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This practical online workshop will introduce good practice for writing efficient, reproducible and robust R code when working with large administrative datasets in the ONS Secure Research Service (SRS).
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This course will provide an introduction to data quality, and how it can affect all aspects of working with administrative data. The course will cover data quality dimensions which include technical and social aspects.
Join this in-person course for a practical introduction to the Educational and Child Health Insights from Linked Data (ECHILD) dataset.
Join the ADR England Community Catalyst: Children at Risk of Poor Outcomes group for their next webinar, focusing on evaluation using administrative data. This session will discuss Born in Bradford's Better Start study for the evaluation of early years support.
Join the UCL Centre for Education Policy and Equalising Opportunities (CEPEO) for a series of short online modules exploring how administrative data can be used for rigorous policy evaluation in UK education and youth transitions.
In this Education and Child Health Insights from Linked Data (ECHILD) seminar, hear from experts who have used the Individualised Learner Record (ILR) for their research.
This event launches the Crime and Justice Administrative Data Network (CJADN). The CJADN is a cross-institutional, cross-regional network open to anyone who uses, wants to use, or wants to learn from crime-related administrative data. The event aims to bring together academics, third-sector organisations, practitioners, and data owners to share knowledge and research using crime-related administrative data.
Join this in-person course delivered by ADR Scotland to learn about the importance of data quality when working with administrative data.
Join the ADR England Community Catalyst: Children at Risk of Poor Outcomes team for their next webinar, focusing on evaluation using administrative data.
Join this seminar to explore the hidden realities of the ‘patchwork economy’, where multiple jobholding and underemployment are often overlooked or misrepresented in official data.
