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This review presents the first comprehensive synthesis of available research on selection methods for STEM graduate study admissions. Ten categories of graduate selection methods emerged. Each category was critically appraised against the following evaluative quality principles: predictive validity and reliability, acceptability, procedural issues, and cost-effectiveness. The findings advance the field of graduate selective admissions by (a) detecting selection methods and study success dimensions that are specific for STEM admissions, (b) including research evidence both on cognitive and noncognitive selection methods, and (c) showing the importance of accounting for all four evaluative quality principles in practice. Overall, this synthesis allows admissions committees to choose which selection methods to use and which essential aspects of their implementation to account for.

In this study, we investigated the currently applied selective admission criteria and tools of the two-year research master's programs of both the Graduate Schools of Life Sciences and Natural Sciences of Utrecht University (the Netherlands). In addition, we evaluated their transparency to applicants. Both admissions staff members and applicants participated. To determine admission criteria that are important for admission decisions, we ranked 51 admission criteria and, on their basis, combined into six domains: academic background, grades, cognitive ability, research background, personality and personal competencies, motivation factors. To evaluate transparency, we contrasted the perceptions of applicants with the actual importance of admission criteria, as reported by admission staff members. We found that admissions criteria related to personality and personal competencies are less important in admission decisions than criteria related to grades, academic background and motivation. The applicants find the admissions decisions transparent to a moderate degree. This study also revealed that selectors use criteria and tools both with and without predictive value for later graduate performance. Moreover, some of the currently applied admission instruments might be prone to admission biases. We advocate selectors to use admission criteria and tools that are evidence-based, resistant to admission biases, and transparent to the applicants.

Graduate admissions committees in Europe have a challenging task of selecting students from an increasingly large pool of candidates with diverse application files. Graduate standardized testing can ease the comparison of application files. The purpose of this study was to examine whether the Graduate Record Examinations (GRE) is predictive of several dimensions of graduate success on English-taught research masters' programs in science, technology, engineering, and mathematics at a large European university. The data from 167 masters' students were collected. Hierarchical regression analyses were conducted. All GRE scales predicted Graduate Grade Point Average. Individual GRE scales predicted internship grade and supervisors' assessments of students' research performance and the content of their research report. None of the individual GRE scales predicted supervisors' assessments of students' practical skills, but the three GRE scales taken together improved the explanatory power of the model. The structure and style of students' research reports was not predicted by the GRE. All relationships were held after accounting for socioeconomic status. Overall, the GRE appeared as a reasonable predictor of graduate study success. Both the benefits and drawbacks of the implementation of the GRE in European masters' programs are discussed, as well as the legal limitations. [ABSTRACT FROM AUTHOR]

In the face of increasing and diversifying graduate application numbers, evidence‐based selective admissions have become a pressing issue. By conducting multilevel regression analyses on institutional admissions data from a Dutch university, this study aims to determine the predictive value of undergraduate academic indicators for graduate study success on research masters' programs in the life sciences. The results imply that in addition to undergraduate grade point average, undergraduate thesis grade is a valid predictor of graduate grade point average. To a small extent, the examined undergraduate academic indicators also predict graduate degree completion and time to degree. The results from this study can be used by admissions committees for evaluating and improving their current practices of graduate selective admissions. Practitioner points: There is substantial scientific evidence that undergraduate grade point average (UGPA) is a valid predictor of certain dimensions of graduate study success. This paper adds to this evidence by showing that undergraduate thesis grade is also a valid predictor of graduate grade point average (GGPA).The predictive power of the type of prior higher education institution for the examined dimensions of graduate study success is small at best.Undergraduate academic indicators are better predictors of GGPA than of graduate degree completion or time to degree.The results of this study can be used for improving admissions decision‐making at graduate schools, especially ones with a research‐oriented curriculum. [ABSTRACT FROM AUTHOR]