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Web Survey Bibliography

Title Professional Respondents in Internet Panels: Who are They and What Do They Do to Our Data?
Author de Leeuw, E. D., Matthijsse, S.
Year 2012
Access date 30.08.2012

Online panels are at present one of fastest growing data collection modes for market and opinion research (AAPOR report on online panels). With the use of online panels, concern grew about the recruitment of panel members, and especially the emergence of large nonprobability panels. The combination of self-selection and incentives, both important characteristics of non-probability based internet surveys, and the increased use of such non-probability based internet panels, lead to an increased fear of the emergence of ‘professional respondents’ (e.g. Comley, 2005) and the negative consequences of this for data quality. So far, there are very few empirical studies into this topic. The goal of this study is (1) to investigate whether ‘professional’ respondents can be distinguished in online panels using latent class analysis, (2) describe who the professional are and provide a demographical and psychographical profile, and (3) investigate whether there is a difference in the quality of the data provided by these ‘professional’ respondents and the other respondents in the panel. In our study, we analyzed a unique data set of the NOPVO (Nederlands Online Panel Vergelijkings Onderzoek; Vonk et al, 2006) that includes 19 large Dutch online panels, which together capture 90% of the respondents to online market research in the Netherlands. A latent class analysis showed that four types of respondents can be distinguished, ranging from the ‘professional’ respondent to the ‘altruistic-voluntary’ respondent. Also, different respondent types can be clearly characterized using demographical and psychographical variables. The ultimate question is whether the existence of professional respondents is a threat to the data quality of internet panels. Do professional respondents satisfice more, do they take more short-cuts? Indeed, small differences in data quality can be detected between the groups. However, these differences disappear when controlling for socio-demographic variables.

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Year of publication2012
Bibliographic typeConferences, workshops, tutorials, presentations

Web survey bibliography - De Leeuw, E. D. (27)