In data analyses based on a deterministic or stochastic approach, using pre-study is very important to identify observations that are not suitable to data in general. Among such observations, those that have a high tendency to change results negatively are called influential observations. In this paper, we propose a new method to identify influential observations in Data Envelopment Analysis (DEA). Our method is a modified version of the one proposed by Pastor et al. . Both methods are compared by using two well-known data sets and the outcomes are discussed. A comparative analysis indicates that our method is an effective alternative to the Pastor et al.  method to identify influential observations in DEA.