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Using field process data to predict best times of contact conditioning on household and interviewer influences

Author

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  • Durrant, Gabriele B.
  • D'Arrigo, Julia
  • Steele, Fiona

Abstract

Establishing contact is an important part of the response process and effective interviewer calling behaviours are critical in achieving contact and subsequent co-operation. The paper investigates best times of contact for different types of households and the influence of the interviewer on establishing contact. Recent developments in the survey data collection process have led to the collection of so-called field process data or paradata, which greatly extend the basic information on interviewer calls. The paper develops a multilevel discrete time event history model based on interviewer call record data to predict the likelihood of contact at each call. The results have implications for survey practice and inform the design of effective interviewer calling times, including responsive survey designs.

Suggested Citation

  • Durrant, Gabriele B. & D'Arrigo, Julia & Steele, Fiona, 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.
    • Handle: RePEc:ehl:lserod:52201
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    File URL: http://eprints.lse.ac.uk/52201/
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    References listed on IDEAS

    as
    1. Gabriele B. Durrant & Fiona Steele, 2009. "Multilevel modelling of refusal and non‐contact in household surveys: evidence from six UK Government surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 361-381, April.
    2. Angela M. Wood & Ian R. White & Matthew Hotopf, 2006. "Using number of failed contact attempts to adjust for non‐ignorable non‐response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 525-542, July.
    3. P. Lahiri & Michael D. Larsen, 2005. "Regression Analysis With Linked Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 222-230, March.
    4. Robert M. Groves & Steven G. Heeringa, 2006. "Responsive design for household surveys: tools for actively controlling survey errors and costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 439-457, July.
    5. Fiona Steele & Ian Diamond & Sajeda Amin, 1996. "Immunization Uptake in Rural Bangladesh: A Multilevel Analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 289-299, March.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Gabriele B. Durrant & Sylke V. Schnepf, 2018. "Which schools and pupils respond to educational achievement surveys?: a focus on the English Programme for International Student Assessment sample," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1057-1075, October.
    2. Ronald R. Rindfuss & Minja K. Choe & Noriko O. Tsuya & Larry L. Bumpass & Emi Tamaki, 2015. "Do low survey response rates bias results? Evidence from Japan," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 32(26), pages 797-828.
    3. Gabriele B. Durrant & Julia D’Arrigo, 2014. "Doorstep Interactions and Interviewer Effects on the Process Leading to Cooperation or Refusal," Sociological Methods & Research, , vol. 43(3), pages 490-518, August.
    4. Lagorio, Carlos, 2016. "Call and response: modelling longitudinal contact and cooperation using Wave 1 call records data," Understanding Society Working Paper Series 2016-01, Understanding Society at the Institute for Social and Economic Research.
    5. Durrant Gabriele B. & Maslovskaya Olga & Smith Peter W. F., 2017. "Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?," Journal of Official Statistics, Sciendo, vol. 33(3), pages 801-833, September.
    6. Steele, Fiona & Durrant, Gabriele B., 2011. "Alternative approaches to multilevel modelling of survey non-contact and refusal," LSE Research Online Documents on Economics 50113, London School of Economics and Political Science, LSE Library.
    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.

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

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