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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

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  • Nguyen Nancy Duong

    (School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland.)

  • Zhang Li-Chun

    (Department of Social Statistics and Demography, University of Southampton, Southampton, UK.)

Abstract

Despite increasing efforts during data collection, nonresponse remains sizeable in many household surveys. Statistical adjustment is hence unavoidable. By reweighting the design, weights of the respondents are adjusted to compensate for nonresponse. However, there is no consensus on how this should be carried out in general. Theoretical comparisons are inconclusive in the literature, and the associated simulation studies involve hypothetical situations not all equally relevant to reality. In this article we evaluate the three most common reweighting approaches in practice, based on real data in Norway from the two largest household surveys in the European Statistical System. We demonstrate how cross- examination of various reweighting estimators can help inform the effectiveness of the available auxiliary variables and the choice of the weight adjustment method.

Suggested Citation

  • Nguyen Nancy Duong & Zhang Li-Chun, 2020. "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," Journal of Official Statistics, Sciendo, vol. 36(1), pages 151-172, March.
    • Handle: RePEc:vrs:offsta:v:36:y:2020:i:1:p:151-172:n:8
    DOI: 10.2478/jos-2020-0008
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    References listed on IDEAS

    as
    1. James R. Carpenter & Michael G. Kenward & Stijn Vansteelandt, 2006. "A comparison of multiple imputation and doubly robust estimation for analyses with missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 571-584, July.
    2. repec:mpr:mprres:4937 is not listed on IDEAS
    3. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    4. Li-Chun Zhang & Ib Thomsen & Øyvin Kleven, 2013. "On the Use of Auxiliary and Paradata for Dealing With Non-sampling Errors in Household Surveys," International Statistical Review, International Statistical Institute, vol. 81(2), pages 270-288, August.
    5. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
    6. repec:mpr:mprres:4780 is not listed on IDEAS
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