The Quartile Index Calculation for the Leading Russian Universities and its Pairwise Correlations with Other Scientific Metrics

Moskovkin, Vladimir M., Reznichenko, Oleg S., Peresypkin, Andrey P. and Doborovich, Anna N. The Quartile Index Calculation for the Leading Russian Universities and its Pairwise Correlations with Other Scientific Metrics. Informology, 2022, vol. 1, n. 2, pp. 11-26. [Journal article (Paginated)]

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English abstract

The historical survey of the foundation and development of the quartile index in different fields of knowledge, as well as that one of the percentile index in scientometrics, was conducted. Based on the improved quartile index introduced in scientometrics by A.V. Grinev, the calculations of three types of such indices, based on the CiteScore, SNIP and SJR impact factors for 45 leading Russian universities at the levels of 2019 and 2020, were carried out. The Scopus articles published in quartile-free journals were included into the calculations. The proportions of Scopus articles published in Q1 and Q2 journals were also calculated. Pairwise correlations were calculated between six indicators supplemented by the h-index. The best results had the pairwise correlations of the Quarterly Indices and their pairwise correlations with the h-index. The worst results were obtained in pairwise correlations between Quarterly Indices and Scopus articles proportions in Q1 and Q2 journals. Despite the fact that the quartile index has great advantages over the h-index and other h-like indices, since it covers the entire range of publications of the author or another subject of publication activity, as well as the qualitative structure of publications based on their distribution by quartiles, it showed a good correlation with the h-index (in different experiments the values of the Pearson correlation coefficient between these indices varied from 0.81 to 0.85.

Item type: Journal article (Paginated)
Keywords: Quartile indices, Percentile indices, Scientometrics, Publication activity, CiteScore
Subjects: B. Information use and sociology of information > BB. Bibliometric methods
Depositing user: Dr. Alireza Noruzi
Date deposited: 12 Aug 2023 09:19
Last modified: 12 Aug 2023 09:19
URI: http://hdl.handle.net/10760/44364

References

Bornmann, L. (2010). Towards an ideal method of measuring research performance: Some comments to the Opthof and Leydesdorff (2010) paper. Journal of Informetrics, 3(4), 441-443. https://doi.org/10.1016/j.joi.2010.04.004

Bornmann, L. (2013). How to analyze percentile citation impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes, and top‐cited papers. Journal of the American Society for Information Science and Technology, 64(3), 587-595. https://doi.org/10.1002/asi.22792

Bornmann, L., Tekles, A., & Leydesdorff, L. (2019). How well does I3 perform for impact measurement compared to other bibliometric indicators? The convergent validity of several (field-normalized) indicators. Scientometrics, 119(2), 1187-1205. https://doi.org/10.1007/s11192-019-03071-6

Calderón, C., & Chong, A. (2004). Volume and quality of infrastructure and the distribution of income: An empirical investigation. Review of Income and Wealth, 50(1), 87-106. https://doi.org/10.1111/j.0034-6586.2004.00113.x

Crum, W. L. (1939). Corporate size and earning power. Harvard University Press.

Grinev, A.V. (2019). Publication Activity of the Leading Russian Historians in Scopus Database and Quartile Index. KLIO, 11(155): 36–47. (In Russian).

Grinev, A. V. (2020, September). The disadvantages of using scientometric indicators in the digital age. In IOP Conference Series: Materials Science and Engineering (Vol. 940, No. 1, p. 012149). IOP Publishing. https://doi.org/10.1088/1757-899X/940/1/012149

Hulte, C.R. (1966). Infrastructure Capital and Economic Growth: How Well You Use It May Be More Important than How Much You Have. NBER Working Paper 5874. Cambridge, United States: National Bureau of Economic Research. 37 p.

Kempton, R. A., & Taylor, L. R. (1976). Models and statistics for species diversity. Nature, 262(5571), 818-820. https://doi.org/10.1038/262818a0

Leydesdorff, L., & Bornmann, L. (2011). Integrated impact indicators compared with impact factors: An alternative research design with policy implications. Journal of the American Society for Information Science and Technology, 62(11), 2133-2146. https://doi.org/10.1002/asi.21609

Leydesdorff, L., Bormann, L., & Adams, J. (2018). A Non – parametric Alternative to the Journal Impact Factor. https://arhiv.org/abs/1812.03448

Leydesdorff, L., Bornmann, L., Mutz, R., & Opthof, T. (2011). Turning the tables on citation analysis one more time: Principles for comparing sets of documents. Journal of the American Society for Information Science and Technology, 62(7), 1370-1381. https://doi.org/10.1002/asi.21534

Moskovkin, V. M. (2021). The Quartile index in scientometrics. Automatic Documentation and Mathematical Linguistics, 55(4), 166-168. https://doi.org/10.3103/S0005105521040063

Pitz, G.F. (1974). Subjective probability distribution for imperfectly known quantities. In L.W. Gregg (Ed.). Knowledge and cognition. New York: Wiley.

Seglen, P. O. (1992). The skewness of science. Journal of the American Society for Information Science, 43(9), 628-638. https://doi.org/10.1002/(SICI)1097-4571(199210)43:9%3C628::AID-ASI5%3E3.0.CO;2-0


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