Does A High Verbal Gre Makeup For An Average Quantitative

• Periodical List
• PLoS One
• PMC5226333

PLoS One. 2017; 12(1): e0166742.

The Limitations of the GRE in Predicting Success in Biomedical Graduate Schoolhouse

Liane Moneta-Koehler

^one The Office of Biomedical Inquiry Education & Grooming, Vanderbilt Academy Schoolhouse of Medicine, Nashville, Tennessee, Us of America

Abigail Yard. Brown

¹ The Role of Biomedical Research Teaching & Training, Vanderbilt University Schoolhouse of Medicine, Nashville, Tennessee, United states of America

Kimberly A. Petrie

¹ The Office of Biomedical Research Education & Training, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America

Brent J. Evans

² Department of Leadership, Policy & Organizations, Peabody College, Vanderbilt University, Nashville, Tennessee, The states of America

Roger Chalkley

¹ The Role of Biomedical Enquiry Education & Preparation, Vanderbilt Academy Schoolhouse of Medicine, Nashville, Tennessee, U.s. of America

Luís A. Nunes Amaral, Editor

Received 2016 Aug 15; Accepted 2016 November 2.

Supplementary Materials

S1 Supporting Information: Details regarding the sample and additional results tables. (DOCX)

GUID: FF27C1A8-3BB0-4922-A01D-F3B2CD419A1C

Information Availability Argument

In order to protect student privacy, information cannot be made publicly bachelor. All interested and qualifying researchers will be able to admission the data upon request. Data requests may be fabricated by contacting the authors or the the Vanderbilt Biomedical Research Education and Training (BRET) Part: The Office of Biomedical Research Education & Training, Vanderbilt University, School of Medicine, 340 Light Hall, Nashville, TN 37232-0301, 615-343-4611 (Main Number).

Abstract

Historically, admissions committees for biomedical Ph.D. programs accept heavily weighed GRE scores when considering applications for admission. The predictive validity of GRE scores on graduate student success is unclear, and there accept been no recent investigations specifically on the relationship between general GRE scores and graduate educatee success in biomedical inquiry. Data from Vanderbilt University Medical School's biomedical umbrella programme were used to test to what extent GRE scores can predict outcomes in graduate school training when decision-making for other admissions information. Overall, the GRE did non bear witness useful in predicating who volition graduate with a Ph.D., pass the qualifying exam, accept a shorter fourth dimension to defense, evangelize more conference presentations, publish more first writer papers, or obtain an individual grant or fellowship. GRE scores were establish to be moderate predictors of starting time semester grades, and weak to moderate predictors of graduate GPA and some elements of a faculty evaluation. These findings advise 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.

Introduction

The goal of biomedical graduate Ph.D. programs is to identify and train students for the purpose of advancing biomedical research. Admissions committees are charged with the chore of predicting who volition be the best Ph.D. students given somewhat limited information most the applicants' past performance: undergraduate grade bespeak average (GPA); Graduate Record Exam (GRE) Quantitative, Verbal, and Writing scores; letters of recommendation; and a personal statement. GRE scores are highly influential in the selection procedure [i,2], yet past research is unclear regarding the ability of GRE scores to predict students' graduate performance, with some studies showing weak correlations with graduate school grades [iii] and some studies showing a more than robust impact of GRE scores on student outcomes [four–6].

The Educational Testing Service (ETS), which administers the GRE, advises restrained employ of general test scores for admissions and discourages the use of a cutoff score [7]. According to their ain studies, the GRE correlates slightly with graduate GPA [eight] and does non predict other skills needed to succeed in a variety of graduate programs [9]. It also has been argued that the GRE is a racially and socioeconomically biased exam [1] similar to arguments fabricated about the Sat and ACT at the undergraduate level [10]. For example, from 2009–2010, White GRE exam takers scores on the Quantitative, Verbal, and Belittling Writing subtests were 18–32% college than Blackness exam takers [xi]. Moreover, students with a low socioeconomic status (SES) perform worse on standardized tests, and exams like the Saturday are highly correlated with parental income [12]. Explanations for these differences include educatee access to academic preparation such as prior schooling or test prep courses [13], stereotype threat [xiv], and fifty-fifty the inability to pay to retake the $195 test after receiving a low score. Regardless of the reason, certain groups perform worse than others on the test, and schools that demand high GRE scores for access may be systematically disadvantaging specific racial and socioeconomic groups. African Americans, Hispanics, Native Americans, and Hawaiian/Pacific Islanders, likewise every bit low SES individuals, are already underrepresented in the biomedical inquiry workforce [15]. A reliance on using the GRE for admission decisions may limit their power to enter the field.

Biomedical enquiry graduate programs take grown in size significantly over the last ten years [16], and many of these programs emphasize GRE scores for admissions decisions [two]. Few studies focus specifically on the relationship between biomedical Ph.D. educatee success and GRE scores. A recent report of 57 Puerto Rican biomedical students at Ponce Health Sciences University revealed a shared variance between GRE and months to defense (rⁱⁱ = .24), but no relationship betwixt GRE score and degree completion or fellowship attainment [17]. Another pocket-size study of 52 Academy of California San Francisco (UCSF) biomedical graduate students attempted to show that general GRE scores are not predictive of educatee success [18], however, as UCSF students accost 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 every bit this one, are needed.

Larger studies of biomedical graduate students have shown shared variance between GRE scores and graduate GPA (r² ranging from .05 to .25), as well as kinesthesia ratings of student performance (r² ranging from .05 to .25), merely rely on data that are 19 or more years old and practise not account for more contempo changes to the examination [iv,5,20]. ETS regularly updates questions and changed the GRE in 2002, replacing the Analytical Ability section with the Analytical Writing Assessment section. Newer GRE scores may show different predictions for biomedical Ph.D. student success than they did 19 years ago, and there appears to be no written report that examines the individual contribution of the GRE Writing subtest on biomedical doctoral pupil outcomes. Furthermore, Ph.D. students have changed. Incoming cohorts of students are vastly more diverse [21] and with more than robust research feel [22] than in previous years. The revision of the GRE in concert with changes in student populations prompt this focused and updated investigation.

The Vanderbilt Interdisciplinary Graduate Program (IGP) is an umbrella admissions program that started in 1992 and serves the graduate programs in biochemistry, biological sciences, cancer biology, prison cell and developmental biology, cellular and molecular pathology, chemical and concrete biological science, human genetics, microbiology and immunology, molecular physiology and biophysics, neurosciences, and pharmacology. Students may apply directly to a biomedical section, however the majority choose to enter through the IGP. IGP students are required to consummate a mutual first semester form, supplemented by electives in the spring semester, and three–4 rotations in different inquiry laboratories across the 11 different programs and departments. After students consummate the first year, they enter a specific caste granting program to continue their studies.

The IGP admissions commission meets weekly during late winter to brand admission decisions. It is made up of 13 kinesthesia and a representative of diverseness initiatives. GRE Quantitative, Verbal, and Writing scores (maximum scores in the case of multiple tests) are used for admissions decisions, along with undergraduate GPA, letters of recommendation, a personal statement, and, for some, campus visits and interviews. Since the inception of the IGP, there has been no minimum GRE cutoff score, but if an bidder's GRE scores are depression, he or she volition have to excel in at least 1 of the other iii awarding requirements to be competitive for admission.

This study investigates the predictive validity of GRE scores on various quantitative and qualitative measures of success for biomedical Ph.D. students including measures of progress in the programme (passing the qualifying exam, graduation with a Ph.D., and time to defence), research productivity (presentation and showtime writer publication rates and obtaining private grants and fellowships), grades (first semester and overall GPAs), and kinesthesia evaluations of students obtained at the time of thesis defense. Kinesthesia evaluations, while beingness subjective measures of success, are important for the IGP given that virtually faculty do not directly select graduate students to enter their labs. Instead the admissions commission selects a cohort of biomedical students that they hope will come across the expectations of their kinesthesia colleagues. Post-graduate career outcomes were excluded from the study, as we are hesitant to categorize one career as more or less successful than another. This, this report focuses solely on measures of success up to and including graduation.

We explore the importance of the GRE Full general Test in the biomedical field using a large and up to engagement dataset. This written report covers hundreds of students from 11 departments and programs and looks at a wider range of outcomes and command variables than prior studies. Such an up-to-date, comprehensive evaluation of the apply of the GRE in evaluating prospective biomedical graduate students is of import to ensure that the admissions process aligns with the goals of the institution and to determine whether a GRE requirement for graduate school admission is worth the inherent biases that the exam might bring into the admissions process.

Methods

Information were nerveless on 683 students who matriculated into the Vanderbilt University IGP from 2003 to 2011, a fourth dimension period in which reliable GRE scores are available. Over lxxx% of students have had fourth dimension to complete the program. GRE Quantitative, Verbal, and Belittling Writing scores were used to test the hypothesis that they could predict several measures of graduate schoolhouse performance, including (ane) graduation with a Ph.D., (2) passing the qualifying exam, (three) fourth dimension to Ph.D. defense, (iv) number of presentations at national or international meetings at time of defense, (5) number of first author peer-reviewed publications at time of defence force, (half-dozen) obtaining an individual grant or fellowship, (7) performance in the commencement semester coursework, (8) cumulative graduate GPA, and (nine) final cess of the competence of the student as a scientist as evaluated by the enquiry mentor. In order to determine the independent contributions of GRE scores on event measures, additional admissions criteria were included in the analyses as controls: undergraduate GPA, undergraduate institution selectivity, whether a educatee has a prior advanced degree, underrepresented minority status, international student status, and gender. Details on variables are described below. The enquiry was canonical by Vanderbilt University IRB (#151678). Consent was not given equally information were analyzed anonymously.

Independent Variables

Students submitted their GRE scores as office of their application to the IGP. If multiple scores were submitted, superscores (the highest score on each subtest) were used for admissions decisions and for this study. Students also submitted their undergraduate GPAs, undergraduate institutions, prior advanced caste data, minority status, international student status, and gender on their IGP applications. Undergraduate institution selectivity from the 2007–2008 year, the median admissions yr for the sample, was acquired from The Integrated Postsecondary Educational activity Data Arrangement [23]. Selectivity is calculated by dividing the number of admissions offers by the number of applicants. Lower numbers are associated with more than selective schools. Prior advanced degrees include master's degrees, medical degrees and pharmacy degrees. Students are considered to have underrepresented minority status if they are underrepresented in science as defined by the National Institutes of Health. Minority status specifically denotes individuals from certain racial and indigenous groups (African Americans, Hispanic Americans, Native Americans, Alaskan Natives, Hawaiian Natives, and natives of the U.S. Pacific Islands), individuals with disabilities, and individuals from disadvantaged backgrounds. Students holding temporary visas are categorized as international students. Only international students with undergraduate degrees from U.S. schools were included in the study (see Results).

Measures of Student Progress through the Ph.D. Program

Shortly afterward the second yr of study, students took a pass/fail qualifying examination to be admitted for Ph.D. candidacy. Afterward, students spent the residuum of their time in the program on their dissertation research projects. Afterward successful completion of their dissertation research, students then dedicated their dissertation and graduated with a Ph.D. Some students withdrew from the program leaving with no degree, while others left with a final Masters caste. Fourth dimension to successful Ph.D. defense was calculated by subtracting a student'due south matriculation date from his or her defence date and dividing past 365.25 days. Data includes all students who dedicated their dissertations before May 2016. The current sample of students trained in over 200 different laboratories, which precludes using mentor controls due to the minor number of students in each lab.

Enquiry Productivity

Within two weeks after the dissertation defence, Ph.D. students were invited to consummate a voluntary 117-question get out survey. The go out survey covers a wide multifariousness of topics including the number of first-author peer-reviewed scientific papers (published or in press), the number of scientific presentations (poster or podium presentation) given at national or international meetings and conferences, and if they received an individual grant or fellowship while enrolled in the Ph.D. program. Each respondent was limited to a maximum of 12 presentations, a response given by one pupil in the sample. Only competitive grants and fellowships were considered. Sixty-4 percent of the grants and fellowships were supported past federal sources, such as the National Institutes of Wellness and the National Science Foundation, while the remaining 36% percent were supported by private organizations, such as the American Heart Association. Eleven percent of all awards promote multifariousness in research. The leave survey began in January, 2007 and has a response rate of over 90%.

Grades

All students inbound the IGP took 1 semester of intensive core coursework, intended to teach the fundamentals of biomedical research, disquisitional thinking, and how to gain information from the scientific literature. Student received a grade out of 100%. In the spring semester of the IGP year, students took elective courses. At the finish of the IGP twelvemonth, students selected a training program in 1 of xi participating departments or programs and completed an additional yr of didactic course work and initiated their thesis requirements. The graduate GPA includes all didactic form grades from the outset two years of written report.

Faculty Evaluation of Student Immediately After Defense

Within one year of the student's dissertation defence force, the thesis mentor completed a concluding evaluation of the student. The mentor was asked to rate the student on a scale from one (all-time possible score) to five (worst possible score) in ten different categories: (1) power to handle the classwork needed for success in the Ph.D. program, (2) bulldoze and conclusion, (3) creativity and imagination in terms of experimental pattern and interpretation, (4) technical power, (v) keeping upwardly with the literature, (six) output (i.eastward. translating observations into a presentable paper), (seven) ability to write creatively, (eight) leadership in the lab and department, (9) trajectory, and (10) overall assessment as a productive scientist. This mentor evaluation began in July, 2007 and has a response charge per unit of over 65%.

Results

Table 1 shows the descriptive statistics for each of the independent and dependent variables. To command for changes in the sample, analyses were performed on the 495 students showing values for all of the contained variables. See S1 Supporting Information for details on how this group differs from the students for whom we do not have complete information. Within the sample of 495 students, not all students have information for each dependent variable. nineteen% of students left the program with a Master's or no degree. Moreover, given that the students took an average of 5.67 years to defend, 17% of students were still active in the plan, further reducing the mean number of students that graduated with a Ph.D. Of those that attained a Ph.D., 91% completed the survey asking well-nigh presentations, publications, and grants, and 65% received evaluations from their faculty mentors.

Table one

Summary Statistics for each of the Contained and Dependent Variables.

Contained Variable	North	Mean or Proportion+	SD
GRE Scores
GRE Quantitative	495	693.35	67.34
GRE Verbal	495	554.26	84.82
GRE Analytical Writing	495	4.62	0.67
Undergraduate GPA	495	3.54	0.32
Undergraduate Institution Selectivity	495	59.44	20.02
Proportion with Prior Advanced Degree	495	0.05	0.21
Proportion with Underrepresented Minority Status	495	0.12	0.33
Proportion International Students	495	0.05	0.21
Proportion Female	495	0.59	0.49
Dependent Variable
Proportion Graduated with a Ph.D.	495	0.64	0.48
Proportion Passed Qualifying Examination	495	0.88	0.32
Time to Defence force (years)	318	v.67	0.98
Presentation Count	271	4.06	2.32
First Writer Publication Count	271	1.79	ane.10
Proportion with Private Grant or Fellowship	271	0.38	viii.36
Get-go Semester Grade	488	79.73	0.ninety
Graduate GPA	492	3.66	0.27
Faculty Evaluation
Ability to Handle Classwork	210	1.79	0.84
Drive	210	i.98	1.02
Creativity with Experimental Design	210	2.22	0.99
Technical Ability	210	one.85	0.88
Keeping up with Literature	210	2.15	0.96
Output	210	ii.xi	one.02
Writing	210	2.31	1.03
Leadership	210	2.04	1.06
Trajectory	210	2.09	0.99
Overall Assessment	210	2.09	1.00

A visual examination of the relationship between GRE Quantitative scores and the continuous measures of progress in the plan and research productivity revealed no effect (Fig 1). GRE Quantitative scores did non significantly correlate with Time to Defense (regression coefficient = 0.00, p = .83, R^two = 0.00), Presentation Count (regression coefficient = 0.00, p = .32, R² = 0.00), or Offset Author Publication Count (regression coefficient = 0.00, p = .62, R² = 0.00, see Fig 1). Like results were plant with GRE Verbal and Writing scores. Given that admissions committees do not base of operations decisions on single measures like GRE Quantitative scores and instead await at a collection of admissions criteria, we have examined the influence of multiple measures as they pertain to graduate pupil success.

Correlations between GRE Quantitative scores and continuous measures of student progress and productivity.

Scatterplots of GRE Quantitative scores and (A) Time to Defence force regression coefficient = 0.00, p = .83, R^two = 0.00, (B) Presentation Count regression coefficient = 0.00, p = .32, R^two = 0.00), and (C) Beginning Author Publication Count (regression coefficient = 0.00, p = .62, R² = 0.00). GRE Quantitative scores do non correlate with these measures of success.

Following a line of inquiry that examines predictive validity of test scores, in gild to evaluate the influences of each independent variable in the presence of the other admission criteria, linear regression analyses were used [24,25]. Access accomplice was included every bit a stock-still upshot to account for systematic changes that occur over time. Nosotros first looked at the influence of GRE scores and other admissions criteria on measures of progress in the program, defined as Passing the Qualifying Exam, Graduation with a Ph.D., and Time to Defence. Nosotros then investigated measures of productivity (Presentation Count, Starting time Author Publication Count, and Obtaining an Individual Grant or Fellowship), grades (First Semester GPA and Graduate GPA), and faculty evaluations.

Fig 2 provides an overview of each GRE subtest'southward relationship with eight different measures of pupil success after decision-making for other admission criteria. Standardized regression coefficients reflect effect sizes such that, for example, one standard deviation change in GRE Quantitative is associated with a 0.16 standard deviation modify in First Semester GPA. Standardized correlation coefficients were used in society to brand comparisons across variables. Afterwards analyses left binary variables unstandardized. We tin can encounter that GRE Verbal scores were a amend predictor of First Semester Grades than Graduate GPA due to the college standardized regression coefficient for Start Semester Grades. In sum, GRE scores showed some validity in predicting classroom performance but non progress in the program or inquiry productivity.

The predictive ability of GRE scores on dissimilar measures of student success.

Standardized regression coefficients reported afterwards controlling for other admissions criteria. Cohort fixed effects are included for each model. Coefficients of zero appear as missing bars. *p < .05. **p < .01. ***p < .001.

GRE Scores Do Non Predict Progress in the Program

The results collected in Table 2 allowed united states of america to see the effect of GRE scores upon an upshot variable collected during graduate grooming, in this case graduation with a Ph.D. Continuous independent variables (GRE Quantitative, GRE Exact, GRE Writing, Undergraduate GPA, and Undergraduate Institution Selectivity) were standardized before entering the regression, whereas binary independent variables (Prior Avant-garde Degree, Underrepresented Minority, International, and Female) were not standardized and are shaded in grey to indicate that they are unstandardized regression coefficients. We used linear probability models for the binary dependent variables, so the coefficients should be interpreted equally a alter in the probability of the event happening (i.e. graduating). The table is synthetic to display first the issue of a single variable, GRE Quantitative, on Graduation with a Ph.D. (Column (1)). The simple bivariate regression between GRE Quantitative and Graduation with a Ph.D. revealed no influence of GRE Quantitative scores. Moving rightward, when GRE Verbal and Writing scores were added to the model (Column (iii)), none were shown to predict Graduation with a Ph.D. The rightmost column (Column (9)) is particularly informative equally it shows the independent contribution of each GRE subtest afterward decision-making for all the other observed admissions variables. Once again, none of the GRE subtests predicted graduating with a Ph.D. Undergraduate GPA significantly predicted Gradation with a Ph.D., such that ane standard deviation increment in Undergraduate GPA was associated with a 0.05 increase in the probability of attaining a Ph.D. Note that one standard deviation of Undergraduate GPA in the sample was 0.32 on a 4 point scale (Tabular array i). Underrepresented Minority status too predicted Graduation with a Ph.D., such that Underrepresented Minority students had a 0.13 subtract in the probability of attaining a Ph.D relative to non-minority students. The full model deemed for 29% of the variance in Graduation with a Ph.D. (run across Adapted R² in Table 2), most of which was driven past the inclusion of cohort stock-still effects which control for a host of unobserved factors that were consistent with accomplice. We chose to nowadays linear probability models because their coefficients are straight interpretable as changes in the probability of graduating; withal, logit models showed the same sign and significance and thus were qualitatively similar to the linear regression results.

Tabular array two

The predictive power of GRE scores and other admissions criteria on Graduation with a Ph.D.

	(1)	(2)	(3)	(4)	(v)	(half dozen)	(7)	(8)	(ix)
GRE Quantitative	0.00	0.00	0.00	0.00	-0.01	0.00	-0.01	-0.01	-0.01
	[-0.04, 0.03]	[-0.04, 0.04]	[-0.04, 0.04]	[-0.04, 0.04]	[-0.05, 0.03]	[-0.04, 0.04]	[-0.05, 0.03]	[-0.05, 0.03]	[-0.05, 0.03]
GRE Verbal		0.00	0.01	0.01	0.00	0.00	0.00	0.00	0.00
		[-0.04, 0.05]	[-0.03, 0.05]	[-0.04, 0.05]	[-0.04, 0.04]	[-0.04, 0.04]	[-0.05, 0.04]	[-0.04, 0.04]	[-0.04, 0.04]
GRE Writing			-0.01	-0.01	-0.02	-0.02	-0.02	-0.02	-0.02
			[-0.05, 0.03]	[-0.05, 0.03]	[-0.06, 0.02]	[-0.06, 0.02]	[-0.06, 0.02]	[-0.06, 0.02]	[-0.06, 0.02]
Undergraduate GPA				0.04*	0.05*	0.05**	0.05**	0.05*	0.05*
				[0.00, 0.08]	[0.01, 0.09]	[0.01, 0.09]	[0.01, 0.09]	[0.01, 0.09]	[0.01, 0.09]
Undergraduate Inst. Selectivity					-0.02	-0.03	-0.03	-0.03	-0.03
					[-0.06, 0.02]	[-0.06, 0.01]	[-0.07, 0.01]	[-0.07, 0.01]	[-0.06, 0.02]
Prior Advanced Caste						0.13	0.14	0.13	0.13
						[-0.04, 0.31]	[-0.04, 0.31]	[-0.05, 0.31]	[-0.05, 0.31]
Underrepresented Minority							-0.12*	-0.thirteen*	-0.13*
							[-0.24, 0.00]	[-0.25, -0.01]	[-0.24, -0.01]
International								0.08	0.08
								[-0.10, 0.26]	[-0.ten, 0.26]
Female									0.02
									[-0.05, 0.10]
Adapted R-Squared	0.28	0.28	0.28	0.29	0.29	0.29	0.29	0.29	0.29
Observations	495	495	495	495	495	495	495	495	495

Similar linear regression analyses were run to predict a educatee's passing the Qualifying Exam and Time to Defense. The results are in Tables A and B in S1 Supporting Data. Continuous independent and dependent variables were standardized before inbound the regressions. When all admission criteria are entered into the model, no variable predicted a student'south likelihood of passing the Qualifying Exam or Fourth dimension to Defence force.

GRE Scores Practise Non Predict Research Productivity

Linear regression analyses were used to compare GRE scores to quantitative measures of research productivity. These measures include Presentation Count (Table C in S1 Supporting Information), Beginning Writer Publication Count (Table D in S1 Supporting Data), and obtaining an Private Grant or Fellowship (Table East in S1 Supporting Information). Continuous contained and dependent variables were standardized before entering the regressions. When all access criteria were included in the models, none of the GRE subtests predicted the in a higher place dependent variables. Minority Status was the only pregnant predictor of obtaining an Individual Grant or Fellowship, and no variables significantly predicted Presentation Count or First Writer Publication Count. GRE scores and most standard objective measures for admissions did not predict measures of student productivity.

GRE Scores Moderately Predict Grades

Linear regressions were run to examine student classroom operation, starting with First Semester Grades (Table 3). For this continuous event, the variable was standardized, equally were all of the continuous independent variables such that regression coefficients tin exist interpreted as issue sizes. The shaded, binary contained variables remained unstandardized. GRE Quantitative scores moderately predicted First Semester GPA (Cavalcade (1)). A i standard deviation increase in GRE Quantitative was associated with a 0.29 standard departure increase in Showtime Semester Grades (Column (1)). When GRE Verbal scores were added (Column (2)), the model accounted for an additional four% of the variance in First Semester Grades. GRE Writing scores did not predict First Semester Grades. GRE Quantitative and Verbal connected to predict Offset Semester Grades after controlling for other factors (Cavalcade (9)), although the magnitude of the human relationship was adulterate with the inclusion of other predictors, given their overlapping influence on grades. Undergraduate GPA, Admission Rate, and Underrepresented Minority status besides predicted Start Semester Grades, with Undergraduate GPA having a college coefficient than the GRE subtests. Undergraduate Institution Selectivity negatively contributed to First Semester Grades. Since competitive schools have lower selectivity scores, college selectivity represents less competitive schools and predicted lower First Semester Grades. Underrepresented Minority status was associated with a 0.35 standard divergence decrease in grades. When all admissions variables were included, the model deemed for 40% of the variance in First Semester Grades, and was strongly driven past the cohort fixed upshot.

Table iii

The predictive ability of GRE scores and other admissions criteria on First Semester Grades.

	(i)	(2)	(iii)	(4)	(5)	(6)	(vii)	(viii)	(9)
GRE Quantitative	0.29***	0.xx***	0.20***	0.18***	0.18***	0.18***	0.16***	0.xvi***	0.xvi***
	[0.22, 0.37]	[0.12, 0.28]	[0.12, 0.28]	[0.11, 0.26]	[0.10, 0.26]	[0.11, 0.26]	[0.08, 0.24]	[0.08, 0.24]	[0.08, 0.24]
GRE Verbal		0.22***	0.21***	0.xix***	0.18***	0.18***	0.17***	0.16***	0.16***
		[0.fourteen, 0.thirty]	[0.12, 0.29]	[0.xi, 0.27]	[0.ten, 0.27]	[0.10, 0.26]	[0.08, 0.25]	[0.08, 0.24]	[0.08, 0.24]
GRE Writing			0.04	0.03	0.02	0.02	0.01	0.01	0.01
			[-0.04, 0.12]	[-0.05, 0.xi]	[-0.06, 0.10]	[-0.06, 0.x]	[-0.06, 0.09]	[-0.07, 0.09]	[-0.07, 0.09]
Undergraduate GPA				0.25***	0.27***	0.27***	0.27***	0.27***	0.28***
				[0.17, 0.32]	[0.19, 0.34]	[0.twenty, 0.35]	[0.twenty, 0.35]	[0.20, 0.35]	[0.twenty, 0.35]
Undergraduate Inst. Selectivity					-0.07	-0.07	-0.08*	-0.08*	-0.08*
					[-0.14, 0.01]	[-0.fifteen, 0.01]	[-0.xv, 0.00]	[-0.xvi, -0.01]	[-0.sixteen, -0.01]
Prior Advanced Caste						0.23	0.24	0.24	0.24
						[-0.11, 0.57]	[-0.10, 0.57]	[-0.09, 0.58]	[-0.09, 0.58]
Underrepresented Minority							-0.37**	-0.35**	-0.35**
							[-0.59, -0.xiv]	[-0.58, -0.13]	[-0.58, -0.xiii]
International								-0.13	-0.13
								[-0.48, 0.21]	[-0.48, 0.21]
Female									-0.02
									[-0.17, 0.12]
Adjusted R-Squared	0.29	0.33	0.33	0.39	0.39	0.39	0.40	0.40	0.forty
Observations	488	488	488	488	488	488	488	488	488

Students took didactic courses for their first two years of graduate school, and grades from those courses comprise the Graduate GPA (examined in Table 4). Column (two) shows that the model with the GRE Quantitative and Exact subtest predicted 8% of the variance of Graduate GPA and each independently fabricated pregnant contributions to the prediction. Yet, Undergraduate GPA, Undergraduate Institution Selectivity, and Underrepresented Minority condition were too related to Graduate GPA (Column (nine)) and when included in the model, GRE Exact was the only GRE subtest to predict Graduate GPA, and to a bottom degree than Undergraduate GPA. Quantitative and GRE Writing scores did non predict Graduate GPA when controlling for other admissions variables. When all variables were analyzed the model, including the cohort fixed outcome, predicted 17% of the variance in Graduate GPA.

Table four

The predictive power of GRE scores and other admissions criteria on Graduate GPA.

	(i)	(2)	(3)	(iv)	(5)	(6)	(7)	(eight)	(9)
GRE Quantitative	0.22***	0.fourteen**	0.13**	0.12*	0.11*	0.12*	0.07	0.08	0.09
	[0.xiv, 0.31]	[0.05, 0.24]	[0.04, 0.23]	[0.03, 0.21]	[0.02, 0.20]	[0.02, 0.21]	[-0.02, 0.17]	[-0.02, 0.17]	[-0.01, 0.18]
GRE Verbal		0.20***	0.17***	0.16**	0.15**	0.14**	0.12*	0.11*	0.xi*
		[0.10, 0.29]	[0.07, 0.27]	[0.06, 0.25]	[0.05, 0.24]	[0.05, 0.24]	[0.02, 0.21]	[0.02, 0.21]	[0.02, 0.21]
GRE Writing			0.09	0.08	0.07	0.07	0.05	0.05	0.05
			[-0.01, 0.eighteen]	[-0.01, 0.17]	[-0.02, 0.16]	[-0.03, 0.16]	[-0.04, 0.14]	[-0.04, 0.14]	[-0.04, 0.14]
Undergraduate GPA				0.22***	0.25***	0.26***	0.25***	0.26***	0.25***
				[0.14, 0.31]	[0.16, 0.34]	[0.17, 0.35]	[0.16, 0.34]	[0.17, 0.34]	[0.sixteen, 0.34]
Undergraduate Inst. Selectivity					-0.09	-0.09	-0.10*	-0.11*	-0.11*
					[-0.eighteen, 0.00]	[-0.18, 0.00]	[-0.nineteen, -0.02]	[-0.xx, -0.02]	[-0.20, -0.02]
Prior Advanced Caste						0.22	0.23	0.24	0.24
						[-0.xix, 0.62]	[-0.17, 0.63]	[-0.16, 0.64]	[-0.16, 0.64]
Underrepresented Minority							-0.66***	-0.65***	-0.65***
							[-0.93, -0.xl]	[-0.92, -0.38]	[-0.92, -0.38]
International								-0.11	-0.11
								[-0.52, 0.29]	[-0.52, 0.xxx]
Female									0.08
									[-0.09, 0.25]
Adjusted R-Squared	0.05	0.08	0.08	0.13	0.13	0.13	0.17	0.17	0.17
Observations	492	492	492	492	492	492	492	492	492

GRE Scores Predict Some Responses to Mentor Evaluations later on Defence force

After a student defended his or her dissertation, the faculty mentor completed a 10-question evaluation. Linear regression analyses were run to examine the influence of each admissions variable on answers to individual questions from the faculty evaluations (Table five). Each column represents a different kinesthesia measured outcome, and simply the full models with all of the contained variables are presented. Faculty evaluation ratings were standardized earlier entering the models. Considering a faculty evaluation rating of i is the highest score and v is the lowest, a negative regression coefficient indicates that a variable predicts good graduate school functioning. Higher GRE Verbal scores predicted better faculty evaluations of a student'due south ability to handle classwork, keep upward with the literature, and write creatively. Undergraduate GPA too contributed to faculty evaluations of a student'southward ability to handle classwork and write creatively. GRE Writing scores were related to leadership in the lab or department, and Undergraduate Selectivity predicted classwork, creativity in terms of experimental design, and the overall assessment. Having a prior advanced degree had a contrary relationship with faculty evaluations of technical power and leadership, and International student status had a reverse human relationship with evaluations of ability to go along upward with the literature. There were no consequent patterns across the unlike kinesthesia ratings and most admission criteria, making it hard to predict faculty evaluations with information available during the admissions procedure.

Table five

The predictive power of all admissions criteria on each response from the Mentor Evaluation After Defense force.

	Classwork	Bulldoze	Experimental Blueprint	Technical Power	Reading Literature	Output	Writing	Leadership	Trajectory	Overall
GRE Quantitative	-0.06	0.02	-0.07	-0.09	-0.01	-0.01	-0.04	0.03	0.04	0.00
	[-0.22, 0.09]	[-0.15, 0.19]	[-0.24, 0.ten]	[-0.25, 0.07]	[-0.xviii, 0.15]	[-0.18, 0.xv]	[-0.xx, 0.12]	[-0.14, 0.19]	[-0.13, 0.21]	[-0.17, 0.xvi]
GRE Verbal	-0.29***	0.10	-0.01	0.xiii	-0.17*	-0.04	-0.17*	0.00	0.02	0.04
	[-0.43, -0.14]	[-0.06, 0.26]	[-0.17, 0.15]	[-0.02, 0.29]	[-0.32, -0.01]	[-0.20, 0.12]	[-0.32, -0.01]	[-0.15, 0.xvi]	[-0.xiv, 0.18]	[-0.12, 0.20]
GRE Writing	-0.03	-0.06	0.07	-0.03	0.01	-0.12	-0.03	-0.17*	-0.08	-0.05
	[-0.18, 0.eleven]	[-0.22, 0.10]	[-0.09, 0.23]	[-0.18, 0.12]	[-0.15, 0.16]	[-0.28, 0.04]	[-0.xviii, 0.xiii]	[-0.33, 0.01]	[-0.23, 0.08]	[-0.21, 0.10]
Undergraduate GPA	-0.15*	0.02	-0.04	-0.10	-0.10	-0.03	-0.sixteen*	-0.07	-0.04	-0.09
	[-0.29, -0.01]	[-0.xiii, 0.17]	[-0.19, 0.10]	[-0.24, 0.04]	[-0.25, 0.04]	[-0.eighteen, 0.12]	[-0.30, -0.01]	[-0.22, -0.08]	[-0.nineteen, 0.11]	[-0.24, 0.06]
Undergraduate Inst. Selectivity	0.15*	0.01	0.18*	0.11	0.ten	0.x	0.ten	0.04	0.14	0.17*
	[0.01, 0.29]	[-0.14, 0.17]	[0.03, 0.33]	[-0.04, 0.26]	[-0.05, 0.25]	[-0.05, 0.25]	[-0.04, 0.25]	[-0.eleven, 0.19]	[-0.02, 0.29]	[0.02, 0.33]
Prior Advanced Caste	0.26	0.47	0.21	0.73*	0.16	0.21	0.25	0.78*	0.49	0.60
	[-0.36, 0.88]	[-0.21, 1.15]	[-0.46, 0.88]	[0.07, one.38]	[-0.50, 0.83]	[-0.46, 0.88]	[-0.39, 0.89]	[0.xi, one.44]	[-0.18, 1.16]	[-0.07, 1.26]
Underrepresented Minority	0.11	0.04	0.36	0.forty	0.00	0.03	0.twoscore	0.44	0.24	0.21
	[-0.39, 0.62]	[-0.52, 0.59]	[-0.nineteen, 0.90]	[-0.xiii, 0.93]	[-0.54, 0.54]	[-0.52, 0.57]	[-0.13, 0.92]	[-0.11, 0.98]	[-0.31, 0.78]	[-0.34, 0.75]
International	0.44	-0.13	0.22	0.twoscore	0.70*	-0.06	0.21	-0.19	-0.05	0.xvi
	[-0.18, ane.07]	[-0.81, 0.56]	[-0.45, 0.89]	[-0.26, 1.05]	[0.03, 1.37]	[-0.73, 0.62]	[-0.44, 0.85]	[-0.86, 0.48]	[-0.72, 0.63]	[-0.51, 0.83]
Female person	0.00	0.01	0.26	0.14	0.17	0.00	-0.xv	-0.03	0.13	0.17
	[-0.26, 0.27]	[-0.28, 0.30]	[-0.03, 0.55]	[-0.14, 0.42]	[-0.12, 0.45]	[-0.29, 0.29]	[-0.43, 0.12]	[-0.32, 0.25]	[-0.xvi, 0.42]	[-0.11, 0.46]
R-Squared	0.15	-0.03	0.02	0.06	0.02	0.00	0.09	0.01	0.01	0.01
Observations	210	210	210	210	210	210	210	210	210	210

Some admissions decisions are made according to GRE percentiles, so all analysis were repeated with GRE percentiles and showed qualitatively similar results as with the GRE raw scores.

Discussion

This analysis is designed to assist admissions committees who are responsible for evaluating candidates for positions in biomedical research graduate programs. The overall result of this report is that there is little objective information in the application to reliably identify future outstanding performers in research.

Data from Vanderbilt Medical School'south IGP reveal that few of the currently used objective criteria for access demonstrate high levels of predictive validity for measures of progress in the program or research productivity. Chiefly, the GRE provides no insight into such of import graduate teaching measures as passing the qualifying exam, graduating with a Ph.D., time to defence force, number of presentations, number of first author publications, or winning an individual grant or fellowship.

When examining classroom functioning, GRE Quantitative and Verbal scores moderately predict first semester grades, and the GRE Verbal subtest minimally predicts graduate GPAs after accounting for other observable components of the applicant. The human relationship betwixt GRE scores and graduate school grades could be due to the GRE exam's measuring characteristics such as examination taking skills, attention, time management, stress management, test question comprehension, and reviewing one's work. These skills likely overlap with the ability to succeed on graduate grade exams and trouble sets, and differ from the disquisitional thinking, experimental blueprint, and writing skills needed for the qualifying test, research productivity and other measures of graduate school success. ETS [seven] and Kuncel [v] argue that the GRE assesses cognitive skills and bookish knowledge that relate to graduate school research ability, all the same, our results do not support such theories." We also notation that didactic coursework takes place in the first two years of graduate school whereas the other measures are captured more two years subsequently a student completed the GRE. The less time that passes between measures, the stronger the relationship [26, 27].

Interestingly, the GRE does moderately predict some elements in kinesthesia evaluations of recently graduated students, which often occur over vi years after the completion of the GRE. The near consistent pattern was found among GRE Exact scores, which moderately predict faculty ratings of how well students handle classwork and minimally predict keeping upwards with literature, supporting our earlier finding that the GRE predicts success in the classroom. GRE Verbal scores also minimally predict writing ability, a highly verbal skill, yet writing ability does not appear to translate to the number of published start-author papers or a successful dissertation defense. Faculty had access to students' GRE scores, which could have biased their responses, however, GRE Quantitative scores did not predict any elements of the evaluation, suggesting that kinesthesia are not using this information when assessing their students. GRE subtests did not predict drive, experimental design, output, trajectory, or faculty evaluations of overall productivity as a scientist, suggesting that the GRE is more closely aligned with classroom beliefs than laboratory operation.

Coursework is a traditional component of graduate education as currently performed in the United States. However, didactic courses are merely useful preparatory steps along the way to more of import aspects of research training, namely the evolution of creative skills and technical abilities, time to degree, and productivity in terms of written and published materials. Thus, to the question of whether GRE scores tin can help guide usa to select individuals with these research-specific skills: the answer is that they exercise not.

Variables other than the GRE are better predictors of graduate educatee success. Undergraduate GPA is a stronger predictor of graduate GPA, commencement semester grades, and graduating with a Ph.D. than GRE scores. Moreover, all of the objective admissions criteria explicate but a small portion of the variance observed in most outcomes, meaning the access criteria are missing many critical components of students' success. Some of those components may exist gleaned from letters and the personal statement. A new study reveals that letters of recommendation predict first author publication counts [28]. Admissions committees might consider placing more weight on these criteria instead of on GRE scores, depending on the outcome measures deemed most important in their program.

Underrepresented minorities were more probable to obtain individual grants or fellowships, possibly due to a number of variety fellowships that are only available to this population. Although underrepresented minorities had lower first semester grades, GPAs, and lower odds of graduating with a Ph.D., with academic and social supports, undergraduate science and engineering programs have been shown to improve minority graduation rates [29,30], a finding supported past preliminary data on our ain graduate population. On most other measures, underrepresented minority status had no meaningful touch, revealing that neither underrepresented minority condition nor GRE scores predicted who would be a productive scientist. The key take-away is to motion away from using GRE scores in admissions decisions, every bit they have little value in predicting success in the biomedical inquiry enterprise, and may in fact run counter to the goal of diversifying the biomedical enquiry workforce.

Chiefly, these findings are limited to but the students admitted to and enrolled in Vanderbilt's IGP and do not include all applicants since we cannot observe outcomes on students who did not enroll. This is a mutual source of bias that exists in near all predictive validity studies of standardized tests used in admission processes. Since we do not discover the outcomes of students who did not enroll, we must assume the predictive validity of GRE scores on matriculants is similar to what it would have been for applicants who were not admitted or did not choose to enroll. Furthermore, our study does not include a random sample of the entire range of GRE scores. The students inbound graduate school at Vanderbilt have been chosen by traditional criteria that rely on conventional wisdom that there is decreased performance below a certain GRE score. As such, the sample of students has GRE Exact and Quantitative scores roughly 100 points higher than the national boilerplate for each subtest [31].

In summary, our recommendations are not radically different from those of ETS who urge that GRE test scores not be the sole czar of admissions to graduate programs. Nevertheless, nosotros would go one step further, at least for applications to graduate schoolhouse in biomedical sciences research, and propose that these standardized tests are unlikely to provide the of import information needed to determine success in graduate school. For Vanderbilt's IGP, GRE scores are mainly valid every bit predictors of performance in didactic coursework, only non for any other important measures of success in graduate school such equally graduating with a Ph.D. or research productivity. Importantly, given the racial and socioeconomic differences in test functioning, a strong reliance on GRE scores for admissions may negatively impact specific groups of students and could reduce the variety of students in a program. The limited benefits of the GRE practice not outweigh the potential costs of excluding minority and low socioeconomic condition applicants.

Supporting Information

S1 Supporting Data

Details regarding the sample and additional results tables.

(DOCX)

Acknowledgments

The authors greatly acknowledge Lindsay Meyers, Nadia Ehtesham, and Carolyn Berry for assistance in gathering and organizing educatee data.

Funding Statement

This study was funded in part past the National Institutes of Health R25GM062459.

Data Availability

In gild to protect pupil privacy, data cannot be made publicly available. All interested and qualifying researchers will be able to access the information upon request. Data requests may be made by contacting the authors or the the Vanderbilt Biomedical Research Instruction and Training (BRET) Office: The Function of Biomedical Research Teaching & Preparation, Vanderbilt University, Schoolhouse of Medicine, 340 Lite Hall, Nashville, TN 37232-0301, 615-343-4611 (Main Number).

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226333/

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