ESRA 2027 sessions by theme

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Session Details

Many survey programmes divide responsibility among central coordinators and national, regional, institutional or local teams. Central teams hold detailed knowledge of common definitions, processing conventions and comparative requirements, while local teams understand their questionnaires, source data, fieldwork processes and institutional settings. Effective quality assurance depends on combining these forms of expertise rather than assuming that either is sufficient on its own.
This session examines how survey programmes with multiple sites of data collection organise responsibility for data preparation, harmonisation, validation and release. We invite contributions on parallel central and local processing pipelines, independent reproduction of indicators, automated checks of data and metadata, including machine-learning-based anomaly detection, discrepancy analysis, shared processing tools, and procedures for resolving differences between centrally and locally prepared results.
Contributions may also consider the infrastructure through which common standards are implemented, including training, data-delivery workshops, technical support and feedback between central and local teams. Scientific-use files and other harmonised research datasets are relevant where their construction or secondary use provides an additional test of documentation, reproducibility and data quality.
Of particular interest are studies showing which problems can be standardised or detected automatically, which require contextual knowledge, and how user feedback can improve conventions, metadata, software and subsequent survey rounds. Contributions may also examine the costs, duplication and governance implications of parallel processing.
The session welcomes work from cross-national surveys, official-statistics networks, international assessments and distributed studies coordinated across regions, cities, universities, schools, or other semi-autonomous sites.