Applying Data Driven Decision Making to rank Vocational and Educational Training Programs with TOPSIS

TL;DR

This paper develops a data-driven approach using TOPSIS and a novel decision support method to rank vocational and educational programs based on detailed labor and study data, enhancing decision-making accuracy.

Contribution

It introduces a new decision support method for criterion influence assessment in multi-criteria ranking, applied to vocational program evaluation.

Findings

The new method provides a robust influence assessment of criteria.

Comparison shows the new method's effectiveness over traditional sensitivity analysis.

Application to Spanish data demonstrates practical utility.

Abstract

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.