Predictors of Student Productivity in Biomedical Graduate School Applications

Joshua D Hall, Anna B O’Connell and Jeanette G Cook

PLOS ONE, 2017, vol. 12, issue 1, 1-14

Abstract: Many US biomedical PhD programs receive more applications for admissions than they can accept each year, necessitating a selective admissions process. Typical selection criteria include standardized test scores, undergraduate grade point average, letters of recommendation, a resume and/or personal statement highlighting relevant research or professional experience, and feedback from interviews with training faculty. Admissions decisions are often founded on assumptions that these application components correlate with research success in graduate school, but these assumptions have not been rigorously tested. We sought to determine if any application components were predictive of student productivity measured by first-author student publications and time to degree completion. We collected productivity metrics for graduate students who entered the umbrella first-year biomedical PhD program at the University of North Carolina at Chapel Hill from 2008–2010 and analyzed components of their admissions applications. We found no correlations of test scores, grades, amount of previous research experience, or faculty interview ratings with high or low productivity among those applicants who were admitted and chose to matriculate at UNC. In contrast, ratings from recommendation letter writers were significantly stronger for students who published multiple first-author papers in graduate school than for those who published no first-author papers during the same timeframe. We conclude that the most commonly used standardized test (the general GRE) is a particularly ineffective predictive tool, but that qualitative assessments by previous mentors are more likely to identify students who will succeed in biomedical graduate research. Based on these results, we conclude that admissions committees should avoid over-reliance on any single component of the application and de-emphasize metrics that are minimally predictive of student productivity. We recommend continual tracking of desired training outcomes combined with retrospective analysis of admissions practices to guide both application requirements and holistic application review.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0169121

DOI: 10.1371/journal.pone.0169121

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