World Bank data shows that education accounts for, on average, 13% of government expenditure in the world, effective spending being a priority. Position in international academic rankings has been a universally accepted, yet criticized, criterion of institutional effectiveness. No consistent positive correlation was revealed during research on how the size of government subsidies affected university ranking. Assessment methodology is adjusted to study the influence of public funding mechanisms on university ranking. Three mechanisms are investigated: formula based funding, performance based funding, and negotiated funding. The sample includes 107 European universities from 27 countries. For each of them, information on the funding model (or a combination of models), total annual revenue, proportion of public subsidies, ranking and ranking movements over the last decade is collected. Analysis results are used to group universities into two major categories: low-ranking universities (ranked in the top 200–500), which are mostly funded using formal mechanisms (formula- and performance based funding), and high-rankings universities (the top 100), which largely use the negotiated funding model (either alone or combined with formal models). This confirms previous research findings that the size of government subsidies has no impact on university ranking. A qualitative analysis of higher education funding patterns in Russia is performed. Formalization of all sources of university funding has become a major trend, yet this empirical study demonstrates that prioritization of formal criteria may be ineffective if Russian universities want to reach their ambitious goals of making it to the top 100 in international rankings.
The contemporary marketing practices methodology (CMP) attracts attention of a substantial number of researchers in the field of strategic marketing. In the past two decades there were more than fifty papers published in peer-reviewed outlets addressing the analytics of usage of contemporary marketing practices in a variety of countries and industries. In this note we discuss reliability of these studies with respect to the usage of specific analytic tools. First, we demonstrate that standard clustering analysis is relatively sensitive to small changes in the datasets with companies being assigned to different clusters at frequent rates. Second, the project national teams make use of different, often incompatible settings. Therefore, to make possible comparisons between the countries and across industries, the researchers must agree on a generic setup and procedures. We conclude the note sketching the basics of these common grounds.
We present a complex analysis of business models for large, medium and small Russian commercial banks from 2006 to 2009. The Russian banks are grouped based on homogeneity criteria of their financial and operational outcomes. The banks’ structure of assets and liabilities, profitability and liquidity ratio are taken into account. The results show how the banks are adjusted their business models before and after the financial turmoil taken place in 2008. In addition, the prevailing banking business models observed for the leading banks in Russia are defined. The banks often changing their business models are found and analyzed.
In article possible approaches to clustering of large city schools of the Russian Federation by results of their educational activity are studied. The extent to which a school has entered a particular cluster is determined by a number of objective conditions in which schools operate. Significant indicators of conditions affecting the EGE-results in schools were identified. It is shown that the studied indexes of the working conditions of schools are not sufficient for the correct clustering of schools according to the aggregated EGE-indicators.
The work continues the research of constructing methods for analyzing patterns in parallel coordinates independent of the sequence of input data of the results. The basic operations on objects of ordinal-invariant pattern clusters are described. The assertion that the centroid of an ordinal-invariant pattern cluster belongs to the original cluster is proved, which allows one to estimate the intracluster object - centroid distances in the multidimensional feature space. Examples of revealing the structural similarity of objects in parallel coordinates are given. The main differences between the methods of analysis of patterns and cluster analysis are noted. The methodology of the centroid detection of the ordinal-invariant pattern- cluster is described. An algorithm for combining groups of objects based on their structural similarity, on the one hand, and minimizing intracluster distances, on the other, is proposed, which makes it possible to improve the accuracy of the final results and partially solve the problem of finding similar objects in the presence of error in the original data. The proposed algorithm uses the concept of intracluster distances “object - centroid” and satisfies the following conditions: endogenous determination of the number and composition of the desired groups of objects under study; low (relatively) computational complexity; independence of the original partition from the initial sequence of input data. The work of the proposed algorithm on classical data sets is demonstrated. The results of testing are presented and the clustering accuracy is increased.
We present the basic properties of the a new pattern analysis method in parallel coordinates; results of the method do not depend on the ordering of data in the original sample of objects being analyzed. We prove that clusters obtained with this method do not overlap. We also show the possibility of representing objects of one cluster in the form of monotonically increasing/decreasing functions.