A comparison of four probability-based online and mixed-mode panels in Europe

Annelies G. Blom, Michael Bosnjak, Anne Cornilleau, Anne-Sophie Cousteaux, J.W.M. Das, S. Douhou, Ulrich Krieger

Research output: Contribution to journalArticleScientificpeer-review

63 Citations (Scopus)


Inferential statistics teach us that we need a random probability sample to infer from a sample to the general population. In online survey research, however, volunteer access panels, in which respondents self-select themselves into the sample, dominate the landscape. Such panels are attractive due to their low costs. Nevertheless, recent years have seen increasing numbers of debates about the quality, in particular about errors in the representativeness and measurement, of such panels. In this article, we describe four probability-based online and mixed-mode panels for the general population, namely, the Longitudinal Internet Studies for the Social Sciences (LISS) Panel in the Netherlands, the German Internet Panel (GIP) and the GESIS Panel in Germany, and the Longitudinal Study by Internet for the Social Sciences (ELIPSS) Panel in France. We compare them in terms of sampling strategies, offline recruitment procedures, and panel characteristics. Our aim is to provide an overview to the scientific community of the availability of such data sources to demonstrate the
potential strategies for recruiting and maintaining probability-based online panels to practitioners and to direct analysts of the comparative data collected across these panels to methodological differences that may affect comparative estimates.
Original languageEnglish
Pages (from-to)8-25
Number of pages18
JournalSocial Science Computer Review
Issue number1
Publication statusPublished - Feb 2016
Externally publishedYes


  • probability-based samples
  • online panels
  • offline recruitment
  • offline respondents
  • longitudinal surveys


Dive into the research topics of 'A comparison of four probability-based online and mixed-mode panels in Europe'. Together they form a unique fingerprint.

Cite this