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Re-engaging with survey non-respondents: evidence from three household panels

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  • Nicole Watson
  • Mark Wooden

Abstract

type="main" xml:id="rssa12024-abs-0001"> Previous research into the correlates and determinants of non-response in longitudinal surveys has focused exclusively on why it is that respondents at one survey wave choose not to participate at future waves. This is very understandable if non-response is always an absorbing state, but in many longitudinal surveys, and certainly most household panels, this is not so. Indeed, in these surveys it is normal practice to attempt to make contact with many non-respondents at the next wave. This study differs from previous research by examining re-engagement with previous wave non-respondents. Drawing on data from three national household panels it is found that the re-engagement decision is indeed distinctly different from the decision about continued participation. Further, these differences have clear implications for the way that panel surveys should be administered given the desire to enhance overall response rates.

Suggested Citation

  • Nicole Watson & Mark Wooden, 2014. "Re-engaging with survey non-respondents: evidence from three household panels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 499-522, February.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:499-522
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-2
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    Cited by:

    1. Cobb-Clark, Deborah A. & Herault, Nicolas & Scutella, Rosanna & Tseng, Yi-Ping, 2016. "A journey home: What drives how long people are homeless?," Journal of Urban Economics, Elsevier, vol. 91(C), pages 57-72.
    2. Matthew Manning & Christopher L. Ambrey & Christopher M. Fleming, 2016. "A Longitudinal Study of Indigenous Wellbeing in Australia," Journal of Happiness Studies, Springer, vol. 17(6), pages 2503-2525, December.
    3. Satya Paul, 2022. "Effects of Happiness on Income and Income Inequality," Journal of Happiness Studies, Springer, vol. 23(3), pages 1021-1041, March.
    4. Otterbach, Steffen & Wooden, Mark & Fok, Yin King, 2016. "Working-Time Mismatch and Mental Health," IZA Discussion Papers 9818, Institute of Labor Economics (IZA).
    5. Bianchi Annamaria & Biffignandi Silvia & Lynn Peter, 2017. "Web-Face-to-Face Mixed-Mode Design in a Longitudinal Survey: Effects on Participation Rates, Sample Composition, and Costs," Journal of Official Statistics, Sciendo, vol. 33(2), pages 385-408, June.
    6. Bettina Müller & Laura Castiglioni, 2020. "Do Temporary Dropouts Improve the Composition of Panel Data? An Analysis of “Gap Interviews†in the German Family Panel pairfam," Sociological Methods & Research, , vol. 49(1), pages 193-215, February.
    7. André Hajek & Hans-Helmut König, 2020. "Prevalence and Correlates of Individuals Screening Positive for Depression and Anxiety on the PHQ-4 in the German General Population: Findings from the Nationally Representative German Socio-Economic ," IJERPH, MDPI, vol. 17(21), pages 1-11, October.
    8. Plewis Ian & Shlomo Natalie, 2017. "Using Response Propensity Models to Improve the Quality of Response Data in Longitudinal Studies," Journal of Official Statistics, Sciendo, vol. 33(3), pages 753-779, September.
    9. Namazi-Rad, Mohammad-Reza & Mokhtarian, Payam & Shukla, Nagesh & Munoz, Albert, 2016. "A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data," Journal of choice modelling, Elsevier, vol. 20(C), pages 49-60.

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