当前位置: X-MOL 学术Scientometrics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Complex networks for benchmarking in global universities rankings
Scientometrics ( IF 3.5 ) Pub Date : 2020-07-31 , DOI: 10.1007/s11192-020-03637-9
Esteban Fernández Tuesta , Máxima Bolaños-Pizarro , Daniel Pimentel Neves , Geziel Fernández , Justin Axel-Berg

Finding a set of units that can serve as a reference for growth or improvement in positions within a ranking is not a simple task, since each ranking method can place the same unit in different positions and may even differ in relative position within the ranking. This study proposes a method that applies a combination of network analysis and efficiency methods to global university rankings. Complex networks allow the creation of a graph structure that selects a set of units that change positions in consecutive rankings and also the selection of the set of nodes that are linked with a selected node. For this new set, it is possible to calculate the efficiency level using Data Envelopment Analysis (DEA), from which the benchmarks of the indicators for each of the selected universities can be computed. The purpose of this paper is to develop a methodology to find a set of universities that compete with any university selected from those in the global university rankings, in particular ARWU, THE and QS. Moreover, this work also proposes to estimate the efficiency level of each university that competes with the selected university using the Data Envelopment Analysis methodology in order to establish benchmarks for each of the target Universities. This methodology is replicable for any university in any ranking or set of rankings. Given the high volatility of rankings, this process can serve university policy makers in selecting indicators to focus on for improved results in the short term.

中文翻译:

用于全球大学排名基准的复杂网络

找到一组可以作为排名中位置增长或改进的参考的单元并不是一项简单的任务,因为每种排名方法都可以将相同的单元放在不同的位置,甚至在排名中的相对位置也可能不同。本研究提出了一种将网络分析和效率方法相结合的方法,用于全球大学排名。复杂网络允许创建图形结构,该结构选择一组在连续排名中改变位置的单元,以及选择与所选节点链接的一组节点。对于这个新集合,可以使用数据包络分析 (DEA) 计算效率水平,从中可以计算出每所选定大学的指标基准。本文的目的是开发一种方法,以找到与从全球大学排名中选出的任何大学(尤其是 ARWU、THE 和 QS)竞争的大学。此外,这项工作还建议使用数据包络分析方法估计与所选大学竞争的每所大学的效率水平,以便为每个目标大学建立基准。这种方法可以在任何排名或一组排名中复制​​到任何大学。鉴于排名的高波动性,这一过程可以帮助大学决策者选择指标,以在短期内提高结果。此外,这项工作还建议使用数据包络分析方法估计与所选大学竞争的每所大学的效率水平,以便为每个目标大学建立基准。这种方法可以在任何排名或一组排名中复制​​到任何大学。鉴于排名的高波动性,这一过程可以帮助大学决策者选择指标,以在短期内提高结果。此外,这项工作还建议使用数据包络分析方法估计与所选大学竞争的每所大学的效率水平,以便为每个目标大学建立基准。这种方法可以在任何排名或一组排名中复制​​到任何大学。鉴于排名的高波动性,这一过程可以帮助大学决策者选择指标,以在短期内提高结果。
更新日期:2020-07-31
down
wechat
bug