IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v185y2022i3p851-871.html
   My bibliography  Save this article

Representativeness in six waves of CROss‐National Online Survey (CRONOS) panel

Author

Listed:
  • Olga Maslovskaya
  • Peter Lugtig

Abstract

Driven by innovations in the digital space, surveys have started to move towards online data collection across the world. However, evidence is needed to demonstrate that online data collection strategy will produce reliable data which could be confidently used to inform policy decisions. This issue is even more pertinent in cross‐national surveys, where the comparability of data is of the utmost importance. Due to differences in internet coverage and willingness to participate in online surveys across Europe, there is a risk that any strategy to move existing surveys online will introduce differential coverage and nonresponse bias. This paper explores representativeness across waves in the first cross‐national online probability‐based panel (CRONOS) by employing R‐indicators that summarize the representativeness of the data across a range of variables. The analysis allows comparison of the results over time and across three countries (Estonia, Great Britain and Slovenia). The results suggest that there are differences in representativeness over time in each country and across countries. Those with lower levels of education and those who are in the oldest age category contribute more to the lack of representativeness in the three countries. However, the representativeness of CRONOS panel does not become worse when compared to the regular face‐to‐face interviewing conducted in the European Social Survey (ESS).

Suggested Citation

  • Olga Maslovskaya & Peter Lugtig, 2022. "Representativeness in six waves of CROss‐National Online Survey (CRONOS) panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 851-871, July.
  • Handle: RePEc:bla:jorssa:v:185:y:2022:i:3:p:851-871
    DOI: 10.1111/rssa.12801
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12801
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rssa.12801?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jamie C. Moore & Gabriele B. Durrant & Peter W. F. Smith, 2018. "Data set representativeness during data collection in three UK social surveys: generalizability and the effects of auxiliary covariate choice," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 229-248, January.
    2. Shlomo, Natalie & Skinner, Chris J. & Schouten, Barry, 2012. "Estimation of an indicator of the representativeness of survey response," LSE Research Online Documents on Economics 39124, London School of Economics and Political Science, LSE Library.
    3. Schouten, Barry & Shlomo, Natalie & Skinner, Chris J., 2011. "Indicators for monitoring and improving representativeness of response," LSE Research Online Documents on Economics 39121, London School of Economics and Political Science, LSE Library.
    4. Henk Roose & Hans Waege & Filip Agneessens, 2003. "Respondent Related Correlates of Response Behaviour in Audience Research," Quality & Quantity: International Journal of Methodology, Springer, vol. 37(4), pages 411-434, November.
    Full references (including those not matched with items on IDEAS)

    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. Roberts Caroline & Vandenplas Caroline & Herzing Jessica M.E., 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.
    2. Dan Hedlin, 2020. "Is there a 'safe area' where the nonresponse rate has only a modest effect on bias despite non‐ignorable nonresponse?," International Statistical Review, International Statistical Institute, vol. 88(3), pages 642-657, December.
    3. 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.
    4. Barry Schouten & Fannie Cobben & Peter Lundquist & James Wagner, 2016. "Does more balanced survey response imply less non-response bias?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 727-748, June.
    5. Barry Schouten & Natalie Shlomo, 2017. "Selecting Adaptive Survey Design Strata with Partial R-indicators," International Statistical Review, International Statistical Institute, vol. 85(1), pages 143-163, April.
    6. Thais Paiva & Jerry Reiter, 2014. "Using Imputation Techniques To Evaluate Stopping Rules In Adaptive Survey Design," Working Papers 14-40, Center for Economic Studies, U.S. Census Bureau.
    7. Jamie C. Moore & Peter W. F. Smith & Gabriele B. Durrant, 2018. "Correlates of record linkage and estimating risks of non‐linkage biases in business data sets," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1211-1230, October.
    8. Carl-Erik Särndal & Imbi Traat & Kaur Lumiste, 2018. "Interaction Between Data Collection And Estimation Phases In Surveys With Nonresponse," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 183-200, June.
    9. Patrick Gleiser & Joseph W. Sakshaug & Marieke Volkert & Peter Ellguth & Susanne Kohaut & Iris Möller, 2022. "Introducing Web in a mixed‐mode establishment survey: Effects on nonresponse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 891-915, July.
    10. Tobias Gummer & Bella Struminskaya, 2023. "Early and Late Participation during the Field Period: Response Timing in a Mixed-Mode Probability-Based Panel Survey," Sociological Methods & Research, , vol. 52(2), pages 909-932, May.
    11. 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.
    12. van Berkel Kees & van der Doef Suzanne & Schouten Barry, 2020. "Implementing Adaptive Survey Design with an Application to the Dutch Health Survey," Journal of Official Statistics, Sciendo, vol. 36(3), pages 609-629, September.
    13. Stephanie Coffey, PhD. & Jaya Damineni & John Eltinge, PhD. & Anup Mathur, PhD. & Kayla Varela & Allison Zotti, 2023. "Some Open Questions on Multiple-Source Extensions of Adaptive-Survey Design Concepts and Methods," Working Papers 23-03, Center for Economic Studies, U.S. Census Bureau.
    14. Li-Chun Zhang & Ib Thomsen & Øyvin Kleven, 2013. "On the Use of Auxiliary and Paradata for Dealing With Non-sampling Errors in Household Surveys," International Statistical Review, International Statistical Institute, vol. 81(2), pages 270-288, August.
    15. Henk Roose & John Lievens & Hans Waege, 2007. "The Joint Effect of Topic Interest and Follow-Up Procedures on the Response in a Mail Questionnaire," Sociological Methods & Research, , vol. 35(3), pages 410-428, February.
    16. Lundquist Peter & Särndal Carl-Erik, 2013. "Aspects of Responsive Design with Applications to the Swedish Living Conditions Survey," Journal of Official Statistics, Sciendo, vol. 29(4), pages 557-582, December.
    17. Aneta Chmielewska & Małgorzata Renigier-Biłozor & Artur Janowski, 2022. "Representative Residential Property Model—Soft Computing Solution," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
    18. Silvia Biffignandi & Alessandro Zeli, 2021. "Longitudinal business data construction and quality: Two different approaches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(2), pages 92-114, May.
    19. Särndal Carl-Erik & Traat Imbi & Lumiste Kaur, 2018. "Interaction Between Data Collection And Estimation Phases In Surveys With Nonresponse," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 183-200, June.
    20. Earp Morgan & Toth Daniell & Phipps Polly & Oslund Charlotte, 2018. "Assessing Nonresponse in a Longitudinal Establishment Survey Using Regression Trees," Journal of Official Statistics, Sciendo, vol. 34(2), pages 463-481, June.

    More about this item

    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:bla:jorssa:v:185:y:2022:i:3:p:851-871. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.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.