Dan-Maniu DuĹ&#x;e, Carmen-Sonia DuĹ&#x;e and Cristina FeniĹ&#x;er The examination of the diagram shows that the first two categories with the highest frequencies are RESEARCH and STUDENTS. They play an important role in identifying the patterns of the candidates' management programs for the most important function in universities. After applying the vocabulary list in the text, the statistical processing of the data uses two analysis techniques varied by interdependence: hierarchical cluster analysis and multidimensional scaling. Cluster analysis technique is based on the grouping algorithm, given by a simple rule of joining, in successive steps, of the similar categories. Its results are displayed as a diagram that resembles a tree type chart. It shows how the groups join at every step according to the distance between them. The dendrogram examination highlights the presence of four clusters of categories formed by the cooccurrence of the keywords in the text. The first cluster contains seven categories: communication, research, development, education, quality, performance, teachers and students. The second category includes labour market and society. The next cluster of the dendrogram includes two content categories: financing and objectives. And the last one includes mission and vision.
Dendogram 1: The structure of the content categories As the cluster analysis suggests the existence of structures that may be interesting for the further analysis of the similarity matrix, we have used the non-metric technique of the multidimensional scaling, which applies to the matrix of the co-occurrence frequency of the words in the text. It produces a map of the relative positioning of the words groups in a three-dimensional space. It is based on comparisons between the content categories through the objective dimensions of the geometric distance between them. Guttman-Lingoes' smallest space computed for 3 dimensions (semi-strong-monotonicity). Guttman-Lingoes' Coefficient of Alienation = 0.07779 after 56 iteration(s). Kruskal-Guttman-Lingoes-Roskam smallest space coordinates in 3 dimensions (weak monotonicity):
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