Nonresponse weighting adjustment using estimated response probability
Abstract
To reduce nonresponse bias in sample surveys, a method of nonresponse weighting adjustment is often used which consists of multiplying the sampling weight of the respondent by the inverse of the estimated response probability. The authors examine the asymptotic properties of this estimator. They prove that it is generally more efficient than an estimator which uses the true response probability, provided that the parameters which govern this probability are estimated by maximum likelihood. The authors discuss variance estimation methods that account for the effect of using the estimated response probability; they compare their performances in a small simulation study. They also discuss extensions to the regression estimator.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
It is rarely possible to bring D̃I A_W down to the level of N^{-1} when the weights themselves are subject to errors, typically much larger than O(N^{-1}) for large N.
“But it is rarely possible to do so when the weights themselves are subject to errors, typically much larger than O(N -1 ) for large N [see Kim and Kim (2007), Kim and Riddles (2012)].”
From Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election (2018) · cites this paper as Kim and Kim (2007)
Cites
Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
Robins, J. M., Rotnitzky, A. G., & Zhao, L. P. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476818 · Cited by 2,803 · see citing works
Rosenbaum, P. R. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478441 · Cited by 998 · see citing works
Estimation in Surveys with Nonresponse
Särndal, C., & Lundström, S. (2005) · Wiley eBooks · doi:10.1002/0470011351 · Cited by 499 · see citing works
Survey Design under the Regression Superpopulation Model
Isaki, C. T., & Fuller, W. A. (1982) · Journal of the American Statistical Association · doi:10.1080/01621459.1982.10477770 · Cited by 423 · see citing works
Estimation in Surveys With Nonresponse
Espejo, M. R., & Pineda, M. D. (2006) · Journal of the American Statistical Association · doi:10.1198/jasa.2006.s108 · Cited by 132 · see citing works
Calibration as a Standard Method for Treatment of Nonresponse
Lundström, S. (1997) · Cited by 129 · see citing works
Variance Estimation for Survey Data with Composite Imputation and Nonnegligible Sampling Fractions
Shao, J., & Steel, P. M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473841 · Cited by 102 · see citing works
Does Weighting for Nonresponse Increase the Variance of Survey Means
Little, R. J. A., & Vartivarian, S. (2004) · Collection of Biostatistics Research Archive · Cited by 69 · see citing works
A Note on Handling Nonresponse in Sample Surveys
Kott, P. S. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476795 · Cited by 40 · see citing works
Multivariate and Multipurpose Stratification
Kish, L., & Anderson, D. W. (1978) · Journal of the American Statistical Association · doi:10.1080/01621459.1978.10479993 · Cited by 37 · see citing works
Calibrated Imputation in Surveys Under a Quasi-Model-Assisted Approach
Beaumont, J. (2005) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2005.00511.x · Cited by 33 · see citing works
Does Weighting for Nonresponse Increase the Variance of Survey Means? (Conference Paper)Preprint
Little, R. J. A., & Vartivarian, S. (2005) · RePEc: Research Papers in Economics · Cited by 32 · see citing works
Iannacchione, V. G. (2003) · Journal of Official Statistics · Cited by 20 · see citing works
Properties of the weighting cell estimator under a nonparametric response mechanism
da Silva, D. N., & Opsomer, J. D. (2004) · Americanae (AECID Library) · Cited by 12 · see citing works
Evaluation of Adjustments for Partial Non-Response Bias in the US National Immunization Survey
Smith, P. J., Hoaglin, D. C., Rao, J. N. K., Battaglia, M. P., & Daniels, D. S. (2003) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1046/j.0964-1998.2003.00636.x · Cited by 8 · see citing works
Flagged first, then by citations
1 of the 16 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Item popularity and recommendation accuracy
Steck, H. (2011) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2043932.2043957 · Cited by 333 · see citing works
Meng, X.-L. (2018) · The Annals of Applied Statistics · doi:10.1214/18-aoas1161sf · Cited by 316 · see citing works
Doubly Robust Inference With Nonprobability Survey Samples
Chen, Y., Li, P., & Wu, C. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1677241 · Cited by 156 · see citing works
Construction of Weights in Surveys: A Review
Haziza, D., & Beaumont, J. (2017) · Statistical Science · doi:10.1214/16-sts608 · Cited by 88 · see citing works
Doubly robust inference with missing data in survey sampling
Kim, J. K., & Haziza, D. (2013) · Statistica Sinica · doi:10.5705/ss.2012.005 · Cited by 54 · see citing works
Self-citationExtending the Kitagawa–Oaxaca–Blinder decomposition approach to panel data
Kröger, H., & Hartmann, J. (2021) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x211025800 · Cited by 51 · see citing works
A Discussion of Weighting Procedures for Unit Nonresponse
Haziza, D., & Lesage, É. (2016) · Journal of Official Statistics · doi:10.1515/jos-2016-0006 · Cited by 47 · see citing works
Phipps, P., & Toth, D. (2012) · The Annals of Applied Statistics · doi:10.1214/11-aoas521 · Cited by 45 · see citing works
Inverse probability weighting for clustered nonresponse
Skinner, C. J., & D'arrigo (2011) · Biometrika · doi:10.1093/biomet/asr058 · Cited by 42 · see citing works
Wang, L., Valliant, R. L., & Li, Y. (2021) · Statistics in Medicine · doi:10.1002/sim.9122 · Cited by 38 · see citing works
Järvikoski, A., Martin, M., Autti‐Rämö, I., & Härkäpää, K. (2012) · International Journal of Rehabilitation Research · doi:10.1097/mrr.0b013e32835692d3 · Cited by 31 · see citing works
To Misreport or Not to Report? The Measurement of Household Financial WealthPreprint
Neri, A., & Ranalli, M. G. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2154922 · Cited by 31 · see citing works
Nonparametric and Semiparametric Estimation in Complex Surveys
Breidt, F. J., & Opsomer, J. D. (2009) · Handbook of statistics · doi:10.1016/s0169-7161(09)00227-2 · Cited by 28 · see citing works
Do Life-sustaining Treatment Orders Match Patient and Surrogate Preferences? The Role of POLST
Hickman, S. E., Torke, A. M., Sachs, G. A., Sudore, R. L., Tang, Q., Bakoyannis, G., Smith, N. H., Myers, A. L., & Hammes, B. J. (2020) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06292-1 · Cited by 22 · see citing works
Motivational Orientation of People Participating in Vocational Rehabilitation
Härkäpää, K., Järvikoski, A., & Gould, R. (2014) · Journal of Occupational Rehabilitation · doi:10.1007/s10926-013-9496-z · Cited by 21 · see citing works
Service characteristics as predictors of parents’ perceptions of child rehabilitation outcomes
Järvikoski, A., Härkäpää, K., Martin, M., Vasari, P., & Autti‐Rämö, I. (2013) · Journal of Child Health Care · doi:10.1177/1367493513503579 · Cited by 21 · see citing works
(2012) · Iowa State University Digital Repository (Iowa State University) · Cited by 17 · see citing works
Self-citationKroh, M., Siegers, R., & Kühne, S. (2015) · Springer eBooks · doi:10.1007/978-3-658-10459-7_13 · Cited by 17 · see citing works
Factors associated with concordance between POLST orders and current treatment preferences
Hickman, S. E., Torke, A. M., Sachs, G. A., Sudore, R. L., Tang, Q., Bakoyannis, G., Smith, N. H., Myers, A. L., & Hammes, B. J. (2021) · Journal of the American Geriatrics Society · doi:10.1111/jgs.17095 · Cited by 17 · see citing works
Lawson, N., & Ponkaew, C. (2018) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2018.1465091 · Cited by 13 · see citing works
Propensity score adjustment with several follow-ups
Kim, J. K., & Im, J. (2014) · Biometrika · doi:10.1093/biomet/asu003 · Cited by 12 · see citing works
Self-citationJabkowski, P., & Piekut, A. (2023) · Field Methods · doi:10.1177/1525822x231194178 · Cited by 11 · see citing works
To misreport or not to report?, The case of the Italian survey on household income and wealth
Neri, A., & Ranalli, M. G. (2011) · Statistics in Transition New Series · doi:10.59170/stattrans-2011-018 · Cited by 11 · see citing works
Three Factors to Signal Non-Response Bias With Applications to Categorical Auxiliary Variables
Särndal, C. (2011) · International Statistical Review · doi:10.1111/j.1751-5823.2011.00142.x · Cited by 10 · see citing works
Popularity Agnostic Evaluation of Knowledge Graph Embeddings
Mohamed, A., Parambath, S. P., Kaoudi, Z., & Aboulnaga, A. (2020) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · Cited by 10 · see citing works
An efficient method of estimation for longitudinal surveys with monotone missing data
Zhou, M., & Kim, J. K. (2012) · Biometrika · doi:10.1093/biomet/ass026 · Cited by 10 · see citing works
Self-citationPrimary Care, Health Promotion, and Disease Prevention with Michigan Medicaid Expansion
Goold, S. D., Tipirneni, R., Chang, T., Kirch, M., Bryant, C., Rowe, Z. E., Beathard, E., Solway, E. S., Lee, S., Clark, S. J., et al. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05370-3 · Cited by 10 · see citing works
Comparisons of Some Weighting Methods for Non-response Adjustment
Rota, B. J., & Laitila, T. (2015) · Lithuanian Journal of Statistics · doi:10.15388/ljs.2015.13882 · Cited by 9 · see citing works
Racial Differences in Time at Work Not Working
Darity, W. A. J., Hamilton, D., Myers, S. L., Price, G. N., & Xu, M. K. (2021) · Industrial and Labor Relations Review · doi:10.1177/00197939211063863 · Cited by 9 · see citing works
Client participation in medical rehabilitation for adults with severe disabilities
Järvikoski, A., Puumalainen, J., & Härkäpää, K. (2013) · Scandinavian Journal of Disability Research · doi:10.1080/15017419.2013.859176 · Cited by 8 · see citing works
Dealing with non-ignorable nonresponse in survey sampling: A latent modeling approach
Matei, A., & Ranalli, M. G. (2015) · Survey methodology · Cited by 8 · see citing works
Evaluation and Selection of Models for Attrition Nonresponse Adjustment
SLUD, E. V., & Bailey, L. (2010) · Cited by 7 · see citing works
Linearisation for Variance Estimation by Means of Sampling Indicators: Application to Non‐response
Vallée, A., & Tillé, Y. (2019) · International Statistical Review · doi:10.1111/insr.12313 · Cited by 7 · see citing works
Hickman, S. E., Sudore, R. L., Torke, A. M., Tang, Q., Bakoyannis, G., Smith, N. H., Myers, A. L., & Hammes, B. J. (2023) · Journal of the American Geriatrics Society · doi:10.1111/jgs.18330 · Cited by 4 · see citing works
Verme, P. (2024) · The World Bank Economic Review · doi:10.1093/wber/lhae044 · Cited by 4 · see citing works
Horton, N. J., Toth, D., & Phipps, P. (2014) · The Annals of Applied Statistics · doi:10.1214/14-aoas714 · Cited by 3 · see citing works
A unified theory on empirical likelihood methods for missing data
Chen, S. (2013) · Statistics and Its Interface · doi:10.4310/sii.2013.v6.n3.a3 · Cited by 3 · see citing works
Robust Inference in Two‐phase Sampling Designs with Application to Unit Nonresponse
Favre‐Martinoz, C., Haziza, D., & Beaumont, J. (2016) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12226 · Cited by 3 · see citing works
Tam, S., Holmberg, A., & Wang, S. (2023) · Journal of Official Statistics · doi:10.2478/jos-2023-0020 · Cited by 2 · see citing works
Bias Corrected Maximum Likelihood Estimator Under the Generalized Linear Model for a Binary Variable
Park, M., & Choi, B. (2008) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610910802063772 · Cited by 2 · see citing works
An approximate Bayesian inference on propensity score estimation under unit nonresponse
Sang, H., & Kim, J. K. (2021) · Canadian Journal of Statistics · doi:10.1002/cjs.11586 · Cited by 2 · see citing works
Self-citationEstimating a Finite Population Mean Using Transformed Data in Presence of Random Nonresponse
Bii, N. K., Onyango, C. O., & Odhiambo, J. (2020) · International Journal of Mathematics and Mathematical Sciences · doi:10.1155/2020/7306315 · Cited by 2 · see citing works
Propensity score adjusted method for missing data
Riddles, M. K. (2013) · doi:10.31274/etd-180810-3412 · Cited by 2 · see citing works
Lopiano, K. K., Lamas, A. C., Abreu, D. A., Arroway, P., & Young, L. J. (2011) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.235033 · Cited by 2 · see citing works
xtoaxaca - Extending the Kitagawa-Oaxaca-Blinder Decomposition Approach to Panel DataPreprint
Kröger, H., & Hartmann, J. (2020) · doi:10.31235/osf.io/egj79 · Cited by 2 · see citing works
Estimation of Domain Means from Business Surveys in the Presence of Stratum Jumpers and Nonresponse
Xu, M., Landsman, V., & Graubard, B. I. (2021) · Journal of Official Statistics · doi:10.2478/jos-2021-0045 · Cited by 1 · see citing works
Weightflow: Reproducible, recipe-aware survey weighting for official statistics in R
Ferreira-Neira, J. P. (2026) · Statistical Journal of the IAOS · doi:10.1177/18747655261484262 · Cited by 1 · see citing works
Metodi inferenziali in presenza di mancate risposte parziali
Nicolini, G., Marasini, D., Montanari, G. E., Pratesi, M., Ranalli, M. G., & Rocco, E. (2013) · Unitext · doi:10.1007/978-88-470-2796-1_6 · Cited by 1 · see citing works
Nonresponse Adjusted Raking Ratio Estimation
Park, M. (2015) · Communications for Statistical Applications and Methods · doi:10.5351/csam.2015.22.6.655 · Cited by 1 · see citing works
Targeting Key Survey Variables at the Unit Nonresponse Treatment Stage
Haziza, D., Chen, S., & Gao, Y. (2020) · Journal of Survey Statistics and Methodology · doi:10.1093/jssam/smaa016 · Cited by 1 · see citing works
Jabkowski, P., & Piekut, A. (2022) · Studia Socjologiczne · doi:10.24425/sts.2022.141427 · Cited by 1 · see citing works
Bridging techniques in the redesign of the Italian Survey on Household Income and Wealth
Gambacorta, R., & Porreca, E. (2024) · METRON · doi:10.1007/s40300-024-00272-9 · Cited by 1 · see citing works
Comparison of Approaches for Variance Estimation with Application to a Survey on Discrimination
Guadarrama, M., Bienvenue, J.-Y., & Chauvet, G. (2025) · Journal of Official Statistics · doi:10.1177/0282423x251314363 · Cited by 1 · see citing works
Using Auxiliary Information and Nonparametric Methods in Weighting Adjustments
Rocco, E. (2013) · Studies in theoretical and applied statistics. Selected papers of the statistical societies · doi:10.1007/978-3-642-35588-2_27 · Cited by 1 · see citing works
Rocco, E. (2011) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-642-11363-5_16 · Cited by 1 · see citing works
Variance estimation under monotone non-response for a panel survey
Juillard, H., & Chauvet, G. (2018) · HAL · Cited by 1 · see citing works
Methods for Handling Missing Data
Wu, C., & Thompson, M. E. (2020) · ICSA book series in statistics · doi:10.1007/978-3-030-44246-0_9 · Cited by 1 · see citing works
Liu, Z., & Yau, C. Y. (2021) · Statistical Papers · doi:10.1007/s00362-021-01261-0 · Cited by 1 · see citing works
Dealing with nonresponse in survey sampling: a latent modeling approachPreprint
Matei, A., & Ranalli, M. G. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.4385
Using auxiliary information and non parametric methods in weighting adjustments
Rocco, E. (2010) · Florence Research (University of Florence)
Partially Calibrated Poststratified Weights for Handling Unit Non Response
Park, M. (2015) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2013.813050
Effects of Missing Data On Modeling Enumeration Status In The U.S. Census
Janicki, R., & SLUD, E. V. (2012)
Resampling Variance Estimation for a Two-Phase Sample
Lee, H. J., & Marker, D. A. (2011)
Adjusting for nonignorable nonresponse using a latent variable mod- eling approach
Matei, A., & Giovanna, M. (2011)
STIMATORI DELLA MEDIA DI UNA POPOLAZIONE PER DATI MANCANTI
Diana, G. (2011)
Bayesian Sparse Propensity Score Estimation for Unit NonresponsePreprint
Sang, H., Goh, G., & Kim, J. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.10873
Self-citationNekrašaitė-Liegė, V. (2012) · doi:10.20334/2099-m
Indicators for Monitoring the Survey Data Quality When Non-response or a Convenience Sample Occurs
Rocco, E. (2019) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-030-21158-5_18
(2019) · Théorie des sondages · doi:10.3917/dunod.tille.2019.01.0421
Sampling and Estimation from Finite Populations: Bibliography
Tillé, Y. (2020) · Sampling and Estimation from Finite Populations · doi:10.1002/9781119071259.biblio
(2009) · Handbook of statistics · doi:10.1016/s0169-7161(09)70062-8
Metodi inferenziali in presenza di mancate risposte totali
Nicolini, G., Marasini, D., Montanari, G. E., Pratesi, M., Ranalli, M. G., & Rocco, E. (2013) · Unitext · doi:10.1007/978-88-470-2796-1_5
Quasi-model-assisted estimators under nonresponse in sample surveys
Hasler, C., & Eustache, E. (2026) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2026.106407
Non‐Response Insights With Binary Variables
Brick, J. M., Lohr, S. L., Kennedy, C., & Mercer, A. W. (2025) · International Statistical Review · doi:10.1111/insr.70006
Use of nonresponse weighting and calibration for bias reduction in quota sampling
Lim, Y., & Park, M. (2025) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2025.32.6.741
Correcting Selection Bias in Non-Probability Two-Phase Payment Survey
Chen, H., & Tsang, J. (2025) · Journal of Survey Statistics and Methodology · doi:10.1093/jssam/smaf043
Real‐World Use of Dupilumab in Atopic Dermatitis: Observational Study Results Linked to SNDS
Dupuy, A., Fougerousse, A., Bécherel, P., Droitcourt, C., Kerbrat, S., Mahieu, A., Allali, N., Vataire, A. L., Thénié, C., Helman, N., et al. (2026) · Fundamental and Clinical Pharmacology · doi:10.1111/fcp.70070
- Authors
- Kim, J. K., & Kim, J. J.
- Affiliations
- National Center for Health Statistics (US)
- Corresponding author
- Kim, Jae Kwang
- Kim, Jay J.
- ORCID
- Kim, Jae Kwang
- Venue
- Canadian Journal of Statistics
- Publisher
- Wiley
- Published
- 2007-12
- Volume
- 35
- Issue
- 4
- Pages
- 501-514
- ISSN
- 0319-5724 (ISSN-L)
- 1708-945X
- OpenAlex
- W2075601854
- Cited by
- 80
- References
- 16
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 7 · 2020: 7 · 2019: 5 · 2018: 4 · 2017: 1 · 2016: 2 · 2015: 5 · 2014: 3 · 2013: 8 · 2012: 9
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Canadian Journal of Statistics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Census and Population Estimation
- Keywords
- nonresponse bias, asymptotic properties, estimator efficiency, maximum likelihood estimation, variance estimation, regression estimator, sample surveys
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review