New Insights into STEM Pathways: The Role of Peers, Networks, and Demand.
Effective Years: 2018-2022
The project is a collaborative effort involving researchers from New York University, Ohio State University, and University of Michigan Ann Arbor who are extending work on a previous project that links multiple data sets to conduct longitudinal analyses of STEM student outcomes. The current project leverages the research infrastructure that was supported by prior awards to examine how peer effects, network connections, and employer demand shape career outcomes for doctoral students. It integrates comprehensive administrative data for 113,000 doctoral students, UMETRICS research data, Proquest data on dissertations with information on fields of study and research, and restricted Census Bureau data on earnings and employment outcomes. Dara integration will occur under the umbrella of the Institute for Research on Innovation and Science (IRIS). The project will contribute new knowledge by articulating and testing key aspects of a novel conceptual model of STEM career choices. This theoretical and empirical work will establish basic facts about how demand and training conditions shape early STEM career choices that can be determine only through the integration of separate datasets. The project is supported by the Education and Human Resources Core Research Program, which funds fundamental research in STEM learning and learning environments, broadening participation in STEM, and STEM workforce development. The research design integrates three approaches - peer-based, network-based, and demand. In their peer-based approach, the researchers propose to take the composition of peers in entering graduate cohorts to be quasi-random and estimate how the composition of peers interacts with their own characteristics. A second set of analyses involves identifying labs from the data and estimating how the composition of the persons in a lab relate to outcomes. The network-based approach will be used to examine how individual differences in social capital shape doctoral student outcomes. The demand will answer the analytical question of the effect of actual and perceived labor market demand on the choice of careers. The analysis will begin with a description of the early career outcomes of students from undergraduate dropouts to college graduates, from master's students to Ph.D. dropouts to doctoral recipients. In addition to enhancing the infrastructure to conduct robust workforce development research, the project will provide a more comprehensive analysis of the role and impact of peer networks on STEM workforce development This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
17 Publications Related to This Project:
- Publish or Perish Selective Attrition as a Unifying Explanation for Patterns in Innovation Over the Career
- High-Impact and Transformative Science (Hits) Metrics: Definition, Exemplification, and Comparison
- Last Place? The Intersection of Ethnicity, Gender, and Race in Biomedical Authorship
- Data-Specific Functions: A Comment on Kindel ET Al.
- The Ripple Effects of Funding on Researchers and Output
- Proximity and Economic Activity: An Analysis of Vendor-University Transactions
- Model Reuse in Machine Learning for Author Name Disambiguation: An Exploration of Transfer Learning
- Nevertheless She Persisted? Gender Peer Effects in Doctoral STEM Programs
- Network Hubs Cease to be Influential in the Presence of Low Levels of Advertising
- Federal Funding of Doctoral Recipients: What Can be Learned from Linked Data
- Orcid-Linked Labeled Data for Evaluating Author Name Disambiguation at Scale
- Generating Automatically Labeled Data for Author Name Disambiguation: An Iterative Clustering Method
- The Impact of Imbalanced Training Data on Machine Learning for Author Name Disambiguation
- Evaluating Author Name Disambiguation for Digital Libraries: A Case of DBLP
- Creative Careers: The Life Cycles of Nobel Laureates in Economics
- Author-Based Analysis of Conference Versus Journal Publication in Computer Science
- Ethnicity-Based Name Partitioning for Author Name Disambiguation Using Supervised Machine Learning
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