Validity of GRE General Test scores and TOEFL scores for graduate admission to a technical university in Western Europe^*

* The opinions expressed in this paper are those of the authors and not necessarily of ETH Zurich or of the Educational Testing Service.

ABSTRACT

Graduate admission has become a critical process in tertiary education, whereby selecting valid admissions instruments is key. This study assessed the validity of Graduate Record Examination (GRE) General Test scores for admission to Master’s programmes at a technical university in Europe. We investigated the indicative value of GRE scores for the Master’s programme grade point average (GGPA) with and without the addition of the undergraduate GPA (UGPA) and the TOEFL score, and of GRE scores for study completion and Master’s thesis performance. GRE scores explained 20% of the variation in the GGPA, while additional 7% were explained by the TOEFL score and 3% by the UGPA. Contrary to common belief, the GRE quantitative reasoning score showed only little explanatory power. GRE scores were also weakly related to study progress but not to thesis performance. Nevertheless, GRE and TOEFL scores were found to be sensible admissions instruments. Rigorous methodology was used to obtain highly reliable results.

KEYWORDS:

• Graduate record examination
• GRE
• test validity
• predicting study success
• graduate admission

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Judith Zimmermann holds a Dipl. Informatik-Ing. ETH (MSc ETH), completed the Certificate of Teaching Ability, and is now pursuing a Ph.D. at the Department of Computer Science, ETH Zurich, Switzerland. Her main interests are factors that influence study success, determining measures to quantify those factors, and elucidating the indicative value of these measures with respect to future study success. Of late, she also became interested in higher education management. Since 2006 she is responsible for organising admission at the Department of Computer Science of ETH Zurich.

Alina A. von Davier is senior research director at ETS in Princeton, NJ, U.S. and is also Adjunct Professor at Fordham University. She holds an M.S. in mathematics from the University of Bucharest, Romania, as well as a Ph.D. in mathematics from the Otto von Guericke University of Magdeburg, Germany. At ETS she leads the Computational Psychometrics Research Center, where she is responsible for developing a team of experts and a psychometric research agenda in support of next generation of assessments. Computational psychometrics, which includes machine learning and data mining techniques, Bayesian inference methods, stochastic processes, and psychometric models are the main set of tools employed in her current work. She also works with psychometric models applied to educational testing: test score equating methods, item response theory models, adaptive testing. She published several books and numerous papers in peer reviewed journals. During her tenure at ETS she also led the operational psychometric work for the international large-scale English assessments, such as TOEFL^® and TOEIC^®.

Joachim M. Buhmann leads the Machine Learning Laboratory in the Department of Computer Science at ETH Zurich. He has been a full professor of Information Science and Engineering (Informatik) since October 2003. Born in 1959 in Friedrichshafen, Germany, he studied Physics at the Technical University Munich and obtained his Ph.D. in Theoretical Biophysics under the supervision of Professor Klaus Schulten. His doctoral thesis was about pattern recognition in neural networks. He then spent three years as a research associate and assistant professor at the University of Southern California, Los Angeles. In 1991 he worked at the Lawrence Livermore National Laboratory in California. He held a professorship for practical Computer Science (praktische Informatik) at the University of Bonn, Germany from 1992 to 2003. His research interests spans the areas of pattern recognition and data analysis, including machine learning, statistical learning theory, and applied statistics. Application areas of his research include image analysis, medical imaging, acoustic processing, and bioinformatics. He serves as president of the German Pattern Recognition Society (Deutsche Arbeitsgemeinschaft für Mustererkennung) since 2009, including serving on the board during 2000–2003. He was associate editor for IEEE Transactions on Neural Networks, IEEE Transactions on Image Processing and IEEE Transaction on Pattern Analysis and Machine Intelligence.

Hans Rudolf Heinimann is a professor of Forest Engineering at ETH (1991–present). He started office as a faculty member with the former Department of Forest und Wood Research of ETH, where he was promoted to full professor in 1997. He was a visiting professor at the Forest Engineering Department of Oregon State University (1999–2000), USA; at the University of Tokyo (summer 2009), Japan, Faculty of Agriculture, and at the Australian National University in 2013, where he was with the Centre for Higher Education, Learning and Teaching (CHELT). From 2004 to 2009, he was a fellow at the Collegium Helveticum, a centre of advanced studies jointly sponsored by ETH and the University of Zurich focusing on cross-disciplinary research. Heinimann has been actively involved in his international scientific community, coordinating the ‘Forest Operations Engineering and Management’ division of the International Union of Forest Research Organizations IUFRO from 2006 to 2014. He had different positions in university bodies, such as head of department, director of studies, head of institute, head of faculty recruiting committees, etc. He was the Prorector for Education of ETH from 2007 to 2013 and is a founding member of ETH Risk Center, of which he was the chairman from 2011 to 2013. Since November 2014 he is the director of the ‘Future Resilient Systems’ research programme, which is the second programme under the ‘Singapore-ETH-Centre’ funding initiative, providing research opportunities for 40 Ph.D. students and 26 PostDoc fellows in the CREATE centre in Singapore. He is a member of several scientific and professional societies, for example, a member of the Swiss Academy of Engineering Sciences SATW. His research aims at designing and continuously improving land use policies and practices that are environmentally sound, physically feasible, economically efficient, and institutionally acceptable. Recent research work has been focusing on spatially explicit optimisation of land use activities, emerging concepts of land-use management (adaptive ecosystem management, precision land-use management), modelling environmental performance of different land-use systems, and risk management of natural hazards. At the Collegium Helveticum, he was involved in behavioural experiments, exploring the influence of emotions on risk behaviour.

Notes

* The opinions expressed in this paper are those of the authors and not necessarily of ETH Zurich or of the Educational Testing Service.