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I presents a new method called Hybrid DISTATIS that can be applied to the analysis of sorting task and object characteristics data. Hybrid DISTATIS allows to project the object, the characteristics, and the assessor for each object in a map, which is a combining Principal Component Analysis Biplot (PCA Biplot) and DISTATIS method. In these maps, the proximity between two objects or assessor points reflects their similarities, the proximity between characteristic and axis vector reflects their correlations, and therefore these maps can be read using the same rules as standard metric multidimensional scaling (MDS) and PCA Biplot methods. Technically, Hybrid DISTATIS started by transforming the individual sorting task data into crossproduct matrices as in classical MDS and evaluating the similarity between these matrices initially. Computes a compromise matrix which is the best aggregate of the individual crossproduct matrices, than analyzes it with PCA. After that computes a column effect matrix as a characteristic coordinates with PCA Biplot. The individual matrices and characteristic coordinates are then projected onto the compromise space. The quality of Hybrid DISTATIS map obtained based on the eigenvalue cumulative percent of the compromise matrix. In this paper, the application using sorting task from the college ranks in 2010 is presented, which is published on a website by Webometrics, 4ICU, and QS, as an assessor and the college that became the objects are ITB, ITS, IPB, Unair, Undip, UGM, UI, and Unpad, with the characteristics variable are the number of students and the accreditation values.
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