Skip to main content
SpringerLink
Log in

Sample Based Methods

  • Chapter
  • First Online:
Subnational Population Estimates

Part of the book series: The Springer Series on Demographic Methods and Population Analysis ((PSDE,volume 31))

  • 1189 Accesses

Abstract

The methods discussed in this chapter are based in concepts discussed in chapters 2 and 4. They also are interconnected and connected to methods discussed earlier. Specifically, we noted in Chapter 8 that the ratio-correlation method can both be informed by sample data (Ericksen, 1973, 1974) and viewed as a form of synthetic estimation (Swanson and Prevost, 1985), a subject we take up in this chapter. Moreover, it is possible to use methods discussed in chapters 7, 8, 9 and 10 with sample data. Conversely, it is the case that some of the methods discussed here, particularly synthetic estimation, do not require sample data for their use. In this regard, the placement of synthetic estimation in this chapter reflects its origins in sample methods and the needs of survey statisticians to leverage the resources they had available (Steinberg, 1979; US NCHS, 1968). As will be seen in this chapter, demographers use a form of synthetic estimation that is not dependent on sample information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Babbie, E. (2009). The Practice of Social Research. Florence, KY: Cengage Learning/Wadsworth Publishing.

    Google Scholar 

  • Berg, E., and W. Fuller. (2009). A SPREE Small Area Procedure for Estimating Population Counts.” Proceedings of the Survey Methods Section, Ottawa, Ontario, Canada: Statistical Society of Canada. (http://www.ssc.ca/survey/documents/SSC2009_EBerg.pdf).

  • Bousfield, M.V. (1977). “Inter-censal Estimation Using a Current Sample and Census Data.” Review of Public Data Use 5: 6–15

    Google Scholar 

  • Bousfield, M. V. (2002). “Population Estimation for Census Tracts using Dynamic Models.” Paper presented at the Annual Meeting of the Population Association of America, Atlanta, GA.

    Google Scholar 

  • Chambers, R. and G. Feeney. (1977). Log linear models for small area estimation. Unpublished paper, Australian Bureau of Statistics

    Google Scholar 

  • Causey, B. (1988). Evaluation of Census Ratio Estimation and Synthetic Estimation. Statistical Research Division Report no. Census/SRD/RR/88/15. (http://www.census.gov/srd/papers/pdf/rr88-15.pdf).

  • Cochran, W. (1977). Sampling Techniques, 3 rd Edition. New York, NY: Wiley.

    Google Scholar 

  • Cohen, M. and X. Zhang, (1988). The Difficulty of Improving Statistical Synthetic Estimation. Statistical Research Division Report no. CENSUS/SRD/RR-88/12. Washington DC: U. S. Bureau of the Census. (http://www.census.gov/srd/papers/pdf/rr88-12.pdf).

  • Cressie, N., and A. Dajani. (1991). “Empirical Bayes Estimation of US Undercount Based on Artificial Populations.” Journal of Official Statistics 7: 57–67.

    Google Scholar 

  • Dillman, D., J. Smyth, L. Christian. (2008). Internet, Mail, and Mixed-Mode Surveys: The Tailored Design Method, 3 rd Edition. New York, NY: Wiley.

    Google Scholar 

  • Ericksen, E. (1974). “A Regression Method for Estimating Population Changes of Local Areas.” Journal of the American Statistical Association 69: 867–875.

    Article  Google Scholar 

  • Ericksen, E. (1973). “A Method for Combining Sample Survey Data and Symptomatic Indicators to obtain Population Estimates for Local Areas.” Demography 10: 137–160.

    Article  Google Scholar 

  • Feeney, G. A. (1987). “The Estimation of the Number of Unemployed at the Small Area Level.” pp. 198 – 218 in R. Platek, J. N. K. Rao, C. E. Särndal, and M. P. Singh (Eds.) Small Area Statistics: An International Symposium. New York, NY: John Wiley and Sons.

    Google Scholar 

  • Ford, B. (1981). The Development of County Estimates in North Carolina. Staff Report AGES 811119. Agriculture Statistical Reporting Service, Research Division. Washington, DC: US Department of Agriculture.

    Google Scholar 

  • Gelman, A., J. Carlin, H. Stern, and D. Rubin. (2004). Bayesian Data Analysis, 2 nd Edition. New York, NY: Chapman and Hall/CRC.

    Google Scholar 

  • Ghosh, M., and J. N. K. Rao. (1994). “Small Area Estimation. An Appraisal.” Statistical Science 9(1): 55–76.

    Article  Google Scholar 

  • Gonzalez, M. and C. Hoza, (1978). “Small Area Estimation with Application to Unemployment and Housing Estimates.” Journal of the American Statistical Association 73 no. 361 (March): 7–15

    Google Scholar 

  • Griffiths, R. (1996). “Current Population Survey Small Area Estimation for Congressional Districts.” 1995 Proceedings of the Statistical Methods Research Section, American Statistical Association: 314–319. Alexandria, VA: American Statistical Association.

    Google Scholar 

  • Groves, R., F. Fowler, Jr., M. Couper, J. Lepkowski, E. Singer, and R. Tourangeau. (2009). Survey Methodology, 2 nd Edition. New York, NY: Wiley.

    Google Scholar 

  • Hansen, M., W. Hurwitz, and W. Madow. (1953). Sample Survey Methods and Theory: Volume I, Methods and Applications. New York, NY: John Wiley and Sons.

    Google Scholar 

  • Iverson, G. (1984). Bayesian Statistical Inference. Quantitative Applications in the Social Sciences, no. 43. Beverly Hill, CA: Sage Publications.

    Google Scholar 

  • Jaffe, A. J. (1951). Handbook of Statistical Methods for Demographers: Selected Problems in the Analysis of Census Data, Preliminary Edition, 2nd Printing (US Bureau of the Census) Washington, DC: US Government Printing Office

    Google Scholar 

  • Judson, D. and C. Popoff, (2004). “Selected General Methods.” pp. 677–732 in J. Siegel and D. A. Swanson (eds.) The Methods and Materials of Demography, 2 nd Edition. Amsterdam, The Netherlands: Elsevier/Academic Press.

    Google Scholar 

  • Kish, L. (1965). Survey Sampling. New York, NY: John Wiley and Sons.

    Google Scholar 

  • Levy, P. (1979). “Small Area Estimation-Synthetic and Other Procedures, 1968-1978.” pp 4–19 in J. Steinberg (Ed.) Synthetic Estimates for Small Areas: Statistical Workshop Papers and Discussion. NIDA Research Monograph 24. Rockville, MD: US Department of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute on Drug Abuse.

    Google Scholar 

  • Lowe, T., and M. Mohrman. (2003). Using a Geographic Information System in a Population Estimate Program: The Pasco Case. Research Brief no. 21, Washington Office of Financial Management. Olympia, WA: Washington Office of Financial Management.

    Google Scholar 

  • Morris, C. (1983). “Parametric Empirical Bayes inference: Theory and applications (with discussions).” Journal of the American Statistical Association. (78): 47–65.

    Google Scholar 

  • Noble A., S. Haslett, and G. Arnold. (2002). “Small Area Estimation via Generalized Linear Models.” Journal of Official Statistics 18(1): 45–60.

    Google Scholar 

  • Pfeffermann, D. (2002). “Small Area Estimation: New Developments and Directions.” International Statistical Review 70(1): 125–143.

    Article  Google Scholar 

  • Platek, R., J. N. K. Rao, C. E. Särndal, and M. P. Singh (Eds.). (1987). Small Area Statistics: An International Symposium. New York, NY: John Wiley and Sons.

    Google Scholar 

  • Purcell, N. J. and L. Kish, L. (1980). “Post-censal Estimates for Local Areas (or Domains).” International Statistical Review. 48: 3–18.

    Google Scholar 

  • Rao, C. R. (1997). Statistics and Truth: Putting Chance to Work, Second Edition. World Scientific Publishing Co. Pte. Ltd. Singapore

    Book  Google Scholar 

  • Rao, J. N. K. (2003). Small Area Estimation. New York, NY: Wiley-Interscience.

    Book  Google Scholar 

  • Salant, P. and D. Dillman, (1994). How to Conduct your own Survey. New York, NY: Wiley.

    Google Scholar 

  • Schaible, W. (1993). “Indirect Estimators, Definition, Characteristics, and Recommendations.” Proceedings of the Survey Research Methods Section, American Statistical Association Vol I: 1-10.. Alexandria, VA: American Statistical Association (http://www.amstat.org/sections/srms/proceedings/y1993f.html).

  • Sinha, A. and R. Sinha. (1999). “Estimating Population Total using Ranked Set Samples.” Paper presented at the Population Estimates Conference, June 8. Suitland, MD: US Census Bureau.

    Google Scholar 

  • Smith, S., J. Tayman, and D. A. Swanson. (2001). State and Local Population Projections: Methodology and Analysis. Dordrecht, The Netherlands: Kluwer/Academic Press (Springer)

    Google Scholar 

  • Steinberg, J. (1979). “Introduction.” pp. 1–2 in J. Steinberg (Ed.) Synthetic Estimates for Small Areas: Statistical Workshop Papers and Discussion. NIDA Research Monograph 24. Rockville, MD: US Department of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute on Drug Abuse.

    Google Scholar 

  • Swanson, D.A. (1980). “Improving Accuracy in Multiple Regression Estimates of County Populations Using Principles from Causal Modeling. Demography 17 (November):413–427.

    Article  Google Scholar 

  • Swanson, D.A. (1981). “Municipal Census Results and Costs for 1981.” Alaska Population Overview 1981. Juneau, AK: Alaska Department of Labor.

    Google Scholar 

  • Swanson, D. A., and L. Pol. (2008). “Applied Demography: Its Business and Public Sector Components.” in Yi Zeng (ed.) The Encyclopedia of Life Support Systems, Demography Volume. UNESCO-EOLSS Publishers. Oxford, England. (http://www.eolss.net/).

  • Swanson, D. A. and R. Prevost. (1985). “A New Technique for Assessing Error in Ratio-Correlation Estimates of Population: A Preliminary Note.” Applied Demography 1 (November): 1–4.

    Google Scholar 

  • Swanson, D. A., B. Baker, and J. Van Patten. (1983). “Municipal Population Estimation: Practical and Conceptual Features of the Housing Unit Method.” Presented at the 1983 Annual Meeting of the Population Association of America, Pittsburgh, PA.

    Google Scholar 

  • Trochim, W. (2006). “Nonprobability Sampling.” Research Methods Knowledge Base (http://www.socialresearchmethods.net/kb/sampnon.php).

  • US Census Bureau. (2009). Design and Methodology: The American Community Survey (ACS DM1). Washington, DC. US Census Bureau.

    Google Scholar 

  • US Census Bureau. (2008). A Compass for Understanding and Using American Community Survey Data: What General Data Users Need to Know. Washington, DC. US Census Bureau.

    Google Scholar 

  • US NCHS (U. S. National Center for Health Statistics). (1968). Synthetic State Estimates of Disability. PHS Publication No. 1759. US Public Health Service. Washington, DC: US Government Printing Office.

    Google Scholar 

  • Voss, P. R., Palit, C. D., Kale, B. D., and Krebs, H. J. (1995). “Censal ratio methods”. pp.70–89 in N. W. Rives, W. J. Serow, A. S. Lee, H. F. Goldsmith, and P. R. Voss (Eds.) Basic methods for preparing small-area estimates. Madison: Applied Population Laboratory, University of Wisconsin.

    Google Scholar 

  • Weisstein, Eric W. (2011). “Estimator Bias.” From MathWorld–A Wolfram Web Resource. (http://mathworld.wolfram.com/EstimatorBias.html).

  • Western, B. (1999). “Bayesian Analysis for Sociologists: An Introduction.” Sociological Methods and Research 28: 7–34.

    Article  Google Scholar 

  • Zhang, L. and R Chambers. (2004). “Small Area Estimates for Cross-classifications. Journal of the Royal Statistical Society, B 66: 479–496.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of California Riverside, Riverside, CA, 92521, USA

    David A. Swanson

  2. Department of Economics, University of California San Diego, Gilman Drive 9500-0508, La Jolla, CA, 92093, USA

    Jeff Tayman

Authors
  1. David A. Swanson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeff Tayman
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Swanson, D.A., Tayman, J. (2012). Sample Based Methods. In: Subnational Population Estimates. The Springer Series on Demographic Methods and Population Analysis, vol 31. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8954-0_11

Download citation

Publish with us

Policies and ethics

Search

Navigation