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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)
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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.
- Language
- e
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Bornmann, L.: On the function of university rankings (2014)
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- Date
- 29. 1.2014 16:55:03
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Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014)
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- Date
- 18. 3.2014 19:13:22
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Bornmann, L.; Daniel, H.-D.: What do we know about the h index? (2007)
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- Date
- 4. 8.2007 21:20:02
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Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
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Bornmann, L.; Marx, W.: Distributions instead of single numbers : percentiles and beam plots for the assessment of single researchers (2014)
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- Date
- 29. 1.2014 15:58:21
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Bornmann, L.; Moya Anegón, F. de; Mutz, R.: Do universities or research institutions with a specific subject profile have an advantage or a disadvantage in institutional rankings? (2013)
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- 29.10.2013 20:26:34
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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)
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- Date
- 22. 3.2013 19:44:17
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Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019)
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- Date
- 8. 1.2019 18:22:45
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Bornmann, L.; Daniel, H.D.: What do citation counts measure? : a review of studies on citing behavior (2008)
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- Date
- 10. 3.1997 20:31:13
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Leydesdorff, L.; Zhou, P.; Bornmann, L.: How can journal impact factors be normalized across fields of science? : An assessment in terms of percentile ranks and fractional counts (2013)
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- 27.12.2012 20:44:36
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Bornmann, L.; Ye, A.; Ye, F.: Identifying landmark publications in the long run using field-normalized citation data (2018)
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- Date
- 10. 3.1997 20:31:13
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Bornmann, L.; Schier, H.; Marx, W.; Daniel, H.-D.: Is interactive open access publishing able to identify high-impact submissions? : a study on the predictive validity of Atmospheric Chemistry and Physics by using percentile rank classes (2011)
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- 8. 1.2011 18:29:40
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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)
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- 22. 1.2011 12:51:07
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Bornmann, L.: Is there currently a scientific revolution in Scientometrics? (2014)
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Bornmann, L.; Bauer, J.; Haunschild, R.: Distribution of women and men among highly cited scientists (2015)
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Bornmann, L.: What do altmetrics counts mean? : a plea for content analyses (2016)
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