Polish universities of economics in European networks

• Authors: Sagan Adam , Brzezińska Justyna , Sztemberg-Lewandowska Mirosława , Pełka Marcin

Title variants

PL

Polskie uniwersytety ekonomiczne w sieciach europejskich

• Languages of publication: EN

Abstracts

EN

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.

PL

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.

Keywords

EN

principal component analysis (PCA); multidimensional scaling (MDS); network analysis; European universities

PL

analiza czynnikowa (PCA); skalowanie wielowymiarowe (MDS); analiza sieci; uniwersytety ekonomiczne

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Econometrics. Ekonometria. Advances in Applied Data Analytics
• Year: 2021
• Volume: 25
• Issue: 1
• Pages: 91-111

Contributors

author

Sagan Adam

• Cracow University of Economics, Cracow, Poland

author

Brzezińska Justyna

• University of Economics in Katowice, Katowice, Poland

author

Sztemberg-Lewandowska Mirosława

• Wroclaw University of Economics and Business, Wroclaw, Poland

author

Pełka Marcin

• Wroclaw University of Economics and Business, Wroclaw, Poland

References

• Aguillo, I. F., Bar-Ilan, J., Levene, M., and Ortega, J. L. (2010). Comparing university rankings. Scientometrics, (85), 243-256.
• Bonacich, P., (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113-120.
• Bollen, K., and Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305-314.
• Bringmann, L. F. (2016). Dynamical networks in psychology: More than a pretty picture? (Doctoral dissertation). Leuven: KU Leuven.
• Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., and Ma, Y. (2015). PCANet: A Simple deep learning baseline for image classification? (IEEE Transactions on Image Processing, vol. 24, issue 12, pp. 5017-5032. Retrieved from https://arxiv.org/pdf/1404.3606.pdf
• Dominiak, P., Mercik, J., and Szymańska, A. (2012). Kapitał intelektualny w rankingach szkół wyższych. Zeszyty Naukowe Uniwersytetu Szczecińskiego, (690). Finanse, Rynki Finansowe, Ubezpieczenia, (1), 675-682.
• Edwards, J. R., and Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155-174.
• Epskamp, S., Costantini, G., Haslbeck, J., Isvoranu, A., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., and Borsboom, D. (2020). Graph plotting methods, psychometric data visualization and graphical model estimation. Package ‘qgraph’. Retrieved from https://cran.r-project.org/web/packages/qgraph/qgraph.pdf
• Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(1), 1-18. Retrieved from http://cran.r-project.org/web/packages/qgraph/index.html
• Epskamp, S., Borsboom, D., and Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195-212.
• Farré-Perdiguer, M., Sala-Rios, M., and Torres-Solé, T. (2016). Network analysis for the study of technological collaboration in spaces for innovation. Science and Technology Parks and their relationship with the university. International Journal of Education Technology in Higher Education, (13)8. https://doi.org/10.1186/s41239-016-0012-3
• Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239.
• Greenland, S., Robins, J. M., and Pearl J. (1999). Confounding and collapsibility in casual inference. Statistical Science, (14), 29-46.
• Hevey, D., (2018). Network analysis: A brief overview and tutorial. Health Psychology and Behavioral Medicine, (6)1, 301-328. Retrieved from https://www.tandfonline.com/doi/full/10.1080/21642850.2018.1521283
• Jones, P. (2020). Tools for identifying important nodes in networks. Package ‘networktools’. Retrieved from https://cran.r-project.org/web/packages/networktools/networktools.pdf
• Jones, P. J., Mair, P., and McNally, J. (2018). Visualizing psychological networks: A tutorial in R. https://doi.org/10.3389/fpsyg.2018.01742
• Kroeze, R., Van der Veen, D. C., Servaas, M. N., Bastiaansen, J. A., Oude Voshaar, R., Borsboom, D., and Riese, H. (2017). Personalized feedback on symptom dynamics of psychopathology: A proof-of-principle study. Journal of Person-Oriented Research, (3), 1-10.
• Marginson, S. (2007). Global university rankings: Implications in general and for Australia. Journal of Higher Education Policy and Management, 29(2), 131-142.
• Pearl, J. (2000). Causality: Models, reasoning and intelligent systems. London, UK: Cambridge University Press.
• Rhemtulla, M., Fried, E. I., Aggen, S. H., Tuerlinckx, F., Kendler, K. S., and Borsboom, D. (2016). Network analysis of substance abuse and dependence symptoms. Drug and Alcohol Dependence, 161(1), 230-237.
• Romero, F., and Costa, E. (2016). Social network analysis and the study of university industry relations. (11th European Conference on Innovation and Entrepreneurship, ECIE 2016). Jyväskylä.
• Valente, T., Foreman, R. (1998). Integration and radiality: Measuring the extent of an individual’s connectedness and reachability in a network. Social Networks, Volume 20, Issue 1, Pages 89-105.
• Watts, D. (1999). Networks, dynamics, and the small world problem. American Journal of Sociology, 2(105), 493-527.