Understanding Faculty, Academic Careers, and Environments in Service of Equity
Effective Years: 2022-2024
This project addresses the fundamentally important question of who the faculty in the United States are, and how they advance knowledge and prepare the future workforce in STEM. Faculty are central to the success of higher education, and through teaching, research, and service, faculty contribute to the education of the future workforce in STEM, the advancement of knowledge, and the success of the higher education enterprise. Since 2004, when data was last collected for the National Study of Postsecondary Faculty (NSOPF), there has been no source of nationally representative data on faculty. Two major developments have also meaningfully changed the landscape for academic employment, particularly in STEM. First, the academic labor force is increasingly contingent, including a substantial increase in the number of non-tenure-track faculty (NTTF). Second, there have been substantial investments into efforts to increase the number of faculty from groups historically underrepresented in STEM (race/ethnicity, disability, gender, and socio-economically disadvantaged). Thus, the discontinuation of NSOPF has resulted in a significant gap in knowledge about postsecondary faculty in the United States. The lack of good information on faculty members’ identities, careers, working conditions, and outcomes handicaps administrators and policymakers in supporting faculty success. Therefore, the purpose of this project is to develop and test the Faculty, Academic Careers, and Environments (FACE) survey, an updated and expanded version of NSOPF that can provide a contemporary understanding of postsecondary faculty in the United States, including who they are and how their working conditions shape their opportunity to be effective scientists and educators.
The goal of this research is to design the research methods and infrastructure for FACE and to conduct a field test to evaluate the quality and effectiveness of project processes and products to facilitate success at scale. Achieving nationally representative data on faculty requires a complex, two-stage sample. At the institution level, researchers aim to (1) create and validate a stratified sampling frame, (2) conduct focus groups with institutional data providers from different institutional contexts to inform data collection instruments and formats, (3) determine methods and processes for collecting institutional data on faculty, (4) develop the institutional data repository and questionnaire to collect accurate data about faculty, and (5) collect and analyze pilot data from a sample of roughly 10% of eligible institutions. At the faculty level, researchers aim to (1) create and validate a faculty-level sample frame, (2) determine methods and processes for collecting faculty-level data to facilitate high response rates, (3) identify valid and reliable items and constructs that inform our understanding of academic careers and environments, (4) develop a faculty survey instrument, and (5) collect and analyze pilot data from a nonprobability sample of at least 1,500 faculty respondents. Additionally, this study uses an innovative approach to field testing, decoupling institution-level and faculty-level data collection to allow researchers to collect pilot data at both levels simultaneously.
This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent.
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.