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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]