Call for Abstracts

Date:  April 23–24, 2026

Location: GESIS – Leibniz Institute for the Social Sciences, Mannheim, Germany

Assessing and mitigating measurement error in surveys remains a key challenge for ensuring the validity of empirical findings. Even well-designed surveys can suffer from measurement error due to question misinterpretation, recall issues, social desirability bias, satisficing, or interviewer effects. If left unaddressed, measurement error can bias estimates and undermine substantial conclusions. Despite established indicators and ongoing conceptual work, workflows and practical tools for detecting and evaluating measurement error in applied social research remain limited. At the same time, new data forms (e.g., digital traces, geolocation, biomarkers) are increasingly used in combination with surveys, enriching measurement but also introducing new sources of measurement error, including linkage and validity issues, platform-driven noise, and temporal misalignment or processing problems. Addressing these challenges requires a deeper understanding of error sources and the development of practical tools for their assessment.  

The KODAQS workshop on Measurement Error in Surveys and Integration of New Data Forms aims to bring together survey researchers and data quality experts to exchange and develop innovative methods and tools for assessing and mitigating measurement error in survey data and linked datasets. It is organized by the Competence Center for Data Quality in the Social Sciences (KODAQS), a cooperation between GESIS, the University of Mannheim, and the LMU Munich, which focuses on improving data quality in the social sciences. We invite conceptual, methodological, and applied contributions from the social and behavioral sciences on indicators, tools, and methods for the assessment or mitigation of measurement error in surveys and linked datasets, including contributions that explore AI-based approaches. Possible topics include but are not limited to:

• Tools, indicators, and diagnostics for identifying and quantifying measurement error in surveys
• Methodological innovations for assessing or reducing error when integrating surveys with other data types
• Conceptual or empirical work on error structures in new data types
• Open-source tools, software, or innovative frameworks for measurement error assessment
• AI-assisted methods and workflows for measurement-quality assessment are encouraged

Keynotes will be delivered by Dr. Caroline Roberts (University of Lausanne), Prof. Dr. Florian Keusch (University of Mannheim), PD Dr. Daniel Seddig (Criminological Research Institute of Lower Saxony, KFN), and Asst. Prof. Dr. Henning Silber (University of Michigan).  

Abstract (max. 300 words) should briefly outline the tool, indicator, or methodological approach used for measurement error assessment or mitigation, its application, and the insights gained. We also encourage the submission of work in progress. Please submit your abstract to Fabienne Kraemer (fabienne.kraemer(at)gesis.org) no later than March 1st, 2026. Participation in the workshop is free of charge. KODAQS will cover travel and accommodation costs up to 400 Euro per participant.