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Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.01

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Date: 18. 3.2014 19:13:22

Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014) 0.01

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Date: 22. 8.2014 17:05:18

Leydesdorff, L.; Wagner, C,; Bornmann, L.: Replicability and the public/private divide (2016) 0.01

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Date: 14. 6.2016 12:01:54

Bornmann, L.: How to analyze percentile citation impact data meaningfully in bibliometrics : the statistical analysis of distributions, percentile rank classes, and top-cited papers (2013) 0.01

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Date: 22. 3.2013 19:44:17

Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019) 0.01

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Date: 8. 1.2019 18:22:45

Bornmann, L.: Complex tasks and simple solutions : the use of heuristics in the evaluation of research (2015) 0.01

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Date: 9. 7.2015 21:50:54

Leydesdorff, L.; Bornmann, L.: How fractional counting of citations affects the impact factor : normalization in terms of differences in citation potentials among fields of science (2011) 0.00

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Date: 22. 1.2011 12:51:07

Marx, W.; Bornmann, L.; Barth, A.; Leydesdorff, L.: Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS) (2014) 0.00

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Date: 18. 3.2014 18:56:54

Bornmann, L.; Daniel, H.D.: What do citation counts measure? : a review of studies on citing behavior (2008) 0.00

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Date: 21. 3.2008 14:00:54

Leydesdorff, L.; Bornmann, L.; Mingers, J.: Statistical significance and effect sizes of differences among research universities at the level of nations and worldwide based on the Leiden rankings (2019) 0.00
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Abstract

The Leiden Rankings can be used for grouping research universities by considering universities which are not statistically significantly different as homogeneous sets. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called "excellence indicator" PPtop-10%-the proportion of the top-10% most-highly-cited papers assigned to a university-we pursue a classification using (a) overlapping stability intervals, (b) statistical-significance tests, and (c) effect sizes of differences among 902 universities in 54 countries; we focus on the UK, Germany, Brazil, and the USA as national examples. Although the groupings remain largely the same using different statistical significance levels or overlapping stability intervals, these classifications are uncorrelated with those based on effect sizes. Effect sizes for the differences between universities are small (w < .2). The more detailed analysis of universities at the country level suggests that distinctions beyond three or perhaps four groups of universities (high, middle, low) may not be meaningful. Given similar institutional incentives, isomorphism within each eco-system of universities should not be underestimated. Our results suggest that networks based on overlapping stability intervals can provide a first impression of the relevant groupings among universities. However, the clusters are not well-defined divisions between groups of universities.

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