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Multilevel modelling of refusal and non‐contact in household surveys: evidence from six UK Government surveys

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  • Gabriele B. Durrant
  • Fiona Steele

Abstract

Summary. We analyse household unit non‐response in six major UK Government surveys by using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of the extent to which effects of household characteristics are survey specific. The analysis is based on the 2001 UK Census Link Study, which is a unique source of data containing an unusually rich set of auxiliary variables. The study contains the response outcome of six surveys, linked to census data and interviewer observations for both respondents and non‐respondents.

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  • 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.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:2:p:361-381
    DOI: 10.1111/j.1467-985X.2008.00565.x
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    1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    2. Jan Pickery & Geert Loosveldt & Ann Carton, 2001. "The Effects of Interviewer and Respondent Characteristics on Response Behavior in Panel Surveys," Sociological Methods & Research, , vol. 29(4), pages 509-523, May.
    3. Jan Pickery & Geert Loosveldt, 2002. "A Multilevel Multinomial Analysis of Interviewer Effects on Various Components of Unit Nonresponse," Quality & Quantity: International Journal of Methodology, Springer, vol. 36(4), pages 427-437, November.
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    1. Schnepf, Sylke V. & Durrant, Gabriele B. & Micklewright, John, 2014. "Which Schools and Pupils Respond to Educational Achievement Surveys? A Focus on the English PISA Sample," IZA Discussion Papers 8411, Institute of Labor Economics (IZA).
    2. Michele Lalla & Maddalena Cavicchioli, 2020. "Nonresponse and measurement errors in income: matching individual survey data with administrative tax data," Department of Economics 0170, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    3. 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.
    4. Francisco Perales & Bernard Baffour & Francis Mitrou, 2015. "Ethnic Differences in the Quality of the Interview Process and Implications for Survey Analysis: The Case of Indigenous Australians," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.
    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. 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.
    7. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    8. Adriana Ana Maria Davidescu & Monica Roman & Vasile Alecsandru Strat & Mihaela Mosora, 2019. "Regional Sustainability, Individual Expectations and Work Motivation: A Multilevel Analysis," Sustainability, MDPI, vol. 11(12), pages 1-23, June.
    9. Maddalena Cavicchioli & Michele Lalla, 2022. "Evidences from survey data and fiscal data: nonresponse and measurement errors in annual incomes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 587-615, September.
    10. 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.
    11. Wagner James & Olson Kristen, 2018. "An Analysis of Interviewer Travel and Field Outcomes in Two Field Surveys," Journal of Official Statistics, Sciendo, vol. 34(1), pages 211-237, March.
    12. 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.
    13. Mark Amos, 2018. "Interviewer effects on patterns of nonresponse: Evaluating the impact on the reasons for contraceptive nonuse in the Indonesia and the Philippines DHS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 39(14), pages 415-430.
    14. Geert Loosveldt & Koen Beullens, 2014. "A Procedure to Assess Interviewer Effects on Nonresponse Bias," SAGE Open, , vol. 4(1), pages 21582440145, February.
    15. 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.
    16. Finaba Berete & Johan Van der Heyden & Stefaan Demarest & Rana Charafeddine & Lydia Gisle & Elise Braekman & Jean Tafforeau & Geert Molenberghs, 2019. "Determinants of unit nonresponse in multi-mode data collection: A multilevel analysis," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-18, April.
    17. Khudnitskaya, Alesia S., 2009. "Microenvironment-specific Effects in the Application Credit Scoring Model," MPRA Paper 23175, University Library of Munich, Germany.
    18. 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.
    19. 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.
    20. Coffey Stephanie & Elliott Michael R., 2023. "Predicting Days to Respondent Contact in Cross-Sectional Surveys Using a Bayesian Approach," Journal of Official Statistics, Sciendo, vol. 39(3), pages 325-349, September.
    21. 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.
    22. 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.

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