Search (4 results, page 1 of 1)

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Abstract

Properties of a percentile-based rating scale needed in bibliometrics are formulated. Based on these properties, P100 was recently introduced as a new citation-rank approach (Bornmann, Leydesdorff, & Wang, 2013). In this paper, we conceptualize P100 and propose an improvement which we call P100'. Advantages and disadvantages of citation-rank indicators are noted.

Date

22. 8.2014 17:05:18

Type

a

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Abstract

In this study, we investigate whether there is a need for the h index and its variants in addition to standard bibliometric measures (SBMs). Results from our recent study (L. Bornmann, R. Mutz, & H.-D. Daniel, 2008) have indicated that there are two types of indices: One type of indices (e.g., h index) describes the most productive core of a scientist's output and informs about the number of papers in the core. The other type of indices (e.g., a index) depicts the impact of the papers in the core. In evaluative bibliometric studies, the two dimensions quantity and quality of output are usually assessed using the SBMs number of publications (for the quantity dimension) and total citation counts (for the impact dimension). We additionally included the SBMs into the factor analysis. The results of the newly calculated analysis indicate that there is a high intercorrelation between number of publications and the indices that load substantially on the factor Quantity of the Productive Core as well as between total citation counts and the indices that load substantially on the factor Impact of the Productive Core. The high-loading indices and SBMs within one performance dimension could be called redundant in empirical application, as high intercorrelations between different indicators are a sign for measuring something similar (or the same). Based on our findings, we propose the use of any pair of indicators (one relating to the number of papers in a researcher's productive core and one relating to the impact of these core papers) as a meaningful approach for comparing scientists.

Type

a

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Abstract

Using data compiled for the SCImago Institutions Ranking, we look at whether the subject area type an institution (university or research-focused institution) belongs to (in terms of the fields researched) has an influence on its ranking position. We used latent class analysis to categorize institutions based on their publications in certain subject areas. Even though this categorization does not relate directly to scientific performance, our results show that it exercises an important influence on the outcome of a performance measurement: Certain subject area types of institutions have an advantage in the ranking positions when compared with others. This advantage manifests itself not only when performance is measured with an indicator that is not field-normalized but also for indicators that are field-normalized.

Type

a

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Abstract

Many studies (in information science) have looked at the growth of science. In this study, we reexamine the question of the growth of science. To do this we (a) use current data up to publication year 2012 and (b) analyze the data across all disciplines and also separately for the natural sciences and for the medical and health sciences. Furthermore, the data were analyzed with an advanced statistical technique-segmented regression analysis-which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (a) the number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (b) the number of cited references in the publications of the source items per cited reference year. We looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three essential growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars, and 8 to 9% to 2010.

Type

a