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

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.