Illustrative example: a representative scenario, not a customer story
University Ranking
Develop transparent university performance rankings using research output, teaching quality, and internationalization metrics. Objective weighting eliminates bias while appraisal-based scoring provides intuitive results.
The decision problem
Twelve institutions are ranked on research, teaching and internationalisation indicators. Every published ranking is attacked at the same point: who chose the weights and why. If the weights come from a committee, the ranking inherits the committee's preferences and the argument never ends.
What the decision matrix looks like
The typical shape of a study like this. Your own matrix can be larger or smaller; nothing here is fixed.
12
Alternatives
5
Criteria
Weighting bias
Uncertainty type
| # | Criterion |
|---|---|
| C1 | Publications per faculty member |
| C2 | Citation impact |
| C3 | Student to faculty ratio |
| C4 | Graduate employment |
| C5 | International collaboration |
Which method, and why
CRITIC derives weights from the standard deviation of each indicator and its correlation with the others, so an indicator that repeats information already present carries less weight. No expert judgement enters the weighting step at all. EDAS ranks against the average institution, which reads naturally in this setting, and MARCOS repeats the ranking against ideal and anti-ideal references as a check.
What you get out
Ranking and scores
Every alternative with its score, its rank and the intermediate matrices that produced them.
Weight sensitivity
One criterion weight moves at a time, so you see exactly where the leader changes.
Method agreement
When more than one ranking method is run, the orders are compared with Spearman rho and Kendall W.
Report and citation
PDF, DOCX and XLSX output carrying the seminal source of every method used.
Run this flow on your own data
Load the matrix, choose the weighting and ranking methods, read the sensitivity.