Grant Insights through Research & Development (GIRD): Using Big Data Centered Mixed Methods to Explain Variances in Grant Funding and Outcomes at Two-Year Colleges

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Recognizing disparities in external funding across two-year institutions in advanced technological education programs and the need to build institutional capacity to address these disparities, this research is designed to surface factors and characteristics of institutions that are associated with successful efforts to secure external funds. Descriptive information derived from this investigation will form the basis for a set of empirically derived best practices associated with success in securing funding. The central research question is, "What characteristics and factors differentiate colleges with varying levels of external funding?" The research team will conduct a mixed methods research study that combines a rich set of data: (1) algorithm-derived meta-data on two-year college characteristics and performance; (2) public and campus institutional data; (3) surveys of college and program faculty and administrators; and (4) in-depth interviews with college and program faculty and administrators. The team will adapt quantitative research methods, such as big data algorithms, cluster analyses, and decisions support systems, commonly employed by the financial and health care sectors, and apply them to higher education. In an effort to support skilled technical workforce development in advanced-technology fields through fostering institutional capacity, the goal of the investigation is to establish viable pathways and impactful practices by which less grant-active, two-year colleges can utilize external funding resources to better meet the needs of diverse student populations, faculty, and institutions in advanced technological programs. Additionally, the project will apply and test an innovative use of quantitative research approaches to answer questions that now can be examined using large data sets and data science methods in combination with more traditional data collection methodologies. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced technology fields that drive the Nation’s economy.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.