GRE and TOEFL scores effectively predict graduate student success
the verdict
CONTESTED
contested - evenly split
refutedsupported
the weight of evidence
7 sources for · 2 against
Studies on GRE and TOEFL scores show mixed evidence, with some meta-analyses supporting their predictive validity for outcomes like coursework and GPA, while other research highlights limitations or finds undergraduate GPA to be a more effective predictor of graduate student success.
This meta-analysis examined the validity of the Graduate Record Examinations (GRE) and undergraduate grade point average (UGPA) as predictors of graduate school performance. The study included samples from multiple disciplines, considered different criterion measures, and corrected for statistical artifacts. Data from 1,753 independent samples were included in the meta-analysis, yielding 6,589 correlations for 8 different criteria and 82,659 graduate students. The results indicated that the GRE and UGPA are generalizably valid predictors of graduate grade point average, 1st-year graduate grade point average, comprehensive examination scores, publication citation counts, and faculty ratings. GRE correlations with degree attainment and research productivity were consistently positive; however, some lower 90% credibility intervals included 0. Subject Tests tended to be better predictors than the Verbal, Quantitative, and Analytical tests.
Abstract Historically, admissions committees for biomedical Ph.D. programs have heavily weighed GRE scores when considering applications for admission. The predictive validity of GRE scores on graduate student success is unclear, and there have been no recent investigations specifically on the relationship between general GRE scores and graduate student success in biomedical research. Data from Vanderbilt University Medical School’s biomedical umbrella program were used to test to what extent GRE scores can predict outcomes in graduate school training when controlling for other admissions information.
Overall, the GRE did not prove useful in predicating who will graduate with a Ph.D., pass the qualifying exam, have a shorter time to defense, deliver more conference presentations, publish more first author papers, or obtain an individual grant or fellowship. GRE scores were found to be moderate predictors of first semester grades, and weak to moderate predictors of graduate GPA and some elements of a faculty evaluation. These findings suggest admissions committees of biomedical doctoral programs should consider minimizing their reliance on GRE scores to predict the important measures of progress in the program and student productivity.
A reliance on using the GRE for admission decisions may limit their ability to enter the field. Biomedical research graduate programs have grown in size significantly over the last ten years [ 16 ], and many of these programs emphasize GRE scores for admissions decisions [ 2 ]. Few studies focus specifically on the relationship between biomedical Ph.D. student success and GRE scores. A recent study of 57 Puerto Rican biomedical students at Ponce Health Sciences University revealed a shared variance between GRE and months to defense (r 2 = .24), but no relationship between GRE score and degree completion or fellowship attainment [ 17 ].
Another small study of 52 University of California San Francisco (UCSF) biomedical graduate students attempted to show that general GRE scores are not predictive of student success [ 18 ], however, as UCSF students address in their critique, a vague definition of success and weak research methods confound the interpretation [ 19 ]. The UCSF students conclude that more rigorous studies, such as this one, are needed.
GRE Quantitative, Verbal, and Analytical Writing scores were used to test the hypothesis that they could predict several measures of graduate school performance, including (1) graduation with a Ph.D., (2) passing the qualifying exam, (3) time to Ph.D. defense, (4) number of presentations at national or international meetings at time of defense, (5) number of first author peer-reviewed publications at time of defense, (6) obtaining an individual grant or fellowship, (7) performance in the first semester coursework, (8) cumulative graduate GPA, and (9) final assessment of
Given that admissions committees do not base decisions on single measures like GRE Quantitative scores and instead look at a collection of admissions criteria, we have examined the influence of multiple measures as they pertain to graduate student success. Fig 1 Correlations between GRE Quantitative scores and continuous measures of student progress and productivity. Scatterplots of GRE Quantitative scores and (A) Time to Defense regression coefficient = 0.00, p = .83, R 2 = 0.00, (B) Presentation Count regression coefficient = 0.00, p = .32, R 2 = 0.00), and (C) First Author Publication Count (regression coefficient = 0.00, p = .62, R 2 = 0.00).
The less time that passes between measures, the stronger the relationship [ 26 , 27 ]. Interestingly, the GRE does moderately predict some elements in faculty evaluations of recently graduated students, which often occur over six years after the completion of the GRE. The most consistent pattern was found among GRE Verbal scores, which moderately predict faculty ratings of how well students handle classwork and minimally predict keeping up with literature, supporting our earlier finding that the GRE predicts success in the classroom.
Thus, to the question of whether GRE scores can help guide us to select individuals with these research-specific skills: the answer is that they do not. Variables other than the GRE are better predictors of graduate student success. Undergraduate GPA is a stronger predictor of graduate GPA, first semester grades, and graduating with a Ph.D. than GRE scores. Moreover, all of the objective admissions criteria explain only a small portion of the variance observed in most outcomes, meaning the admission criteria are missing many critical components of students’ success. Some of those components may be gleaned from letters and the personal statement.
ABSTRACT This meta-analysis assesses the predictive validity of the Graduate Record Examination (GRE) across outcome variables, including grade point average, for graduate students. In addition to aggregate effects, this paper also assessed changes in observed effects over time as related to increasing diversity in the graduate student population and as a function of gender and racial/ethnic composition of study samples. Framed using a lens of critical whiteness, this analysis examined n = 1,659 individual effects across k = 201 studies. Overall, 62.3% of reported effects were nonsignificant (i.e. no predictive value of GRE scores on student outcomes). Further, the magnitude of observed predictive relationships decreased significantly over time. The aggregate mean effect across all studies and outcomes was small, significant, and positive: GRE score predicted 3.24% of variance across measured outcomes, 4% of variance in overall GPA, and 2.56% of variance in first-year graduate GPA. Sample composition effects by race/ethnicity were notable under some conditions, but nonsignificant, with increasing proportions of people of Color within a study sample associated with poorer predictive validity for GPA. Likewise, the magnitude of negative effects where lower GRE scores predicted stronger student outcomes showed increasing trends from 0.16% of variance for all-white samples to 7.3% for samples comprised entirely of people of Color.
This study examined the selection ratio for 253 doctorate-level psychology programs. On average, the selection ratio for psychology Ph.D. programs was .11 ( SD = .12). This information was used to estimate the existing range restriction on General Record Examination (GRE) scores in validation studies. Results of the investigation showed the importance of correcting for range restriction in GRE validation research. The low selection ratio of graduate programs restricted the GRE variance in the validation sample, decreasing the observed mean correlations with graduate school performance criteria reported in the literature. Appropriate range restriction corrections were made using previous research data. Revised estimates of the GRE validity for predicting performance in psychology graduate programs were computed taking into account range restriction. Results showed that the GRE is a valid predictor of graduate school performance. We offer a GRE range restriction distribution that can be employed in future research on the validity of the GRE.
Graduate admissions committees throughout the United States examine both quantitative and qualitative data from applicants to make admissions determinations. A number of recent studies have examined the ability of commonly used quantitative metrics such as the GRE and undergraduate GPA to predict the likelihood of applicant success in graduate programs. We examined whether an admissions committee could predict applicant success at The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences based on quantitative metrics. We analyzed the predictive validity of admissions scores, undergraduate GPA, and the GRE for student success. We observed nuanced differences based on gender, ethnicity, race, and citizenship status. The scores assigned to applicants by the admissions committee could not predict time to degree in PhD students regardless of demographic group. Undergraduate GPA was correlated with time to degree in some instances. Interestingly, while GRE scores could predict time to degree, GRE percentile scores could predict both time to degree and PhD candidacy examination results. These findings suggest that there is a level of nuance that is required for interpretation of these quantitative metrics by admissions committees.
International student exchange programs have gained popularity as a means to increase enrollments, support international academic partnerships, and improve student preparedness for globalized work environments. However, the relationships between English language proficiency, cultural intelligence, teamwork, self-efficacy, academic success, and other factors within these programs are not clear. This study investigates the correlations among international accounting students' English language proficiency, accounting knowledge, and academic performance in a transnational education program in mainland China. Data were obtained from academic records of 104 accounting students enrolled in the program. A quantitative measuring of the Pearson correlation statistical tests were employed to measure the relationships between English language proficiency and academic performance, as well as between previous accounting knowledge and academic success. The results indicate a statistically significant relationship between English language proficiency and academic performance, and between previous accounting knowledge and academic success. This study has significant implications for transnational education programs, academic institutions, and policymakers and provides insights into effective strategies for enhancing the quality of transnational education programs and promoting the internationalization of higher education.
The competition for postgraduate admissions has intensified due to a rise in the number of applicants. Manystudents struggle to understand specific admission criteria and rely on costly and biased consultancy services. Toaddress this, a machine learning-based system is proposed to predict admission chances based on individualprofiles. The system uses a historical dataset with features like GRE scores, GPA, TOEFL Scores, Statement ofpurpose, Letter of recommendation, research experience, and professional background. Three models aredeveloped and compared: Linear Regression, Decision Tree Regression, and Logistic Regression. Linear andDecision Tree models explore linear and non-linear patterns, respectively. Logistic Regression, designed forbinary classification, predicts admission probabilities effectively. Logistic Regression shows superior accuracyand minimal error, making it the best choice. The system provides a scalable and data-driven alternative to helpstudents apply strategically.
Graduate school programs that are considering dropping the GRE as an admissions tool often focus on claims that the test is biased and does not predict valued outcomes. This paper addresses the bias issue and provides evidence related to the prediction of valued outcomes. Two studies are included. The first study uses data from chemistry and computer engineering programs from a flagship state university and an Ivy League university to demonstrate the ability of the GRE to predict dropout. The second study shows the relationship of GRE Analytical Writing scores to writing produced as part of graduate school coursework. In both studies results that are both practically and statistically significant are presented.
An analysis of 1,955 physics graduate students from 19 PhD programs shows that undergraduate grade point average predicts graduate grades and PhD completion more effectively than GRE scores. Students’ undergraduate GPA (UGPA) and GRE Physics (GRE-P) scores are small but statistically significant predictors of graduate course grades, while GRE quantitative and GRE verbal scores are not. We also find that males and females score equally well in their graduate coursework despite a statistically significant 18 percentile point gap in median GRE-P scores between genders. A counterfactual mediation analysis demonstrates that among admission metrics tested only UGPA is a significant predictor of overall PhD completion, and that UGPA predicts PhD completion indirectly through graduate grades. Thus UGPA measures traits linked to graduate course grades, which in turn predict graduate completion. Although GRE-P scores are not significantly associated with PhD completion, our results suggest that any predictive effect they may have are also linked indirectly through graduate GPA. Overall our results indicate that among commonly used quantitative admissions metrics, UGPA offers the most insight into two important measures of graduate school success, while posing fewer concerns for equitable admissions practices.
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