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

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
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