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Functional principal components analysis on the example of the achievements of students in the years 2009-2017

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Sztemberg-Lewandowska M.

Econometrics. Ekonometria. Advances in Applied Data Analytics

2019

issue vol. 23 no. 4

16-29

The functional principal components analysis joins the advantages of the principal components analysis and provide analysis of dynamic data. The main difference in both methods is the type of data the PCA is based on multivariate data, whereas the FPCA on the functional data including curves and trajectories, i.e. a series of individual observations, not a single observation, as usual. The functional principal components analysis with functional data, will be used in the analysis. This method allows the analysis of dynamic data. The purpose of the article is to apply of functional principal components analysis to the problem of student’s achievements. The article was compared the level of students' knowledge during different stages of education in 2009-2017. The analysis covers the average exam results after the II, III and IV stage of education.

Polish universities of economics in European networks

51%

Sagan A., Brzezińska J., Sztemberg-Lewandowska M., Pełka M.

Econometrics. Ekonometria. Advances in Applied Data Analytics

2021

vol. 25

issue 1

91-111

In recent years, the evaluation of research conducted in European universities has become a significant problem. The growing concern for the quality and evaluation of research conducted at universities highlights the importance of university rankings, especially global rankings. The aim of the paper is to identify the network system of Polish universities of economics among their European counterparts belonging to the same networks, and indicate the positions of Polish universities within these networks. The study used a network approach to analyse the connections of European universities using university networks. The networks enable the visualization of complex, multidimensional data and provide statistical indicators for interpreting the resultant graphs. The analysis is exploratory in its nature and uses visualisation techniques of social network analysis (SNA), multidimensional scaling (MDS), principal component analysis (PCA), and Eigen-model network analysis (ENA). The analysis covered 150 universities of economics in Europe and 11 university networks. Network analyses were performed with the R program. The paper presents different methods that allowed for the identification of network systems of Polish economic universities within the networks of European universities. An analysis of the social networks based on network indicators was also included.

Ostatnio dużym problemem stała się ocena badań prowadzonych na europejskich uczelniach. Troska o jakość i ocenę badań naukowych prowadzonych na uczelniach zwiększa znaczenie rankingów uczelni, zwłaszcza rankingów światowych. W artykule zastosowano podejście sieciowe do analizy powiązań europejskich uniwersytetów korzystających z sieci uniwersytetów. Sieci umożliwiają wizualizację złożonych, wielowymiarowych danych i zapewniają wskaźniki statystyczne do interpretacji wynikowych wykresów. Analiza obejmuje 150 uczelni ekonomicznych w Europie i 11 sieci uniwersytetów. Analizy sieciowe wykonano programem R. W artykule przedstawiono różne metody, które pozwoliły na identyfikację systemów sieciowych polskich uczelni ekonomicznych na uczelniach europejskich, oraz sieci społecznościowych na podstawie wskaźników sieciowych.

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2 Econometrics. Ekonometria. Advances in Applied Data Analytics

2 Sztemberg-Lewandowska M.

1 Brzezińska J.

1 Pełka M.

1 Sagan A.

1 2021

1 2019

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Functional principal components analysis on the example of the achievements of students in the years 2009-2017

Polish universities of economics in European networks