ESRA 2027 sessions by theme
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Harmonizing survey data. Challenges, methods, and opportunities |
| Coordinator 1 | Dr Marta Kołczyńska (University of Warsaw) |
| Coordinator 2 | Professor Sylvia Kritzinger (University of Vienna) |
| Coordinator 3 | Professor Jennifer Oser (Ben-Gurion University of the Negev) |
Session Details
As the number and diversity of survey data sources have expanded in recent years, data harmonization has become increasingly important across the social sciences. Broadly understood as the process of transforming data from multiple sources into comparable forms for analysis, harmonization creates new opportunities for comparative, cumulative, and interdisciplinary research. At the same time, it raises important methodological and conceptual challenges concerning measurement, comparability, documentation, and the interpretation of harmonized data.
Despite the growing use of harmonized datasets, the principles, standards, and best practices guiding harmonization efforts remain under development. Decisions about how concepts are defined, variables are aligned, samples are made comparable, and differences across surveys are addressed can have important consequences for the conclusions drawn from research.
This session aims to contribute to interdisciplinary discussions on advancing survey data harmonization by bringing together conceptual, methodological, and empirical perspectives. We invite contributions that examine the challenges and opportunities of creating, evaluating, and using harmonized survey data, as well as research that reflects on the broader implications of harmonization for survey research.
Possible topics include, but are not limited to:
• Conceptual approaches and frameworks for survey data harmonization;
• Harmonization of survey measures, instruments, and response categories;
• Challenges of cross-national, cross-cultural, and cross-temporal survey harmonization;
• Representation, sampling, and population comparability across surveys;
• Ex ante and ex post harmonization strategies and their implications;
• Statistical, computational, and AI-assisted approaches to harmonizing and analyzing survey data;
• Documentation, metadata, data infrastructures, and standards for harmonized datasets;
• Assessing uncertainty, quality, and robustness in harmonization decisions;
• Ethical and epistemological questions surrounding comparability and standardization;
• Harmonization of survey data with other data sources, including administrative and digital data;
• Lessons from harmonization projects for future survey design and data collection initiatives;
• Substantive research enabled by the harmonization of survey datasets.
