Applying Data Driven Decision Making to rank Vocational and Educational Training Programs with TOPSIS
This work addresses the specific problem of program ranking for educational policymakers in Extremadura, Spain, and is incremental as it builds on existing TOPSIS methods with a new sensitivity analysis approach.
The paper tackled the problem of ranking vocational and educational training programs in Extremadura, Spain, by integrating individual and labor data into a database and using the TOPSIS method with a new decision support technique for criterion influence assessment, resulting in a multi-criteria classification for the period 2009-2016.
In this paper we present a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) during the period 2009-2016. This ranking has been carried out through the integration into a complete database of the detailed information of individuals finishing such studies together with their labor data. The multicriteria method used is TOPSIS together with a new decision support method for assessing the influence of each criterion and its dependence on the weights assigned to them. This new method is based on a worst-best case scenario analysis and it is compared to a well known global sensitivity analysis technique based on the Pearson's correlation ratio.