Ranking the Airports with Data Envelopment Analysis and Canonical Correlation Analysis

Ozturk E., BAL H.

GAZI UNIVERSITY JOURNAL OF SCIENCE, vol.30, no.2, pp.237-245, 2017 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 2
  • Publication Date: 2017
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.237-245
  • Gazi University Affiliated: Yes


This paper deals with joint use Canonical correlation analysis (CCA) and Data envelopment analysis (DEA) techniques. CCA is a multivariate statistical technique that can be used to determine the relationship between two multiple variable sets. DEA is a nonparametric approach for measuring the relative efficiency of peer decision making units(DMUs) when multiple inputs and outputs are present. Data envelopment analysis (DEA) model selection is problematic. The estimated efficiency for any DMU depends on the inputs and outputs included in the model. A benefical method for model selection is proposed in this paper. Efficiencies are calculated for all possible DEA model specifications. The results are analysed using Canonical Correlation Analysis. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The aim of this study is to get an effective result by using the CCA for the correct model choice in DEA. For this purpose, data set of airports in Turkey were used. The correlation calculations are carried out to understand the nature of the relationship between the models of DEA. It is aimed to find the most effective DEA model by using CCA technique.