Battling bots and bad data: enhancing data quality in online surveys
Abstract
Researchers have increasingly pivoted from paper and phone to online survey delivery. However, bots and inattentive participants can lower data quality dramatically compared with in-person delivery. In this paper, we demonstrate to the reader the effectiveness of measures used to screen for bots and inattentive participants. Using our own survey that incorporates current best practices, we illustrate that these measures aided us in detecting both bots and inattentive participants; however, our attention checks eliminated more inattentive respondents than our bot detection questions. Thus, we show that including checks for bots and reader attention is crucial to implementing online surveys and generating high-quality data.
Fields of Study & Subjects
Survey Methodology and Nonresponse
Data-Driven Disease Surveillance
Mobile Crowdsensing and Crowdsourcing
Publication details
- Journal / source
- Quality & Quantity
- Publisher
- Springer Science and Business Media LLC
- Volume
- 60
- Issue
- 4
- Pages
- 14003–14018
- Publication date
- 11 April 2026
- Language
- EN