Implementation of Replication Studies
Identifying evidence-based interventions and policies that make a difference in student outcomes is critical for improving individuals’ life trajectories and reducing inequality in the United States. Despite increased support for education researchers to conduct high quality evaluations to identify effective interventions, many researchers lack the capacity to collect, process, and analyze large volumes of data. The Collaboratory Replication Lab addresses these concerns by providing education researchers with tools and resources for developing, piloting, and testing data infrastructure systems to conduct high quality, systematic research in school settings.
Natural Language Processing for Implementation Research
Led by Kylie L Anglin and Brian Wright.
Low-cost, scalable methods of monitoring implementation in field settings using natural language processing
Data Engineering and Management for Collecting High Quality Data.
Led by Anandita Krishnamachari, Alexis Prijoles, and Brian Wright.
Data Science principles and practices that promote transparency and replicability in social and behavioral science research.
Research Workflow and Systems Organization.
Led by Christina Taylor and Alexis Prijoles.
Systematic organizations of resources that work interdependently to complete research activities and obtain project goals with high fidelity across sites.
