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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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Collins, H.; Bornmann, L.: On scientific misconduct (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1089-1090
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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.: 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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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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- Date
- 22. 1.2011 12:51:07
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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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- Abstract
- Using the CD-ROM version of the Science Citation Index 2010 (N = 3,705 journals), we study the (combined) effects of (a) fractional counting on the impact factor (IF) and (b) transformation of the skewed citation distributions into a distribution of 100 percentiles and six percentile rank classes (top-1%, top-5%, etc.). Do these approaches lead to field-normalized impact measures for journals? In addition to the 2-year IF (IF2), we consider the 5-year IF (IF5), the respective numerators of these IFs, and the number of Total Cites, counted both as integers and fractionally. These various indicators are tested against the hypothesis that the classification of journals into 11 broad fields by PatentBoard/NSF (National Science Foundation) provides statistically significant between-field effects. Using fractional counting the between-field variance is reduced by 91.7% in the case of IF5, and by 79.2% in the case of IF2. However, the differences in citation counts are not significantly affected by fractional counting. These results accord with previous studies, but the longer citation window of a fractionally counted IF5 can lead to significant improvement in the normalization across fields.
-
Egghe, L.; Bornmann, L.: Fallout and miss in journal peer review (2013)
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- Date
- 5. 4.2015 19:17:10
-
Bornmann, L.; Mutz, R.; Daniel, H.-D.: Are there better indices for evaluation purposes than the h index? : a comparison of nine different variants of the h index using data from biomedicine (2008)
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- Source
- Journal of the American Society for Information Science and Technology. 59(2008) no.5, S.830-837
-
Bornmann, L.; Mutz, R.; Daniel, H.-D.: Multilevel-statistical reformulation of citation-based university rankings : the Leiden ranking 2011/2012 (2013)
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- Abstract
- Since the 1990s, with the heightened competition and the strong growth of the international higher education market, an increasing number of rankings have been created that measure the scientific performance of an institution based on data. The Leiden Ranking 2011/2012 (LR) was published early in 2012. Starting from Goldstein and Spiegelhalter's (1996) recommendations for conducting quantitative comparisons among institutions, in this study we undertook a reformulation of the LR by means of multilevel regression models. First, with our models we replicated the ranking results; second, the reanalysis of the LR data showed that only 5% of the PPtop10% total variation is attributable to differences between universities. Beyond that, about 80% of the variation between universities can be explained by differences among countries. If covariates are included in the model the differences among most of the universities become meaningless. Our findings have implications for conducting university rankings in general and for the LR in particular. For example, with Goldstein-adjusted confidence intervals, it is possible to interpret the significance of differences among universities meaningfully: Rank differences among universities should be interpreted as meaningful only if their confidence intervals do not overlap.
-
Bornmann, L.; Moya Anegón, F.de: What proportion of excellent papers makes an institution one of the best worldwide? : Specifying thresholds for the interpretation of the results of the SCImago Institutions Ranking and the Leiden Ranking (2014)
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- Abstract
- University rankings generally present users with the problem of placing the results given for an institution in context. Only a comparison with the performance of all other institutions makes it possible to say exactly where an institution stands. In order to interpret the results of the SCImago Institutions Ranking (based on Scopus data) and the Leiden Ranking (based on Web of Science data), in this study we offer thresholds with which it is possible to assess whether an institution belongs to the top 1%, top 5%, top 10%, top 25%, or top 50% of institutions in the world. The thresholds are based on the excellence rate or PPtop 10%. Both indicators measure the proportion of an institution's publications which belong to the 10% most frequently cited publications and are the most important indicators for measuring institutional impact. For example, while an institution must achieve a value of 24.63% in the Leiden Ranking 2013 to be considered one of the top 1% of institutions worldwide, the SCImago Institutions Ranking requires 30.2%.
-
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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- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.5, S.509-525
