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Estimation of an indicator of the representativeness of survey response

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  • Shlomo, Natalie
  • Skinner, Chris J.
  • Schouten, Barry

Abstract

Nonresponse is a major source of estimation error in sample surveys. The response rate is widely used to measure survey quality associated with nonresponse, but is inadequate as an indicator because of its limited relation with nonresponse bias. Schouten et al. (2009) proposed an alternative indicator, which they refer to as an indicator of representativeness or R-indicator. This indicator measures the variability of the probabilities of response for units in the population. This paper develops methods for the estimation of this R-indicator assuming that values of a set of auxiliary variables are observed for both respondents and nonrespondents. We propose bias adjustments to the point estimator proposed by Schouten et al. (2009) and demonstrate the effectiveness of this adjustment in a simulation study where it is shown that the method is valid, especially for smaller sample sizes. We also propose linearization variance estimators which avoid the need for computer-intensive replication methods and show good coverage in the simulation study even when models are not fully specified. The use of the proposed procedures is also illustrated in an application to two business surveys at Statistics Netherlands.

Suggested Citation

  • Shlomo, Natalie & Skinner, Chris J. & Schouten, Barry, 2012. "Estimation of an indicator of the representativeness of survey response," LSE Research Online Documents on Economics 39124, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:39124
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    File URL: http://eprints.lse.ac.uk/39124/
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    References listed on IDEAS

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    1. F. Kreuter & K. Olson & J. Wagner & T. Yan & T. M. Ezzati‐Rice & C. Casas‐Cordero & M. Lemay & A. Peytchev & R. M. Groves & T. E. Raghunathan, 2010. "Using proxy measures and other correlates of survey outcomes to adjust for non‐response: examples from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 389-407, April.
    2. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
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    Cited by:

    1. Barry Schouten & Natalie Shlomo, 2017. "Selecting Adaptive Survey Design Strata with Partial R-indicators," International Statistical Review, International Statistical Institute, vol. 85(1), pages 143-163, April.
    2. Olga Maslovskaya & Peter Lugtig, 2022. "Representativeness in six waves of CROss‐National Online Survey (CRONOS) panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 851-871, July.
    3. Barry Schouten & Fannie Cobben & Peter Lundquist & James Wagner, 2016. "Does more balanced survey response imply less non-response bias?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 727-748, June.
    4. Thais Paiva & Jerry Reiter, 2014. "Using Imputation Techniques To Evaluate Stopping Rules In Adaptive Survey Design," Working Papers 14-40, Center for Economic Studies, U.S. Census Bureau.
    5. Jamie C. Moore & Peter W. F. Smith & Gabriele B. Durrant, 2018. "Correlates of record linkage and estimating risks of non‐linkage biases in business data sets," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1211-1230, October.
    6. Dan Hedlin, 2020. "Is there a 'safe area' where the nonresponse rate has only a modest effect on bias despite non‐ignorable nonresponse?," International Statistical Review, International Statistical Institute, vol. 88(3), pages 642-657, December.

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    More about this item

    Keywords

    nonresponse; quality; representative; response propensity; sample survey;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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