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Presentation Open Access

Improving the representativeness of non-probability samples: A case study of two web surveys

Ana Slavec

Web surveys, even for purposes of scientific data collection, are commonly based on non-probability samples as this saves costs and other resources. Unlike probability sampling procedures, non-probability sampling does not enable the generalisation of results from sample to the population. Since certain users are more likely to volunteer to participate, non-probability samples often have a certain selection bias. The representativeness of non-probability sampling designs can be improved with measures such as trying to spread the sample recruitment as broadly as possible by combining several recruitment channels. This contribution presents the case study of two web surveys in Slovenia that were based on large convenience samples, first on the topic of COVID-19 protective measures and the second on topic of COVID-19 vaccination. In both cases, we run a parallel survey where the same questionnaire was administered to members of an online market research panel that is representative of the Slovenian population. Based on the comparison of results of the two convenience samples to the respective panel samples we estimate how biased they are and discuss possible approaches to improve their representativeness.

The author acknowledges the support of the Slovenian Research Agency for funding the project using questionnaires to measure attitudes and behaviours of building users (Z5-1879), the European Commission for funding the InnoRenew project [Grant Agreement #739574] under the Horizon 2020 Widespread-Teaming program, and the Republic of Slovenia (investment funding from the Republic of Slovenia and the European Regional Development Fund).
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Publication date:
September 21, 2021
DOI:
10.5281/zenodo.5518984

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Keyword(s):
web surveys sampling representativness COVID-19 protective measures COVID-19 vaccination
Grants:
European Commission:
  • InnoRenew CoE - Renewable materials and healthy environments research and innovation centre of excellence (739574)
Meeting:
Applied Statistics 2021, Online, 20-22 September 2021 (Session Statistical applications II)
Communities:
License (for files):
Creative Commons Attribution 4.0 International

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