Analytics & Insights Conference
Yoshimi* Battles the Survey Bots
How you can work to defeat those evil-natured robots in your online survey samples
Leyla Eden, Daniel Barkley, Trevor Olsen
Advances in automation technology and artificial intelligence have made it easier to create and deploy bots for various purposes, including survey participation. As AI technology becomes more sophisticated, survey bots can become more intelligent and difficult to detect. This can exacerbate the challenges associated with identifying and preventing bots from participating in surveys, which is a concern for the market research industry. Consequently, market researchers need to continuously adapt and develop more robust methods to distinguish between genuine survey participants and automated bots. However, AI bots also have weaknesses (so far) and we at Numerious developed a new approach that is designed to exploit their weaknesses.
Using Seeded Items to Improve Express Best Worst Designs
Jon Godin, Megan Peitz and Tom Eagle (Eagle Analytics)
Previous research on many-item Best Worst tasks has shown that Sparse BW designs have generally outperformed Express BW designs, especially regarding out-of-sample predictions. Recently, a suggestion was made to improve Express BW designs by including a small, fixed number of items [3-5] across all respondents. We undertake this research to ascertain whether item seeding results in better out-of-sample predictions than traditional Express BW designs have without this seeding.
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