Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
NSF Grantees Poster Session
8
10.18260/1-2--42671
https://peer.asee.org/42671
164
Doctoral attrition is a significant issue: The ten-year completion rates for engineering PhDs are only 65% for men and 56% for women across engineering disciplines, and significantly lower for students from underrepresented groups. Each graduate student represents a loss of talent, but also a significant investment from funding organizations, faculty, departments, and students themselves. National statistics measure PhD and MS conferral rates, but not the rates at which students choose to leave with a Master’s. The objective of this grant is to characterize and model Master’s-level departure from the engineering PhD by analyzing the perspectives of departers, currently “questioning” graduate students considering departure, and faculty. The poster that will be presented at the 2023 ASEE Conference will show quantitative and qualitative data from the first aim of this NSF-funded CAREER project, which is to characterize common narratives of Master’s-level departure from the engineering PhD and model departure decisions over time through a mixed methods phase employing unique qualitative and quantitative streams of data. In this phase, interviews with 42 departers and “questioning” PhD students were analyzed through thematic analysis, Qualitative Comparative Analysis (QCA) methods and narrative analysis methods. From these results, a longitudinal (SMS) survey was developed and deployed to questioning graduate students with the goal of quantitatively modeling pathways to attrition or persistence through time-series methods. To date, the SMS survey has been collecting data three times per week from two cohorts representing over 500 graduate engineering student participants across the United States since January 2022.
Berdanier, C. (2023, June), Board 231: CAREER: Characterizing Master’s-Level Departure from the Engineering Doctorate through Multiple Stakeholders’ Perspectives Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42671
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