Document (#42227)

Author
Leydesdorff, L.
Bornmann, L.
Mingers, J.
Title
Statistical significance and effect sizes of differences among research universities at the level of nations and worldwide based on the Leiden rankings
Source
Journal of the Association for Information Science and Technology. 70(2019) no.5, S.509-525
Year
2019
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.
Content
Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24130.
Theme
Informetrie

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  5. Dobrota, M.; Bulajic, M.; Bornmann, L.; Jeremic, V.: ¬A new approach to the QS university ranking using the composite I-distance indicator : uncertainty and sensitivity analyses (2016) 0.14
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