Measurement errors in smart surveys: Accomplishments, challenges, and looking forward |
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| Coordinator 1 | Dr Jonas Klingwort (Statistics Netherlands (CBS) - Department of Research & Development) |
| Coordinator 2 | Dr Vera Toepoel (Statistics Netherlands (CBS) - Department of Research & Development) |
| Coordinator 3 | Professor Peter Lugtig (Utrecht University - Methodology and Statistics) |
Integrating digital technologies into survey research transforms the survey landscape and leads to the development of smart surveys, which have at least one of the following features: internal/external sensors, open/public online data, personal online data, or linkage consent. This session is dedicated to accomplishments in smart surveys, focusing on using sensors, mobile apps, and data donation. These innovations offer opportunities to improve data quality, reduce respondent burden, and capture real-time behavior. However, they also bring challenges, particularly concerning measurement errors. This session focuses on the accomplishments and challenges associated with these technologies, emphasizing understanding, quantifying, and mitigating measurement errors.
Sensors in smartphones, wearables, and connected devices collect passive data on physical activity, environmental conditions, or physiological responses. Despite their potential, sensors introduce measurement errors due to calibration issues, data loss, and variability in sensor sensitivity. This session will discuss how researchers address these errors to ensure reliable and valid data collection.
Mobile apps have emerged as tools to engage survey participation and enable fine-granular, real-time data collection. However, apps can also introduce measurement errors, including biases related to device compatibility, app design, and respondent behavior. We want to discuss how these factors impact data quality and what strategies are being implemented to reduce measurement error in app-based survey research.
Data donation, where participants voluntarily share digital traces---such as social media activity, browsing history, or transactional data---offers an opportunity to gain insights into behaviors and attitudes. However, this approach suffers from self-selection bias, incomplete data, and varying levels of data accuracy. We want to address how these measurement errors can be mitigated through careful study design and respondent recruitment strategies.
We invite researchers, practitioners, and technologists to discuss harnessing these innovations while highlighting the complexities they introduce to the survey research landscape.