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Joint Response Propensity And Calibration Method

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  • Laaksonen Seppo

    (University of Helsinki, ; Helsinki, ; Finland)

  • Hämäläinen Auli

    (University of Helsinki, ; Helsinki, ; Finland)

Abstract

This paper examines the chain of weights, beginning with the basic sampling weights for the respondents. These were then converted to reweights to reduce the bias due to missing quantities. If micro auxiliary variables are available for a gross sample, we suggest taking advantage first of the response propensity weights, and then of the calibrated weights with macro (aggregate) auxiliary variables. We also examined the calibration methodology that starts from the basic weights. Simulated data based on a real survey were used for comparison. The sampling design used was stratified simple random sampling, but the same methodology works for multi-stage sampling as well. Eight indicators were examined and estimated. We found differences in the performance of the reweighting methods. However, the main conclusion was that the response propensity weights are the best starting weights for calibration, since the auxiliary variables can be more completely exploited in this case. We also tested problems of calibration methods, since some weights may lead to unacceptable weights, such as below 1 or even negative.

Suggested Citation

  • Laaksonen Seppo & Hämäläinen Auli, 2018. "Joint Response Propensity And Calibration Method," Statistics in Transition New Series, Statistics Poland, vol. 19(1), pages 45-60, March.
    • Handle: RePEc:vrs:stintr:v:19:y:2018:i:1:p:45-60:n:1
    DOI: 10.21307/stattrans-2018-003
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    References listed on IDEAS

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    1. Kott, Phillip S. & Chang, Ted, 2010. "Using Calibration Weighting to Adjust for Nonignorable Unit Nonresponse," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1265-1275.
    2. Thomas Lumley & Pamela A. Shaw & James Y. Dai, 2011. "Connections between Survey Calibration Estimators and Semiparametric Models for Incomplete Data," International Statistical Review, International Statistical Institute, vol. 79(2), pages 200-220, August.
    3. J. Michael Brick & Michael E. Jones, 2008. "Propensity to respond and nonresponse bias," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 51-73.
    4. Martin Wittenberg, 2009. "Sample Survey Calibration: An Informationtheoretic perspective," SALDRU Working Papers 41, Southern Africa Labour and Development Research Unit, University of Cape Town.
    5. Brick J. Michael, 2013. "Unit Nonresponse and Weighting Adjustments: A Critical Review," Journal of Official Statistics, Sciendo, vol. 29(3), pages 329-353, June.
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