Our Support

Our work is supported through a number of sources, including grants to promote methodology development as well as grants to conduct original study evaluations and systematic replications. Our work has also received generous support from internal University of Virginia resources.

Support for Methodological Research

Institute of Education Sciences (2019-2022). Developing Methodological Foundations for Replication Sciences. Wong (PI), Steiner (co-PI), #R305D190043.

National Science Foundation (2015-20). Collaborative Research: Developing Methodological Foundations for Empirical Evaluations of Non-Experimental Methods in STEM Intervention Evaluations. Wong (PI), Steiner (co-PI). #GA11158

Support for Systematic Replication Studies with Partners

Institute of Education Sciences (2019-2022), Developing Infrastructure and Procedures for the Special Education Research Accelerator. Cook (PI), Therrien (co-PI), Wong (co-PI).

Institute of Education Sciences (2020-2025), Iterative Replication of Read Well in First Grade. Solari (PI), Wong (co-PI), Baker (co-PI), Richards-Tutor (co-PI)

National Science Foundation (2020-2024), Collaborative Research: Leveraging Simulations in Preservice Preparation to Improve Mathematics Teaching for Students with Disabilities. Cohen (PI), Jones (PI), Berry (co-PI), Wong (co-PI).

Robertson Foundation (2020-2022). Simulations in Teacher Education: Systematic Replication Research to Improve the Preparation of Teachers. Cohen (PI), Wong (co-PI).

Support from the University of Virginia

Jefferson Trust Foundation (2017-2018), Pilot Evaluation of Using Simulated Environments for Improving Teacher Preparation, Cohen (PI), Wong (Co-PI)

UVA Bankard Fund for Political Economy (2017-2018), Identifying and Mitigating the Role of Racial Implicit Bias in Teacher Preparation Role, Cohen (PI), Wong (Co-PI)

Curry IDEAS Fund

  • A Pilot Evaluation for Mitigating Racial Implicit Bias among Pre-Service Teachers

    Role

  • Meta-analysis of Empirical Evaluation of Non-Experimental Methods

  • Building Tools to Support High Quality, Systematic Replication Studies

School of Data Science Capstone Teams (2020-2021).

  • Natural Language Text Process for Assessing Implementation Fidelity

  • Data Engineering for Collecting High Quality Data for Conducting Systematic Replication Studies