Russian universities in terms of performance indicators

M. Astafyeva (), O. Zyateva (), I. Peshkova () and E. Pitukhin ()

University Management: Practice and Analysis, issue 4

Abstract: The article is devoted to the analysis of performance indicators for Russian universities that were officially published by the Ministry for Education and Science of the Russian federation in 2014. Analysis results will be used for forecasting performance indicators meanings. Planned research consists of several stages: forming performance indicators history; statistical and comparative data analysis; construction of performance indicators calculation model considering factors of influence in the previous history; calculation of performance indicators perspective according to previously obtained models. The research covers many Russian higher education institutions and the topic of the research is performance indicators. The aim of this stage is to conduct statistic and comparative analysis on research objects and their segmentation into clusters. We calculated and analyzed average meanings for performance indicators in different aspects: regional, federal areas, university groups and other characteristics of educational institutions (type, status, branch affiliation, legal structure, etc.) and also received other statistical evaluations. Research results demonstrate that average meaning for performance indicators are not informative because of significant standard deviation. In order to get more reliable evaluation and identify higher educational institutions with similar performance criteria we conducted cluster grouping using the methods of ^-average and self-organizing Kokhonnen maps in which significant factors include 5 main performance indicators (educational, academic, international, financial and infrastructural activities). According to the results of the analysis universities were subdivided into seven clusters, 4 of which represent average groups covering 98% of the total university number and 3 others feature universities with extreme performance indicators. Of all the cluster 2 "positive" were chosen (contain 48% of overall organization number) with universities with good and excellent marks in which percentage of universities successfully passed performance efficacy monitoring is higher than average in Russia. These clusters have high performance indicators for educational and financial activities. University management can make conclusions about the state of performance indicators by defining to which cluster their university belongs. Presented results of statistic performance indicators processing can be useful in conducting self evaluation, including evaluation of its position in terms of these indicators as compared to other educational institutions in different aspects, defining chances for successfully passing performance efficacy monitoring as well as to executive authorities making managerial decisions concerning higher education. Research results will form the basis for mathematic models forecasting dependence of efficacy performance indicators on influence factors both for educational institutions and external environment.

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