United States

Online panels have become an important resource for research in political science, but the financial compensation involved incentivizes respondents to become “survey professionals”, which raises concerns about data quality. We provide evidence on survey professionalism using behavioral web browsing data from three U.S. samples, recruited via Lucid, YouGov, and Facebook (total n = 3,886). Survey professionalism is common but varies across samples: By our most conservative measure, we identify 1.7% of respondents on Facebook, 7.9% of respondents on YouGov, and 34.3% of respondents on Lucid as survey professionals. However, evidence that professionals lower data quality is limited: they do not systematically differ demographically or politically from non-professionals and do not respond more randomly—although they are somewhat more likely to speed, to straightline, and to take questionnaires repeatedly. While concerns are warranted, we conclude that survey professionals do not, by and large, distort inferences of research based on online panels.

A challenge for public opinion surveys is achieving representativeness of respondents across demographic groups. We test the extent to which ideological alignment with a survey’s sponsor shapes differential partisan response and users’ choice of whether to participate in a research study on Facebook. While the use of Facebook advertisements for recruitment has increased in recent years and offers potential benefits, it can yield difficulties in recruiting politically representative samples. We recruit respondents for a short survey through two otherwise identical advertisements associated with either New York University (from a liberal state) or the University of Mississippi (from a conservative state). Contrary to our expectations, we don’t find an asymmetry in completion rates between self-reported Democrats and Republicans based on the survey sponsor. Nor do we find statistically significant differences in attitudes of respondents across the two survey sponsors when we control for observables.