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Data set representativeness during data collection in three UK social surveys: generalizability and the effects of auxiliary covariate choice

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  • Jamie C. Moore
  • Gabriele B. Durrant
  • Peter W. F. Smith

Abstract

We consider the use of representativeness indicators to monitor risks of non‐response bias during survey data collection. The analysis benefits from use of a unique data set linking call record paradata from three UK social surveys to census auxiliary attribute information on sample households. We investigate the utility of census information for this purpose and the performance of representativeness indicators (the R‐indicator and the coefficient of variation of response propensities) in monitoring representativeness over call records. We also investigate the extent and effects of misspecification of auxiliary covariate sets used in indicator computation and design phase capacity points in call records beyond which survey data set improvements are minimal, and whether such points are generalizable across surveys. Given our findings, we then offer guidance to survey practitioners on the use of such methods and implications for optimizing data collection and efficiency savings.

Suggested Citation

  • Jamie C. Moore & Gabriele B. Durrant & Peter W. F. Smith, 2018. "Data set representativeness during data collection in three UK social surveys: generalizability and the effects of auxiliary covariate choice," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 229-248, January.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:1:p:229-248
    DOI: 10.1111/rssa.12256
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    Citations

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    Cited by:

    1. van Berkel Kees & van der Doef Suzanne & Schouten Barry, 2020. "Implementing Adaptive Survey Design with an Application to the Dutch Health Survey," Journal of Official Statistics, Sciendo, vol. 36(3), pages 609-629, September.
    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. Tobias Gummer & Bella Struminskaya, 2023. "Early and Late Participation during the Field Period: Response Timing in a Mixed-Mode Probability-Based Panel Survey," Sociological Methods & Research, , vol. 52(2), pages 909-932, May.
    4. Patrick Gleiser & Joseph W. Sakshaug & Marieke Volkert & Peter Ellguth & Susanne Kohaut & Iris Möller, 2022. "Introducing Web in a mixed‐mode establishment survey: Effects on nonresponse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 891-915, July.
    5. Wu Shiya & Schouten Barry & Meijers Ralph & Moerbeek Mirjam, 2022. "Data Collection Expert Prior Elicitation in Survey Design: Two Case Studies," Journal of Official Statistics, Sciendo, vol. 38(2), pages 637-662, June.
    6. Roberts Caroline & Vandenplas Caroline & Herzing Jessica M.E., 2020. "A Validation of R-Indicators as a Measure of the Risk of Bias using Data from a Nonresponse Follow-Up Survey," Journal of Official Statistics, Sciendo, vol. 36(3), pages 675-701, September.
    7. Jamie C. Moore & Gabriele B. Durrant & Peter W. F. Smith, 2021. "Do coefficients of variation of response propensities approximate non‐response biases during survey data collection?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 301-323, January.
    8. 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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