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2013 | OriginalPaper | Chapter

13. Basic Steps in Weighting

Authors : Richard Valliant, Jill A. Dever, Frauke Kreuter

Published in: Practical Tools for Designing and Weighting Survey Samples

Publisher: Springer New York

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Abstract

Survey weights are a key component to producing population estimates.

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Footnotes
1
See Sect.​ 4.​1 for a discussion of unbiased and consistent estimates.
 
Literature
go back to reference Breiman L., Friedman J., Stone C., Olshen R. (1993). Classification and Regression Trees. Chapman & Hall, London Breiman L., Friedman J., Stone C., Olshen R. (1993). Classification and Regression Trees. Chapman & Hall, London
go back to reference Cochran W. (1968). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24:295–313MathSciNetCrossRef Cochran W. (1968). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24:295–313MathSciNetCrossRef
go back to reference 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 10:117–131 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 10:117–131
go back to reference 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 17:2265–2281CrossRef 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 17:2265–2281CrossRef
go back to reference Gelman A., Carlin J., Stern H., Rubin D.B. (1995). Data Analysis. Chapman & Hall/CRC., Boca Raton FL Gelman A., Carlin J., Stern H., Rubin D.B. (1995). Data Analysis. Chapman & Hall/CRC., Boca Raton FL
go back to reference 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):234–249CrossRef 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):234–249CrossRef
go back to reference 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 3159–3166 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 3159–3166
go back to reference 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 409–428 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 409–428
go back to reference Kass G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29(2):119–127CrossRef Kass G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29(2):119–127CrossRef
go back to reference Kim J.J., Li J., Valliant R. (2007). Cell collapsing in poststratification. Survey Methodology 33(2):139–150 Kim J.J., Li J., Valliant R. (2007). Cell collapsing in poststratification. Survey Methodology 33(2):139–150
go back to reference Kish L. (1965). Survey Sampling. John Wiley & Sons, Inc., New YorkMATH Kish L. (1965). Survey Sampling. John Wiley & Sons, Inc., New YorkMATH
go back to reference Kreuter F., Olson K. (2011). Multiple auxiliary variables in nonresponse adjustment. Sociological Methods and Research 40:311–332MathSciNetCrossRef Kreuter F., Olson K. (2011). Multiple auxiliary variables in nonresponse adjustment. Sociological Methods and Research 40:311–332MathSciNetCrossRef
go back to reference 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 282–296 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 282–296
go back to reference Little R.J.A., Rubin D.B. (2002). Statistical Analysis with Missing Data. John Wiley & Sons, Inc., New JerseyMATH Little R.J.A., Rubin D.B. (2002). Statistical Analysis with Missing Data. John Wiley & Sons, Inc., New JerseyMATH
go back to reference Little R.J.A., Vartivarian S. (2003). On weighting the rates in non-response weights. Statistics in Medicine 22:1589–1599CrossRef Little R.J.A., Vartivarian S. (2003). On weighting the rates in non-response weights. Statistics in Medicine 22:1589–1599CrossRef
go back to reference Little R.J.A., Vartivarian S. (2005). Does weighting for nonresponse increase the variance of survey means? Survey Methodology 31:161–168 Little R.J.A., Vartivarian S. (2005). Does weighting for nonresponse increase the variance of survey means? Survey Methodology 31:161–168
go back to reference Lohr S.L. (1999). Sampling: Design and Analysis. Duxbury Press, Pacific Grove CAMATH Lohr S.L. (1999). Sampling: Design and Analysis. Duxbury Press, Pacific Grove CAMATH
go back to reference Michie D. (1989). Problems of computer-aided concept formation. In Applications of Expert Systems 2. Turing Institute Press/Addison-Wesley Michie D. (1989). Problems of computer-aided concept formation. In Applications of Expert Systems 2. Turing Institute Press/Addison-Wesley
go back to reference Morgan J.N., Sonquist J.A. (1963). Problems in the analysis of survey data and a proposal. Journal of the American Statistical Association 58:415–434MATHCrossRef Morgan J.N., Sonquist J.A. (1963). Problems in the analysis of survey data and a proposal. Journal of the American Statistical Association 58:415–434MATHCrossRef
go back to reference Rizzo L., Kalton G., Brick J.M. (1996). A comparison of some weighting adjustments for panel nonresponse. Survey Methodology 22:43–53 Rizzo L., Kalton G., Brick J.M. (1996). A comparison of some weighting adjustments for panel nonresponse. Survey Methodology 22:43–53
go back to reference Rosenbaum P., Rubin D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55MathSciNetMATHCrossRef Rosenbaum P., Rubin D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55MathSciNetMATHCrossRef
go back to reference Royall R.M. (1976). Current advances in sampling theory: Implications for human observational studies. American Journal of Epidemiology 104:463–473 Royall R.M. (1976). Current advances in sampling theory: Implications for human observational studies. American Journal of Epidemiology 104:463–473
go back to reference Särndal C., Swensson B., Wretman J. (1992). Model Assisted Survey Sampling. Springer, New YorkMATHCrossRef Särndal C., Swensson B., Wretman J. (1992). Model Assisted Survey Sampling. Springer, New YorkMATHCrossRef
go back to reference Smith T.M.F. (1976). The foundations of survey sampling: A review. Journal of the Royal Statistical Society A 139:183–204CrossRef Smith T.M.F. (1976). The foundations of survey sampling: A review. Journal of the Royal Statistical Society A 139:183–204CrossRef
go back to reference Smith T.M.F. (1984). Present position and potential developments: Some personal views, sample surveys. Journal of the Royal Statistical Society A 147:208–221MATHCrossRef Smith T.M.F. (1984). Present position and potential developments: Some personal views, sample surveys. Journal of the Royal Statistical Society A 147:208–221MATHCrossRef
go back to reference Smith T.M.F. (1994). Sample surveys 1975–1990; an age of reconciliation? International Statistical Review 62:5–34MATHCrossRef Smith T.M.F. (1994). Sample surveys 1975–1990; an age of reconciliation? International Statistical Review 62:5–34MATHCrossRef
go back to reference Stuart E. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science 25(1):1–21MathSciNetCrossRef Stuart E. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science 25(1):1–21MathSciNetCrossRef
go back to reference Valliant R., Dorfman A.H., Royall R.M. (2000). Finite Population Sampling and Inference: A Prediction Approach. John Wiley & Sons, Inc., New YorkMATH Valliant R., Dorfman A.H., Royall R.M. (2000). Finite Population Sampling and Inference: A Prediction Approach. John Wiley & Sons, Inc., New YorkMATH
go back to reference Venables W.N., Ripley B.D. (2002). Modern Applied Statistics with S, 4th edn. Springer, New YorkMATHCrossRef Venables W.N., Ripley B.D. (2002). Modern Applied Statistics with S, 4th edn. Springer, New YorkMATHCrossRef
Metadata
Title
Basic Steps in Weighting
Authors
Richard Valliant
Jill A. Dever
Frauke Kreuter
Copyright Year
2013
Publisher
Springer New York
DOI
https://doi.org/10.1007/978-1-4614-6449-5_13

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