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      Multi-institutional study of GRE scores as predictors of STEM PhD degree completion: GRE gets a low mark

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          Abstract

          The process of selecting students likely to complete science, technology, engineering and mathematics (STEM) doctoral programs has not changed greatly over the last few decades and still relies heavily on Graduate Record Examination (GRE) scores in most U.S. universities. It has been long debated whether the GRE is an appropriate selection tool and whether overreliance on GRE scores may compromise admission of students historically underrepresented in STEM. Despite many concerns about the test, there are few studies examining the efficacy of the GRE in predicting PhD completion and even fewer examining this question in STEM fields. For the present study, we took advantage of a long-lived collaboration among institutions in the Northeast Alliance for Graduate Education and the Professoriate (NEAGEP) to gather comparable data on GRE scores and PhD completion for 1805 U.S./Permanent Resident STEM doctoral students in four state flagship institutions. We found that GRE Verbal (GRE V) and GRE Quantitative (GRE Q) scores were similar for women who completed STEM PhD degrees and those who left programs. Remarkably, GRE scores were significantly higher for men who left than counterparts who completed STEM PhD degrees. In fact, men in the lower quartiles of GRE V or Q scores finished degrees more often than those in the highest quartile. This pattern held for each of the four institutions in the study and for the cohort of male engineering students across institutions. GRE scores also failed to predict time to degree or to identify students who would leave during the first year of their programs. Our results suggests that GRE scores are not an effective tool for identifying students who will be successful in completing STEM doctoral programs. Considering the high cost of attrition from PhD programs and its impact on future leadership for the U.S. STEM workforce, we suggest that it is time to develop more effective and inclusive admissions strategies.

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 October 2018
                2018
                : 13
                : 10
                : e0206570
                Affiliations
                [1 ] Department of Veterinary and Animal Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
                [2 ] School of Graduate Studies, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States of America
                [3 ] Graduate School, University of New Hampshire, Durham, New Hampshire, United States of America
                [4 ] Department of Biology and Office of the Provost, University of Vermont, Burlington, Vermont, United States of America
                [5 ] Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
                Northwestern University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-4612-7803
                Article
                PONE-D-18-13450
                10.1371/journal.pone.0206570
                6205626
                30372469
                a85b4b58-7610-4355-be6f-5b00efc13091
                © 2018 Petersen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 May 2018
                : 16 October 2018
                Page count
                Figures: 4, Tables: 7, Pages: 15
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Engineering and Technology
                Social Sciences
                Sociology
                Education
                Educational Attainment
                Standardized Tests
                People and Places
                Population Groupings
                Educational Status
                Graduates
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Statistical Hypothesis Testing
                Chi Square Tests
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Statistical Hypothesis Testing
                Chi Square Tests
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Metaanalysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Metaanalysis
                Science Policy
                Science and Technology Workforce
                Careers in Research
                Engineers
                People and Places
                Population Groupings
                Professions
                Engineers
                Social Sciences
                Social Sciences
                Sociology
                Education
                Medical Education
                Medicine and Health Sciences
                Medical Humanities
                Medical Education
                Custom metadata
                The data set used in this study has been de-identified by institution and specific discipline to preserve anonymity. The relevant data are included as a Supporting Information file.

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                Uncategorized

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