An Appraisal of Common Reweighting Methods for Nonresponse in Household Surveys Based on the Norwegian Labour Force Survey and the Statistics on Income and Living Conditions Survey

Andersson, P.G. and C.-E. Särndal. 2016. “Calibration for nonresponse treatment: in one or two steps?” Statistical Journal of the IAOS 32: 1–7.10.3233/SJI-160996Search in Google Scholar

Bang, H. and J.M. Robins. 2005. “Doubly robust estimation in missing data and causal inference models.” Biometrics 61: 962–972. DOI: https://doi.org/10.1111/j.1541-0420.2005.00377.x.10.1111/j.1541-0420.2005.00377.x16401269Search in Google Scholar

Bethlehem, J.G. 1988. “Reduction of nonresponse bias through regression estimation.” Journal of Official Statistics 4: 251–260. Available at: https://search.proquest.com/openview/90feaa08b1f293c8544d8576e6d94436/1?pq-origsite=gscholar&cbl=105444 (accessed February 2020).Search in Google Scholar

Bethlehem, J., F. Cobben, and B. Schouten. 2011. Handbook of Nonresponse in Household Surveys. Hoboken: Wiley.10.1002/9780470891056Search in Google Scholar

Brick, M.J. 2013. “Unit nonresponse and weighting adjustments: A critical review.” Journal of Official Statistics 29: 329–353. DOI: https://doi.org/10.2478/jos-2013-0026.10.2478/jos-2013-0026Search in Google Scholar

Canty, A. and A. Davison. 1999. “Resampling-based Variance Estimation for Labour Force Surveys.” The Statistician 48: 379–391.10.1111/1467-9884.00196Search in Google Scholar

Carpenter, J.R., M.G. Kenward, and S. Vansteelandt. 2006. “A comparison of multiple imputation and doubly robust estimation for analyses with missing data.” J. R. Statist. Soc. A 169: 571–584. DOI: https://doi.org/10.1111/j.1467-985X.2006.00407.x.10.1111/j.1467-985X.2006.00407.xSearch in Google Scholar

Cassel, C.M., C.-E. Särndal, and J.H. Wretman. 1983. “Some uses of statistical models in connection with the nonresponse problem.” In Incomplete Data in Sample Surveys, edited by W.G. Madow, I. Olkin, and D.B. Rubin, vol. 3, 143–160. New York: Academic Press.Search in Google Scholar

Cochran, W. 1953. Sampling Techniques. New York: Wiley.Search in Google Scholar

De Leeuw, E. and W. de Heer. 2002. “Trends in household survey nonresponse – a longitudinal and international comparison.” In Survey Nonresponse, edited by R. Groves, D. Dillman, J. Eltinge, and R.J.A. Little, 41–54. New York: Wiley. Available at: https://www.wiley.com/en-us/Survey+Nonresponse-p-9780471396277.Search in Google Scholar

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. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X19970013103 (accessed February 2020).Search in Google Scholar

Haziza, D. and É. Lesage. 2016. “A discussion of weighting procedures for unit nonresponse.” Journal of Official Statistics 32: 129–145. DOI: https://doi.org/10.1515/jos-2016-0006.10.1515/jos-2016-0006Search in Google Scholar

Kalton, G. and I. Flores-Cervantes. 2003. “Weighting methods.” Journal of Official Statistics 19: 81–97. Available at: https://www.scb.se/contentassets/ca21efb41-fee47d293bbee5bf7be7fb3/weighting-methods.pdf (accessed February 2020).Search in Google Scholar

Kalton, G. and D. Kasprzyk. 1986. “The treatment of missing survey data.” Survey Methodology 12: 1–16. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X198600114404 (accessed February 2020).Search in Google Scholar

Kang, J.D.Y. and J.L. Schafer. 2007. “Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data.” Statistical Science 22: 523–539. DOI: https://doi.org/10.1214/07-STS227.10.1214/07-STS227239755518516239Search in Google Scholar

Kim, J. and D. Haziza. 2014. “Doubly robust inference with missing data in survey sampling.” Statistica Sinica 24: 375–394. DOI: http://doi.org/10.5705/ss.2012.005.10.5705/ss.2012.005Search in Google Scholar

Kott, P.S. and D. Liao. 2015. “One step or two? Calibration weighting from a complete list frame with nonresponse.” Survey Methodology 41: 165–181. Available at: https://www150.statcan.gc.ca/n1/pub/12-001-x/2015001/article/14172-eng.htm (accessed February 2020).Search in Google Scholar

Little, R.J.A. 1986. “Survey nonresponse adjustments for estimates of means.” International Statistical Review 54: 139–157. DOI: https://doi.org/10.2307/1403140.10.2307/1403140Search in Google Scholar

Little, R.J. and D.B. Rubin. 1987. Statistical Analysis with Missing Data. New York: Wiley.Search in Google Scholar

Little, R.J. and S. Vartivarian. 2005. “Does weighting for nonresponse increase the variance of survey means?” Survey Methodology 31: 161–168. Available at: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20050029046 (accessed February 2020).Search in Google Scholar

Lunceford, J.K. and M. Davidian. 2004. “Stratification and weighting via the propensity score in estimation of causal treatment effects: A comparative study.” Statist. Med. 23: 2937–2960. DOI: https://doi.org/10.1002/sim.1903.10.1002/sim.190315351954Search in Google Scholar

Lundström, S. and C.-E. Särndal. 1999. “Calibration as a Standard Method for Treatment of Nonresponse.” Journal of Official Statistics 15: 305–327. DOI: https://doi.org/10.1002/sim.1903.10.1002/sim.190315351954Search in Google Scholar

Meyer, B.D., W.K.C. Mok, and J.X. Sullivan. 2015. “Household surveys in crisis.” Journal of Economic Perspectives 29: 199–226. Available at: https://pubs.aeaweb.org/doi/pdf/10.1257/jep.29.4.199 (accessed September 2019).10.1257/jep.29.4.199Search in Google Scholar

Oh, H.L. and F.S. Scheuren. 1983. “Weighting adjustments for unit nonresponse.” In Incomplete Data in Sample Surveys, edited by W.G. Madow, I. Olkin, and D.B. Rubin, vol. 2, 143–184. New York: Academic Press.Search in Google Scholar

Robins, B.J.M. and N. Wang. 2000. “Inference for imputation estimators.” Biometrika 87: 113–124. Available at: https://pdfs.semanticscholar.org/3214/538c562c010bdb-b055582e04fc3a4d7b6ce9.pdf (accessed February 2020).Search in Google Scholar

Robins, J.M., A. Rotnitzky, and L.P. Zhao. 1994. “Estimation of regression coefficients when some regressors are not always observed.” J. Am. Statist. Ass. 89: 846–866. DOI: https://doi.org/10.1080/01621459.1994.10476818.10.1080/01621459.1994.10476818Search in Google Scholar

Särndal, C.-E. and S. Lundström. 2005. Estimation in Surveys with Nonresponse. Chichester: Wiley.10.1002/0470011351Search in Google Scholar

Särndal, C.-E. and S. Lundström. 2008. “Assessing auxiliary vectors for control of nonresponse bias in the calibration estimator.” Journal of Official Statistics 24: 167–191. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/assessing-auxiliary-vectors-for-control-of-nonresponse-bias-in-the-calibration-estimator.pdf (accessed February 2020).Search in Google Scholar

Särndal, C.-E. and S. Lundström. 2010. “Design for estimation: Identifying auxiliary vectors to reduce nonresponse bias.” Survey Methodology 36: 131–144. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2010002/article/11376-eng.pdf?st=XFmzYjVz (accessed February 2020).Search in Google Scholar

Schouten, B. 2007. “A selection strategy for weighting variables under a not-missing- at-random assumption.” Journal of Official Statistics 23: 51–68. Available at: https://pdfs.semanticscholar.org/634f/ca032e7c166eef11ee65b84192e5aca1e039.pdf (accessed February 2020).Search in Google Scholar

Stoop, I., J. Billiet, A. Koch, and R. Fitzgerald. 2010. Improving Survey Response: Lessons Learned from the European Social Survey. Chichester, UK: Wiley.10.1002/9780470688335Search in Google Scholar

Thomsen, I. 1973. “A note on the efficiency of weighting subclass means to reduce the effects of non-response when analyzing survey data.” Statistisk Tidskrift 11: 278–285. Available at: https://www.statistics.no/a/histstat/ano/ano_io73_02.pdf (accessed February 2020).Search in Google Scholar

Thomsen, I. 1978. “A second note on the efficiency of weighting subclass means to reduce the effects of non-response when analyzing survey data.” Statistisk Tidskrift 16: 278–285. Available at: https://www.statistics.no/a/histstat/ano/ano_io73_02.pdf (accessed February 2020).Search in Google Scholar

Thomsen, I. and L.-C. Zhang. 2001. “The effects of using administrative registers in economic short term statistics: The Norwegian Labour Force Survey as a case study.” Journal of Official Statistics 17: 285–294. Available at: https://www.scb.se/content-assets/ca21efb41fee47d293bbee5bf7be7fb3/the-effects-of-using-administrative-regis-ters-in-economic-short-term-statistics-the-norwegian-labour-force-survey-as-a-case-study.pdf (accessed February 2020).Search in Google Scholar

Zhang, L.-C. 1999. “A note on post-stratification when analyzing binary survey data subject to nonresponse.” Journal of Official Statistics 15: 329–334. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/a-note-on-post-stratification-when-analyzing-binary-survey-data-subject-to-nonresponse.pdf (accessed February 2020).Search in Google Scholar

Zhang, L.-C. 2005. “On the bias in gross labour flow estimates due to nonresponse and mis-classification.” Journal of Official Statistics 21: 591–604. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/on-the-bias-in-gross-labour-flow-estimates-due-to-nonresponse-and-misclassification.pdf (accessed February 2020).Search in Google Scholar

Zhang, L.-C., I. Thomsen, and Ø. Kleven. 2013. “On the use of auxiliary and para-data for dealing with non-sampling errors in household surveys.” International Statistical Review 81: 270–288. DOI: https://doi.org/10.1111/insr.12009.10.1111/insr.12009Search in Google Scholar