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What is the Quasi-Experimental Evaluation Network?
The Quasi Experimental Evaluation Network links research departments across the UK and internationally to improve the use of quasi-experimental methods for evaluation.
The Network will meet the growing demand for efficient evaluation of interventions to understand what works to improve health and wellbeing.
The aim of the Network is to enhance the use of quasi-experimental methods to evaluate policy, service and clinical interventions, in order to improve health and social outcomes. This includes:
Who is involved?
The Quasi-Experimental Evaluation Network links researchers at the Bradford Institute for Health Research, University of Leeds, University of Liverpool, University of Manchester, University of York and University of Sheffield. Our partners have expertise in methods and disciplines that are key to quasi-experimental evaluation, including routine data linkage, statistics and causal inference.
The Network will also make use of the growing infrastructure and opportunities for quasi-experimental evaluation throughout the north of England, including data linkage through Connected Health Cities, use of electronic health data through the Farr Institute and Health Data Research Institute.
Why a Quasi-Experimental Evaluation Network?
There is an urgent need for more and better evidence on what works to improve health and wellbeing. Randomised controlled trials provide gold standard evidence, but are costly and often outpaced by the speed of development of real life policy and practice innovation.
Evaluation of real-world policy and practice changes using quasi experimental approaches presents an opportunity to add to the evidence base in a timely, rigorous and efficient manner. Methods such as regression discontinuity, differences in differences, time series analyses and propensity score matching are increasingly used in evaluations. The Quasi-Experimental Evaluation Network links researchers to coordinate efforts, share good practice and apply quasi-experimental approaches to improve the evidence base.
For further information about the Network, contact Tiffany Yang