The Comparison of Principal Component Analysis and Data Envelopment Analysis in Ranking of Decision Making Units

In this study, Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) were compared when these two methods are used for ranking Decision Making Units (DMU) with multiple inputs and outputs. DEA, a nonstatistical technique, is a methodology using a linear programming model for evaluating and ranking DMU’s performance. PCA, a multivariate statistical method, uses new measures defined by DMU’s inputs and outputs. The results of both methods were applied to a real data set that indicates the economic performances of European Union member countries and also, a simulation study was done for different sample sizes and for different numbers of input-output, and the results were examined. For both applications, consistent results were obtained. Spearman’s correlation test is employed to compare the rankings obtained by PCA and DEA.