Basic Steps in Weighting

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


Survey weights are a key component to producing population estimates.


Propensity Score Base Weight Terminal Node Response Probability Eligible Respondent 
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.


  1. Breiman L., Friedman J., Stone C., Olshen R. (1993). Classification and Regression Trees. Chapman & Hall, LondonGoogle Scholar
  2. Cochran W. (1968). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24:295–313MathSciNetCrossRefGoogle Scholar
  3. Czajka J., Hirabayashi S., Little R.J.A., Rubin D.B. (1992). Projecting from advance data using propensity modeling: An application to income and tax statistics. Journal of Business and Economic Statistics 10:117–131Google Scholar
  4. D’Agostino R.B. (1998). Propensity score methods for bias reduction for the comparison of a treatment to a non-randomized control group. Statistics in Medicine 17:2265–2281CrossRefGoogle Scholar
  5. Gelman A., Carlin J., Stern H., Rubin D.B. (1995). Data Analysis. Chapman & Hall/CRC., Boca Raton FLGoogle Scholar
  6. Harder V., Stuart E., Anthony J. (2010). Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological Methods 15(3):234–249CrossRefGoogle Scholar
  7. Judkins D., Hao H., Barrett B., Adhikari P. (2005). Modeling and polishing of nonresponse propensity. In: Proceedings of the Survey Research Methods Section, American Statistical Association, pp 3159–3166Google Scholar
  8. Kalton G., Maligalig D.S. (1991). A comparison of methods of weighting adjustment for nonresponse. Proceedings of the US Bureau of the Census Annual Research Conference pp 409–428Google Scholar
  9. Kass G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29(2):119–127CrossRefGoogle Scholar
  10. Kim J.J., Li J., Valliant R. (2007). Cell collapsing in poststratification. Survey Methodology 33(2):139–150Google Scholar
  11. Kish L. (1965). Survey Sampling. John Wiley & Sons, Inc., New YorkzbMATHGoogle Scholar
  12. Kreuter F., Olson K. (2011). Multiple auxiliary variables in nonresponse adjustment. Sociological Methods and Research 40:311–332MathSciNetCrossRefGoogle Scholar
  13. 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
  14. Little R.J.A. (1986). Survey nonresponse adjustments for estimates of means. International Statistical Review 54(2):139–157MathSciNetzbMATHCrossRefGoogle Scholar
  15. Little R.J.A., Rubin D.B. (2002). Statistical Analysis with Missing Data. John Wiley & Sons, Inc., New JerseyzbMATHGoogle Scholar
  16. Little R.J.A., Vartivarian S. (2003). On weighting the rates in non-response weights. Statistics in Medicine 22:1589–1599CrossRefGoogle Scholar
  17. Little R.J.A., Vartivarian S. (2005). Does weighting for nonresponse increase the variance of survey means? Survey Methodology 31:161–168Google Scholar
  18. Lohr S.L. (1999). Sampling: Design and Analysis. Duxbury Press, Pacific Grove CAzbMATHGoogle Scholar
  19. Lumley T. (2012). survey: analysis of complex survey samples. URL
  20. Michie D. (1989). Problems of computer-aided concept formation. In Applications of Expert Systems 2. Turing Institute Press/Addison-WesleyGoogle Scholar
  21. Morgan J.N., Sonquist J.A. (1963). Problems in the analysis of survey data and a proposal. Journal of the American Statistical Association 58:415–434zbMATHCrossRefGoogle Scholar
  22. Rizzo L., Kalton G., Brick J.M. (1996). A comparison of some weighting adjustments for panel nonresponse. Survey Methodology 22:43–53Google Scholar
  23. Rosenbaum P., Rubin D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55MathSciNetzbMATHCrossRefGoogle Scholar
  24. Royall R.M. (1976). Current advances in sampling theory: Implications for human observational studies. American Journal of Epidemiology 104:463–473Google Scholar
  25. Särndal C., Swensson B., Wretman J. (1992). Model Assisted Survey Sampling. Springer, New YorkzbMATHCrossRefGoogle Scholar
  26. Smith T.M.F. (1976). The foundations of survey sampling: A review. Journal of the Royal Statistical Society A 139:183–204CrossRefGoogle Scholar
  27. Smith T.M.F. (1984). Present position and potential developments: Some personal views, sample surveys. Journal of the Royal Statistical Society A 147:208–221zbMATHCrossRefGoogle Scholar
  28. Smith T.M.F. (1994). Sample surveys 1975–1990; an age of reconciliation? International Statistical Review 62:5–34zbMATHCrossRefGoogle Scholar
  29. Stuart E. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science 25(1):1–21MathSciNetCrossRefGoogle Scholar
  30. Therneau T., Atkinson B., Ripley B. (2012). rpart: Recursive Partitioning. URL
  31. Valliant R., Dorfman A.H., Royall R.M. (2000). Finite Population Sampling and Inference: A Prediction Approach. John Wiley & Sons, Inc., New YorkzbMATHGoogle Scholar
  32. Vapnik V.N. (1995) The Nature of Statistical Learning Theory. Springer, New YorkzbMATHCrossRefGoogle Scholar
  33. Venables W.N., Ripley B.D. (2002). Modern Applied Statistics with S, 4th edn. Springer, New YorkzbMATHCrossRefGoogle Scholar
  34. Weisstein E.W. (2010). Extreme Value Distribution. URL, from MathWorld–A Wolfram Web Resource

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