ANALYSIS OF INDICATORS FOR ASSESSING THE EFFICIENCY OF STRUCTURAL SUBDIVISIONS OF THE UNIVERSITY
Abstract
The task of the authors was to rank the factors that are used to assess the rating of the structural units of the University. The authors define and describe the stages of ranking. The statistical analysis of data structural units, Moscow Сity University and Рeople's Friendship University of Russia in 2017. Significant factors were selected on the basis of the data of the Moscow State Pedagogical University and the PFUR separately, and then they were compared. The resulting numerical index of structural units evaluation is proposed. With the help of correlation analysis, the data were first systematized and internal connections were revealed. Next, an analysis of the multicollinearity of vectors was carried out using the correlation matrix. As a result of the study, significant factors affecting the rating of the structural unit were selected. The interpretation of the parameters of the model showed that an increase by one such parameter as "the ratio of the number of protected applicants and graduate students to the number of graduates" leads to an increase in the "rating of the relevant Department of the University" by an average of 0,696 units. Such analysis is carried out for each indicator of work of divisions which participate in the General assessment of activity of University. The average of the Hirsch index has the greatest impact on the rating of the division.
Verification of the model was carried out with the help of indicators of structural divisions of PFUR. The most significant contribution to the model is given by the parameter "Number of publications in journals included in the WAC list". This factor is comparable to the significant factor of the regression model in terms of MCU ("average Hirsch index").
Comparing the results of the analysis of structural divisions of different universities, it can be concluded that the factors that have the greatest and least impact are the same. Built standard was applied to split the departments of the PFUR on two groups of "effective" and "ineffective."
Thus, using the current statistics of the University, the basis for the development of a system for supporting the adoption of managerial decisions aimed at improving the rating of the university was created.
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