IES launches new Trials Unit to help organisations test and apply evidence of ‘what works’ in public policy and workplace practice
26 Apr 2022
The Institute for Employment Studies (IES) is pleased to announce the launch of a new Trials Unit, dedicated to supporting organisations to test and evaluate new approaches and apply evidence of what works.
Led by Dr Anneka Dawson, the Trials Unit is staffed by IES personnel from multiple teams spanning employment, education and human resource policy and practice, bringing together expertise across the Institute from multiple research perspectives and subject disciplines.
Commenting on the launch, Dr Dawson said: “This Unit brings together multiple strands of IES research methodology into a single cohesive group, where experience and expertise can be shared responsively, getting to the heart of intervention evaluation with pace and enabling the highest standard of delivery.”
Becci Newton, IES Director of Public Policy Research, said: “IES has many years of experience in conducting and managing a wide variety of research trials for a diverse range of clients.
“The launch of the Trials Unit is an opportunity to build a community of practice underpinning our mission of bringing about sustainable improvements to employment policy and practice through evidence-based approaches.
“IES covers employment and education policy, in addition to employer practices and workforce matters so we bring a unique combination. Our team devises the most appropriate and robust designs for clients who are seeking to test what works and crucially why, to deliver high quality, evidence-based analysis.”
The work of the Trials Unit will focus on providing support to develop, test and trial interventions and to then robustly measure their impact. It will build on a long track record of work within IES on trial support and evaluation – including with What Works Centres, national government Departments and major employers – and will draw on a range of techniques including randomised control trials (RCTs) and quasi-experimental methods using techniques including Regression Discontinuity Designs, Propensity Score Matching and Difference in Differences.