| Peer-Reviewed

Adaptive Survey Design for the Dutch Labour Force Survey

Received: 19 July 2022    Accepted: 23 August 2022    Published: 31 August 2022
Views:       Downloads:
Abstract

A challenge for the National Statistical Institutes is to produce reliable statistics with a limited budget for data collection. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done on the impact on quality and costs of reducing the use of interviewers in mixed-mode surveys that start with Internet observation, followed by telephone or face-to-face observation of Internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. For this purpose, a methodology has been developed in this paper. The methodology is applied in the development of an adaptive survey design for the Dutch Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and some response and survey results.

Published in American Journal of Theoretical and Applied Statistics (Volume 11, Issue 4)
DOI 10.11648/j.ajtas.20221104.12
Page(s) 114-121
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Balanced Response, Nonresponse Bias, Accuracy, Data Collection

References
[1] Alabama R package. Available at: https://CRAN.Rproject=alabama.
[2] Berkel, C. van, S. van der Doef, and B. Schouten. (2020). Implementing Adaptive Survey Design with an Application to the Dutch Health Survey. Journal of Official Statistics 36 (3): 609–629. DOI: http://dx.doi.org/10.2478/JOS-2020-0031.
[3] Bethlehem, J. G. (1988). Reduction of Nonresponse Bias Through Regression Estimation. Journal of Official Statistics 4 (3): 251–260. Available at: https://www.scb.se/contentassets /-ca21efb41fee47d293bbee5bf7be7fb3/reduction-of-nonresponse-bias-through-regression-estimation.pdf.
[4] Brakel, J. van den. (2021). Monthly Labour Force Figures during the 2021 Redesign of the Dutch Labour Force Survey. Statistics Netherlands, discussion paper, January 2022. Available at: https://www.cbs.nl/-/media/_pdf/2022/03/lfs-redesign-2021.pdf.
[5] Brick, J. M. and R. Tourangeau. (2017). Responsive Survey Designs for reducing Nonresponse Bias. Journal of Official Statistics 33 (3): 735–752. DOI: https://doi.org/10.1515/jos-2017-0034.
[6] Burger, J., K. Perryck and B. Schouten. (2017). Robustness of Adaptive Survey Designs to Inaccuracy of Design Parameters. Journal of Official Statistics 33 (3): 687–708. DOI: https://doi.org/10.1515/jos-2017-0034.
[7] Chun, A. Y., S. G. Heeringa and B. Schouten. (2018). Responsive and adaptive design for survey optimization. Journal of Official Statistics 34 (3): 581–597. DOI: https://doi.org/10.2478/jos-2018-0028.
[8] Groves, R. M., M. P. Couper, S. Presser, E. Singer, R. Tourangeau, G. P. Acosta, and L. Nelson. (2006). Experiments in producing nonresponse bias. Public Opinion Quarterly 70 (5): 720–736. Available at: https://www.jstor.org/stable/4124223#metadata_info_tab_contents.
[9] Groves, R. M. and S. G. Heeringa. (2006). Responsive Design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs. Journal of the Royal Statistical Society, Series A169: 439–457. DOI: http://dx.doi.org/10.1111/j.1467-985X.2006.00423.x.
[10] Klausch, T., J. Hox, and B. Schouten. (2017). Evaluating bias of sequential mixed-mode designs against benchmark surveys. Sociological Methods & Research 46 (3): 456-489. DOI: https://doi.org/10.1177/0049124115585362.
[11] Paiva, T. and J. P. Reiter. (2017). Stop or Continue Data Collection: A Nonignorable Missing Data Approach for Continuous Variables. Journal of Official Statistics 33 (3): 579–599. DOI: https://doi.org/10.1515/jos-2017-0028.
[12] Lewis, T. (2017). Univariate Tests for Phase Capacity: Tools for Identifying When to Modify a Survey’s Data Collection Protocol. Journal of Official Statistics 33 (3): 601–624. DOI: https://doi.org/10.1515/jos-2017-0029.
[13] Peytchev, A. (2010). Responsive design in telephone survey data collection. International Workshop on Household Survey Nonresponse, Nürnberg, Germany, August 30.
[14] Plewis, I., and N. Shlomo. (2017). Using Response Propensity Models to Improve the Quality of Response Data in Longitudinal Studies. Journal of Official Statistics 33 (3): 753–779. DOI: https://doi.org/10.1515/jos-2017-0035.
[15] Särndal, C. and P. Lundquist. (2017). Inconsistent Regression and Nonresponse Bias: Exploring Their Relationship as a Function of Response Imbalance. Journal of Official Statistics 33 (3): 709–734. DOI: https://doi.org/10.1515/jos-2017-0033.
[16] Schouten, B., A. Peytchev, and J. Wagner. (2017). Adaptive Survey Design. Series on Statistics Handbooks. Chapman and Hall/CRC. Available at: https://www.routledge.com/Adaptive-Survey-Design/Schouten-Peytchev-Wagner/p/book/9780367735982.
[17] Schouten, B., J. van den Brakel, B. Buelens, J. van der Laan, and T. Klausch. (2013). Disentangling mode-specific selection and measurement bias in social surveys. Social Science Research 42: 1555–1570. Available at: https://www.cbs.nl/-/media/imported/documents/2012/32/2012-11-x10-pub.pdf.
[18] Schouten, B., M. Calinescu, and A. Luiten. (2013). Optimizing quality of response through adaptive survey designs. Survey Methodology 39 (1): 29–58. Available at: https://www.cbs.nl/-/media/imported/documents/2011/23/2011-x10-18.pdf.
[19] Schouten, B., N. Shlomo, and C. Skinner. (2011). Indicators for monitoring and improving representativeness of response. Journal of Official Statistics 27 (2): 231–253. Available at: https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/indicators-for-monitoring-and-improving-representativeness-of-response.pdf.
[20] Tourangeau, R., M. Brick, S. Lohr, and J. Li. (2017). Adaptive and responsive survey designs: a review and assessment. Journal of the Royal Statistical Society, Series A, 180 (1): 203–223. DOI: https://doi.org/10.1111/rssa.12186.
[21] Wagner, J. (2008). Adaptive Survey Design to Reduce Nonresponse Bias. PhD thesis, University of Michigan, Ann Arbor, USA. Available at: https://deepblue.lib.umich.edu/bitstream/handle/2027.42/60831/jameswag_1.pdf?sequence=1&isAllowed=y.
[22] Wagner, J. (2013). Adaptive contact strategies in telephone and face-to-face surveys. Survey Research Methods 7 (1): 45–55. DOI: https://doi.org/10.18148/srm/2013.v7i1.5037.
Cite This Article
  • APA Style

    Kees van Berkel. (2022). Adaptive Survey Design for the Dutch Labour Force Survey. American Journal of Theoretical and Applied Statistics, 11(4), 114-121. https://doi.org/10.11648/j.ajtas.20221104.12

    Copy | Download

    ACS Style

    Kees van Berkel. Adaptive Survey Design for the Dutch Labour Force Survey. Am. J. Theor. Appl. Stat. 2022, 11(4), 114-121. doi: 10.11648/j.ajtas.20221104.12

    Copy | Download

    AMA Style

    Kees van Berkel. Adaptive Survey Design for the Dutch Labour Force Survey. Am J Theor Appl Stat. 2022;11(4):114-121. doi: 10.11648/j.ajtas.20221104.12

    Copy | Download

  • @article{10.11648/j.ajtas.20221104.12,
      author = {Kees van Berkel},
      title = {Adaptive Survey Design for the Dutch Labour Force Survey},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {11},
      number = {4},
      pages = {114-121},
      doi = {10.11648/j.ajtas.20221104.12},
      url = {https://doi.org/10.11648/j.ajtas.20221104.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20221104.12},
      abstract = {A challenge for the National Statistical Institutes is to produce reliable statistics with a limited budget for data collection. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done on the impact on quality and costs of reducing the use of interviewers in mixed-mode surveys that start with Internet observation, followed by telephone or face-to-face observation of Internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. For this purpose, a methodology has been developed in this paper. The methodology is applied in the development of an adaptive survey design for the Dutch Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and some response and survey results.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Adaptive Survey Design for the Dutch Labour Force Survey
    AU  - Kees van Berkel
    Y1  - 2022/08/31
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ajtas.20221104.12
    DO  - 10.11648/j.ajtas.20221104.12
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 114
    EP  - 121
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20221104.12
    AB  - A challenge for the National Statistical Institutes is to produce reliable statistics with a limited budget for data collection. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done on the impact on quality and costs of reducing the use of interviewers in mixed-mode surveys that start with Internet observation, followed by telephone or face-to-face observation of Internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. For this purpose, a methodology has been developed in this paper. The methodology is applied in the development of an adaptive survey design for the Dutch Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and some response and survey results.
    VL  - 11
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Statistics Netherlands, Division of Data Services, Research and Innovation, Heerlen, the Netherlands

  • Sections