Revisiting the current state of online probability-based and opt-in survey samples

This event is co-organised with City, University of London and the European Social Survey
  • Event time:
    20th March 2024 17:00 – 18:00
  • Format:

In this webinar, Andrew Mercer will discuss the results of a Pew Research Center study designed to shed light on the current state of online probability-based and opt-in samples.

It compares the accuracy of six online surveys of U.S. adults – three from probability-based panels and three from opt-in sources.

This is the first such study to include samples from multiple probability-based panels, allowing for their side-by-side comparison.

The study was also designed to permit an in-depth comparison of accuracy not only for full-sample estimates, but for estimates within key demographic subgroups.

Consistent with previous studies, it found that probability-based samples generally yielded more accurate estimates.

More interestingly, it also found that on opt-in samples, especially large errors for 18-to-29- year-olds and Hispanic adults resulted from the presence of “bogus respondents” who make no effort to answer questions truthfully.

Mercer will discuss these and other findings from the report and its implications for the practice of online public opinion research.


  • Andrew Mercer
    Senior research methodologist Pew Research Center
    Andrew Mercer is a senior research methodologist at Pew Research Center. He is an expert on nonprobability survey methods, survey nonresponse and statistical analysis. His research focuses on methods of identifying and correcting bias in survey samples, as well as on the use of machine learning for survey data. Mercer leads the Center’s research on nonprobability samples and co-authored several reports and publications on the subject. He has also authored blog posts and analyses making methodological concepts such as oversampling accessible to a general audience. Prior to joining the Center, Mercer was a senior survey methodologist at Westat.