IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/52201.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/52201/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kristen Olson, 2013. "Paradata for Nonresponse Adjustment," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 142-170, January.
    2. Roger Tourangeau & J. Michael Brick & Sharon Lohr & Jane Li, 2017. "Adaptive and responsive survey designs: a review and assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 203-223, January.
    3. 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.
    4. Walejko Gina & Wagner James, 2018. "A Study of Interviewer Compliance in 2013 and 2014 Census Test Adaptive Designs," Journal of Official Statistics, Sciendo, vol. 34(3), pages 649-670, September.
    5. 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.
    6. Early Kirstin & Mankoff Jennifer & Fienberg Stephen E., 2017. "Dynamic Question Ordering in Online Surveys," Journal of Official Statistics, Sciendo, vol. 33(3), pages 625-657, September.
    7. Chun Asaph Young & Schouten Barry & Wagner James, 2017. "JOS Special Issue on Responsive and Adaptive Survey Design: Looking Back to See Forward – Editorial: In Memory of Professor Stephen E. Fienberg, 1942–2016," Journal of Official Statistics, Sciendo, vol. 33(3), pages 571-577, September.
    8. Al-Kandari Noriah M. & Lahiri Partha, 2016. "Prediction of a Function of Misclassified Binary Data," Statistics in Transition New Series, Polish Statistical Association, vol. 17(3), pages 429-447, September.
    9. Reza C. Daniels, 2012. "A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys," SALDRU Working Papers 90, Southern Africa Labour and Development Research Unit, University of Cape Town.
    10. Reist, Benjamin M. & Rodhouse, Joseph B. & Ball, Shane T. & Young, Linda J., 2019. "Subsampling of Nonrespondents in the 2017 Census of Agriculture," NASS Research Reports 322826, United States Department of Agriculture, National Agricultural Statistics Service.
    11. Alireza Rezaee & Mojtaba Ganjali & Ehsan Bahrami Samani, 2022. "Sample selection bias with multiple dependent selection rules: an application to survey data analysis with multilevel nonresponse," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-15, December.
    12. Lewis Taylor, 2017. "Univariate Tests for Phase Capacity: Tools for Identifying When to Modify a Survey’s Data Collection Protocol," Journal of Official Statistics, Sciendo, vol. 33(3), pages 601-624, September.
    13. Jiayun Jin & Caroline Vandenplas & Geert Loosveldt, 2019. "The Evaluation of Statistical Process Control Methods to Monitor Interview Duration During Survey Data Collection," SAGE Open, , vol. 9(2), pages 21582440198, June.
    14. Andy Peytchev, 2013. "Consequences of Survey Nonresponse," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 88-111, January.
    15. repec:iab:iabfda:201307(en is not listed on IDEAS
    16. Roberts Caroline & Herzing Jessica M.E. & Vandenplas Caroline, 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.
    17. Böhme, Marcus & Stöhr, Tobias, 2012. "Guidelines for the use of household interview duration analysis in CAPI survey management," Kiel Working Papers 1779, Kiel Institute for the World Economy (IfW Kiel).
    18. Rebecca Vassallo & Gabriele Durrant & Peter Smith, 2017. "Separating interviewer and area effects by using a cross-classified multilevel logistic model: simulation findings and implications for survey designs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 531-550, February.
    19. Dasylva Abel, 2018. "Design-Based Estimation with Record-Linked Administrative Files and a Clerical Review Sample," Journal of Official Statistics, Sciendo, vol. 34(1), pages 41-54, March.
    20. Mario Callegaro & Charlotte Steeh & Trent D. Buskirk & Vasja Vehovar & Vesa Kuusela & Linda Piekarski, 2007. "Fitting disposition codes to mobile phone surveys: experiences from studies in Finland, Slovenia and the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 647-670, July.
    21. Raphael Nishimura & James Wagner & Michael Elliott, 2016. "Alternative Indicators for the Risk of Non-response Bias: A Simulation Study," International Statistical Review, International Statistical Institute, vol. 84(1), pages 43-62, April.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:52201. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.