Research: A Data Quality Perspective. John Wiley & Sons, Ltd., United Kingdom.
Casella G., Berger R. (2002). Statistical Inference. Duxbury Press, Pacific Grove, CA.
Cochran W. (1977). Sampling Techniques. John Wiley & Sons, Inc., New York.
Dever J. A., Valliant R. (2010). A comparison of variance estimators for poststratification to estimated control totals. Survey Methodology 36:45–56.
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, NJ.
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 Quality.
Fuller W. A. (1998). Replication variance estimation for two-phase samples. Statistica Sinica 8:1153–1164.
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):439–457.
Groves R. M., Peytcheva E. (2008). The impact of nonresponse rates on nonresponse bias. Public Opinion Quarterly 72:167–189.
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 85:856–864.
Hansen M. H., Hurwitz W. N., Madow W. G. (1953a). Sample Survey Methods and Theory, Volume I. John Wiley & Sons, Inc., New York.
Hidiroglou M. A. (2001). Double sampling. Survey Methodology 27(2):143–154.
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):129–140.
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 documention (NCES 2011-328). Tech. rep., National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education, Washington, DC.
Kalton G., Anderson D. (1986). Sampling rare populations. Journal of the Royal Statistical Society A 149:65–82.
Kim J. K., Yu C. L. (2011). Replication variance estimation under two-phase sampling. Survey Methodology 37(1):67–74.
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):312–320.
Korn E. L., Graubard B. I. (1999). Analysis of Health Surveys. John Wiley & Sons, New York.
Kott P. S. (2006). Using calibration weighting to adjust for nonresponse and coverage errors. Survey Methodology 32(2):133–142.
Kott P. S., Stukel D. M. (1997). Can the jackknife be used with a two-phase sample? Survey Methodology 23:81–89.
Kreuter F., Couper M. P., 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 282–296.
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 2024–2028.
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
Little R. J. A., Rubin D. B. (2002). Statistical Analysis with Missing Data. John Wiley & Sons, Inc., New Jersey.
Liu J., Aragon E. (2000). Subsampling strategies in longitudinal surveys. In: Proceedings of the Survey Research Methods Section, American Statistical Association, pp 307–312.
Lohr S. L. (1999). Sampling: Design and Analysis. Duxbury Press, Pacific Grove, CA.
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 4:165–178.
Miller P. (2014). What does adaptive design mean to you? https://www.census.gov/fedcasic/fc2014/ppt/keynote_miller.pdf
Neyman J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association 33(201):101–116.
Peytchev A. (2014). Models and interventions in adaptive and responsive survey designs. DC AAPOR, http://dc-aapor.org/ModelsInterventionsPeytchev.pdf
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).
Rao J. N. K. (1973). On double sampling for stratification and analytical surveys (Corr: V60 p669). Biometrika 60:125–133.
RTI International. (2012). SUDAAN Language Manual, Release 11.0. Research Triangle Park, NC.
Rust K., Graubard B., Fuller W. A., Stokes S. L., Kott P. S. (2006). Finite population correction factors (Panel Discussion). In: Proceedings of the Survey Research Methods Section, American Statistical Association, ResearchGate [Accessed 14-Feb-2018]. https://www.researchgate.net/publication/251618326_Finite_Population_Correction_Factors_Panel_Discussion
Särndal C., Lundström S. (2005). Estimation in Surveys with Nonresponse. John Wiley & Sons, Inc., Chichester.
Särndal C., Swensson B., Wretman J. (1992). Model Assisted Survey Sampling. Springer, New York.
Schouten B., Peytchev A., Wagner J. (2017). Adaptive Survey Design. Chapman and Hall/CRC.
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 4343–4350.
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://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.2189&rep=rep1&type=pdf
Tourangeau R., Brick J. M., Lohr S. L., Li J. (2017). Adaptive and responsive survey designs: A review and assessment. Statistics in Society, Series A 180(1):203–223, URL http://onlinelibrary.wiley.com/doi/10.1111/rssa.12186/full
Valliant R. (1993). Poststratification and conditional variance estimation. Journal of the American Statistical Association 88:89–96.
Valliant R. (2004). The effect of multiple weight adjustments on variance estimation. Journal of Official Statistics 20:1–18.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Valliant, R., Dever, J.A., Kreuter, F. (2018). Multiphase Designs. In: Practical Tools for Designing and Weighting Survey Samples. Statistics for Social and Behavioral Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-93632-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-93632-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93631-4
Online ISBN: 978-3-319-93632-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)