Quality Assurance in the Linkage of Survey Data: Frameworks, Tools, and Best Practices |
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| Coordinator 1 | Dr Jessica Daikeler (GESIS- Leibniz Institute for the Social Sciences ) |
| Coordinator 2 | Anne Stroppe (GESIS- Leibniz Institute for the Social Sciences ) |
| Coordinator 3 | Laura Young (University of Mannheim) |
As survey research increasingly incorporates diverse data sources, ensuring the quality and reliability of linked survey data is essential. Linked survey data, often combining traditional survey responses with external data sources like administrative records, sensor data, or social media data presents unique challenges due to the complexity of integration and the potential for discrepancies. These challenges in the linkage process necessitate robust frameworks and tools to manage, validate, and enhance data quality.
This session will focus on the key aspects of quality assurance in the collection and utilization of linked survey data. We will explore comprehensive frameworks, cutting-edge tools, and best practices specifically designed to maintain the integrity and usability of data from multiple sources when linked to survey responses. Key topics will include:
1. Frameworks for Quality Assurance: An overview of frameworks developed to assess the quality of data linkage.
2. Tools and Platforms for Data Validation: A discussion on tools and technologies aimed at validating the quality of linked survey data and the linkage process itself. This will include both automated and manual validation techniques and open-source platforms tailored to linked data validation, such as the KODAQS toolbox.
3. Best Practices and Case Studies: Guidelines for the collection and processing of linked survey data, focusing on strategies to assess and improve data quality during the linkage. Real-world case studies will demonstrate successful methods for linking external data sources with survey responses, addressing the specific challenges encountered and the solutions applied.
4. Didactics of Data Quality Issues: Approaches to teaching and promoting data quality assurance for linked survey data. This section will explore educational strategies to equip researchers and practitioners with the necessary skills to effectively tackle data quality issues.