 The Best Academic Computer Science Depts: A Ranking Case StudyRaymond Bisdorff Chapter Chapter 13 in Algorithmic Decision Making with Python Resources, 2022, pp 165-185 from Springer Abstract: Abstract In this case study, we are solving with our Digraph3 resources a ranking decision problem based on published data from the Times Higher Education (THE) World University Rankings 2016 by Computer Science (CS) subject. Several hundred academic CS Departments, from all over the world, were ranked that year following an overall numerical score based on the weighted average of five performance criteria: Teaching (the learning environment, 30%), Research (volume, income and reputation, 30%), Citations (research influence, 27.5%), International outlook (staff, students, and research, 7.5%), and Industry income (innovation, 5%). To illustrate our Digraph3 programming resources, we shall first have a look into the THE multiple-criteria ranking data with short Python scripts. In a second section, we shall relax the commensurability hypothesis of the ranking criteria and show how to similarly rank with multiple incommensurable performance criteria of solely ordinal significance. A third section is finally devoted to introduce quality measures for qualifying ranking results. Date: 2022 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-90928-4_13 Ordering information: This item can be ordered from DOI: 10.1007/978-3-030-90928-4_13 Access Statistics for this chapter More chapters in International Series in Operations Research & Management Science from Springer |
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