Instruments for the analysis of orders based on multi-criteria methods. Application to Iberoamerican universities

Abstract

Traditional multi-criteria decision-making methods can be adapted to uncertainty scenarios, allowing for rankings even when some model requirements are imprecise. In this work, we demonstrate another use of these methods: the Unweighted TOPSIS (UW-TOPSIS) method will be used to detect anomalies in rankings made with precise data. By applying UW-TOPSIS, each alternative is associated with an interval indicating its worst and best overall evaluation position in the ranking. To extract useful information from this range of possibilities, we introduce the intervention coefficient, which measures the similarity between the position of an alternative and its best possible situation.

The proposal is applied to the ranking of Ibero-American universities (including Andorra, Spain and Portugal) published by SCImago IBER for the five-year period 2018-2022. Different levels detected by the intervention coefficient are studied, and an alternative ranking method that better fits the data from the database itself is proposed.