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Join the ADR England Community Catalyst: Children at Risk of Poor Outcomes team for their next webinar, focusing on evaluation using administrative data.
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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.
Join this online event to explore female offending trends in England and Australia through administrative data insights.
Join the ADR England Community Catalyst: Children at Risk of Poor Outcomes group for their next webinar, focusing on evaluation using administrative data.
Part of the Unlocking Youth Transitions Data series: This webinar will describe how we create and utilise Free School Meal (FSM) indicators and the Index of Multiple Deprivation (IMD) to measure socioeconomic background in LEO.
Join this in-person computer workshop to learn how to work with the Longitudinal Education Outcomes (LEO) dataset using R, delivered by ADR UK and the UCL Centre for Education Policy and Equalising Opportunities (CEPEO).
Part of the Unlocking Youth Transitions Data series: This webinar will draw on examples of administrative data studies that evaluate impacts from a raising of the participation age (RPA) and the post-16 education pathways taken by young people, to explain the PICO approach to specifying details of a policy evaluation.
Join an upcoming knowledge-sharing session on facilitating data access with the Project Accreditation Service for the Secure Research Service (PASS).
Part of the Unlocking Youth Transitions Data series: The session focuses on what researchers need to know before accessing data, including understanding its operational origins, identifying common limitations, and assessing feasibility using documentation and synthetic resources. It also covers the implications of TRE workflows, approval processes, and disclosure controls for research planning and project design.
We are inviting expressions of interest to take part in SynthSprint, a four-week data sprint exploring how synthetic data performs across different levels of fidelity and real-world use cases.
