Quality Assurance of Sensor Data in Survey Research |
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| Coordinator 1 | Dr Fiona Draxler (University of Mannheim) |
| Coordinator 2 | Dr Vanessa Lux (GESIS) |
| Coordinator 3 | Dr Yannik Peters (GESIS) |
As sensor data becomes increasingly integrated into survey research, ensuring its quality and reliability is critical. Sensor data, often collected through wearable devices, mobile apps, and environmental sensors, comes with the promise of highly granular and nonintrusive data collection in everyday life. However, due to its dynamic and context-dependent nature, the use of sensor data entails unique challenges to data quality. To harvest its potential for survey research, we need standards, frameworks, and tools to manage these challenges. The session aims to provide a thorough understanding of the methodological challenges and practical solutions in ensuring the quality of this new data type in the context of survey research. We invite contributions which discuss any aspect of quality assurance of sensor data when integrated into survey research. We specifically encourage contributions which explore comprehensive frameworks, cutting-edge tools, and best practices designed to maintain the integrity and usability of sensor data at all stages of the data life cycle. Contributions may cover but are not limited to:
• Standards and Frameworks for Quality Assurance: Contributions that focus on the applicability of new and existing data quality standards and frameworks to assess sensor data, emphasizing criteria for evaluating reliability, validity, and representativeness in diverse sensor data applications.
• Tools and Platforms for Data Validation: Contributions that present individual tools and technologies that evaluate sensor data quality, both automated and manual evaluation techniques, the use of machine learning and AI-driven approaches, as well as open-source platforms (e.g., KODAQS toolbox).
• Best Practices and Case Studies: Contributions that showcase best practice examples and case studies that demonstrate the successful integration of sensor data into survey research yet address the specific challenges encountered and the solutions applied.
• Didactics of Data Quality Issues: Contributions that discuss approaches to teaching and promoting data quality assurance