This is a collaborative project involving Ohio State University (lead institution), Pennsylvania State University, American Institutes for Research, University of Illinois-Urbana, and the University of Iowa. The project examines the impact of different research funding structures on the training of graduate students and postdoctoral fellows and the impact of their subsequent outcomes. The rationale for the study is the recognition that research teams are organized differently in composition, size, and reliance of graduate students versus postdoctoral fellows. In addition, funding agencies change the structure of science training by creating programs that ?encourage? interdisciplinary groups, multi-university collaborations, or large research centers that focus on specific research questions. However, little research has been done about how these factors shape the career preparation of STEM professionals.
The PIs will begin by examining the different contexts of research funding and training and then (1) relate measures of team structure to the structure of funding to determine the extent to which funding agency policies shape research teams and (2) capture the trajectories of the students and postdoctoral fellows during and after their contact with the teams to quantify how the structure of teams affects training. They will use longitudinal administrative data that to capture information about sources of funding for the training and support of Ph.D.s and Postdocs from 13 major research institutions that participate in the Committee on Institutional Cooperation. They will then identify the nature of the research training through a text analysis of pertinent documents and use a quasi-experimental design to estimate the causal impact of the structure of research and length of training on trainee outcomes.
The project will have broad implications for the entire field of STEM education policy and research. The underlying algorithms and tools will be made available to the academic research community and can be leveraged to link internal human resources data sets to external data sets. This new data infrastructure also will facilitate the assessment of the effects of research investments on research productivity as well as undergraduate and graduate curriculum development.
