A Discussion of Weighting Procedures for Unit Nonresponse

Open access


Weighting procedures are commonly applied in surveys to compensate for nonsampling errors such as nonresponse errors and coverage errors. Two types of weight-adjustment procedures are commonly used in the context of unit nonresponse: (i) nonresponse propensity weighting followed by calibration, also known as the two-step approach and (ii) nonresponse calibration weighting, also known as the one-step approach. In this article, we discuss both approaches and warn against the potential pitfalls of the one-step procedure. Results from a simulation study, evaluating the properties of several point estimators, are presented.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Brick M.J. 2013. “Unit Nonresponse and Weighting Adjustments: A Critical Review.” Journal of Official Statistics 29: 329-353. Doi: http://dx.doi.org/10.2478/jos-2013-0026.

  • Da Silva D.N. and J.D. Opsomer. 2006. “A Kernel Smoothing Method of Adjusting for Unit Non-Response in Sample Surveys.” The Canadian Journal of Statistics 34: 563-579.

  • Da Silva D.N. and J.D. Opsomer. 2009. “Nonparametric Propensity Weighting for Survey Nonresponse through Local Polynomial Regression.” Survey Methodology 35: 165-176.

  • Deville J.-C. and C.-E. Särndal. 1992. “Calibration Estimators in Survey Sampling.” Journal of the American Statistical Association 87: 376-382. Doi: http://dx.doi.org/10.1080/01621459.1992.10475217.

  • Deville J.-C. C.-E. Särndal and O. Sautory. 1993. “Generalized Raking Procedures in Survey Sampling.” Journal of the American Statistical Association 88: 1013-1020. Doi: http://dx.doi.org/10.1080/01621459.1993.10476369.

  • Ekholm A. and S. Laaksonen. 1991. “Weighting via Response Modeling in the Finnish Household Budget Survey.” Journal of Official Statistics 7: 325-337.

  • Eltinge J.L. and I.S. Yansaneh. 1997. “Diagnostics for Formation of Nonresponse Adjustment Cells with an Application to Income Nonresponse in the U.S. Consumer Expenditure Survey.” Survey Methodology 23: 33-40.

  • Giommi A. 1987. “Nonparametric Methods for Estimating Individual Response Probabilities.” Survey Methodology 13: 127-134.

  • Haziza D. and J.-F. Beaumont. 2007. “On the Construction of Imputation Classes in Surveys.” International Statistical Review 75: 25-43. Doi: http://dx.doi.org/10.1111/j.1751-5823.2006.00002.x.

  • Kalton G. and I. Flores-Cervantes. 2003. “Weighting Methods.” Journal of Official Statistics 19: 81-97.

  • Kim J.K. and J.J. Kim. 2007. “Nonresponse Weighting Adjustment Using Estimated Response Probability.” The Canadian Journal of Statistics 35: 501-514. Doi: http://dx.doi.org/10.1002/cjs.5550350403.

  • Kott P. 2006. “Using Calibration Weighting to Adjust for Nonresponse and Undercoverage.” Survey Methodology 32: 133-142.

  • Kott P.S. and D. Liao. 2012. “Providing Double Protection for Unit Nonresponse With a Nonlinear Calibration-Weighting Routine.” Survey Research Methods 6: 105-111.

  • Lee S. 2006. “Propensity Score Adjustments as a Weighting Scheme for Volunteer Panel Web Surveys.” Journal of Official Statistics 22: 329-349.

  • Little R.J.A. 1986. “Survey Nonresponse Adjustments for Estimates of Means.” International Statistical Review 54: 139-157.

  • Little R.J.A. and S. Vartivarian. 2005. “Does Weighting for Nonresponse Increase the Variance of Survey Means?” Survey Methodology 31: 161-168.

  • Phipps P. and D. Toth. 2012. “Analyzing Establishment Nonresponse Using an Interpretable Regression Tree Model With Linked Administrative Data.” Annals of Applied Statistics 6: 772-794. Doi: http://dx.doi.org/10.1214/11-AOAS521.

  • Rosenbaum P.R. and D.B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70: 41-55. Doi: http://dx.doi.org/10.1093/biomet/70.1.41.

  • Rubin D.B. 1976. “Inference and Missing Data.” Biometrika 63: 581-590. Doi: http://dx.doi.org/10.1093/biomet/63.3.581.

  • Särndal C.-E. 2011. “Three Factors to Signal Non-Response Bias With Applications to Categorical Auxiliary Variables.” International Statistical Review 79: 233-254. Doi: http://dx.doi.org/10.1111/j.1751-5823.2011.00142.x.

  • Särndal C.-E. and S. Lundström. 2005. Estimation in Surveys with Nonresponse. New York: John Wiley and Sons.

Journal information
Impact Factor

IMPACT FACTOR 2018: 0.837
5-year IMPACT FACTOR: 0.934

CiteScore 2018: 1.04

SCImago Journal Rank (SJR) 2018: 0.963
Source Normalized Impact per Paper (SNIP) 2018: 1.020

Cited By
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 727 452 108
PDF Downloads 309 176 5