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Responsive design for household surveys: tools for actively controlling survey errors and costs

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  • Robert M. Groves
  • Steven G. Heeringa
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    Abstract

    Over the past few years surveys have expanded to new populations, have incorporated measurement of new and more complex substantive issues and have adopted new data collection tools. At the same time there has been a growing reluctance among many household populations to participate in surveys. These factors have combined to present survey designers and survey researchers with increased uncertainty about the performance of any given survey design at any particular point in time. This uncertainty has, in turn, challenged the survey practitioner's ability to control the cost of data collection and quality of resulting statistics. The development of computer-assisted methods for data collection has provided survey researchers with tools to capture a variety of process data ('paradata') that can be used to inform cost-quality trade-off decisions in realtime. The ability to monitor continually the streams of process data and survey data creates the opportunity to alter the design during the course of data collection to improve survey cost efficiency and to achieve more precise, less biased estimates. We label such surveys as 'responsive designs'. The paper defines responsive design and uses examples to illustrate the responsive use of paradata to guide mid-survey decisions affecting the non-response, measurement and sampling variance properties of resulting statistics. Copyright 2006 Royal Statistical Society.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

    Volume (Year): 169 (2006)
    Issue (Month): 3 ()
    Pages: 439-457

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    Handle: RePEc:bla:jorssa:v:169:y:2006:i:3:p:439-457

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    Cited by:
    1. William Axinn & Cynthia Link & Robert Groves, 2011. "Responsive Survey Design, Demographic Data Collection, and Models of Demographic Behavior," Demography, Springer, vol. 48(3), pages 1127-1149, August.
    2. Gabriele B. Durrant & Julia D'Arrigo & Fiona Steele, 2011. "Using field process data to predict best times of contact conditioning on household and interviewer influences," LSE Research Online Documents on Economics 52201, London School of Economics and Political Science, LSE Library.
    3. repec:ese:iserwp:2008-01 is not listed on IDEAS
    4. Bethmann, Arne (Hrsg.) & Fuchs, Benjamin (Hrsg.) & Wurdack, Anja (Hrsg.), 2013. "User Guide "Panel Study Labour Market and Social Security" (PASS) : Wave 6," FDZ Datenreport. Documentation on Labour Market Data 201307_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    5. repec:ese:iserwp:2008-41 is not listed on IDEAS
    6. Sauermann, Henry & Roach, Michael, 2013. "Increasing web survey response rates in innovation research: An experimental study of static and dynamic contact design features," Research Policy, Elsevier, vol. 42(1), pages 273-286.
    7. Barry Schouten & Natalie Shlomo & Chris J. Skinner, 2011. "Indicators for monitoring and improving representativeness of response," LSE Research Online Documents on Economics 39121, London School of Economics and Political Science, LSE Library.
    8. Marcus Böhme, Tobias Stoehr, 2012. "Guidelines for the Use of Household Interview Duration Analysis in CAPI Survey Management," Kiel Working Papers 1779, Kiel Institute for the World Economy.
    9. Bogumil Kaminski & Mariusz Kozakiewicz & Wit Jakuczun & Malgorzata Poltorak, 2012. "An optimal assignment procedure for multiple online surveys," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 4, pages 69-85.

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