An increasing demand for bibliometric assessment of individuals has led to a growth of new bibliometric indicators as well as new variants or combinations of established ones. The aim of this review is to contribute with objective facts about the usefulness of bibliometric indicators of the effects of publication activity at the individual level. This paper reviews 108 indicators that can potentially be used to measure performance on individual author-level, and examines the complexity of their calculations in relation to what they are supposed to reflect and ease of end-user application. As such we provide a schematic overview of author-level indicators, where the indicators are broadly categorised into indicators of publication count, indicators that qualify output (on the level of the researcher and journal), indicators of the effect of output (effect as citations, citations normalized to field or the researcher's body of work), indicators that rank the individual's work and indicators of impact over time. Supported by an extensive appendix we present how the indicators are computed, the complexity of the mathematical calculation and demands to data-collection, their advantages and limitations as well as references to surrounding discussion in the bibliometric community. The Appendix supporting this study is available online as supplementary material.
Null hypothesis statistical significance tests (NHST) are widely used in quantitative research in the empirical sciences including scientometrics. Nevertheless, since their introduction nearly a century ago significance tests have been controversial. Many researchers are not aware of the numerous criticisms raised against NHST. As practiced, NHST has been characterized as a 'null ritual' that is overused and too often misapplied and misinterpreted. NHST is in fact a patchwork of two fundamentally different classical statistical testing models, often blended with some wishful quasi-Bayesian interpretations. This is undoubtedly a major reason why NHST is very often misunderstood. But NHST also has intrinsic logical problems and the epistemic range of the information provided by such tests is much more limited than most researchers recognize. In this article we introduce to the scientometric community the theoretical origins of NHST, which is mostly absent from standard statistical textbooks, and we discuss some of the most prevalent problems relating to the practice of NHST and trace these problems back to the mix-up of the two different theoretical origins. Finally, we illustrate some of the misunderstandings with examples from the scientometric literature and bring forward some modest recommendations for a more sound practice in quantitative data analysis.
This article outlines and discusses the bibliometric indicator used for performance-based funding of research institutions in Norway. It is argued that the indicator is novel and innovative as compared to the indicators used in other funding models. It compares institutions based on all their publication-based research activities across all disciplines. Specific incentives are given to researchers to focus their publication behaviour on the most 'prestigious' publication channels within the different fields. Such aims necessitate a documentation system based on high-quality data, and require differentiated publication counts as the basic measure. Experience until now suggests that the indicator works as intended.
Gender and sex analysis is increasingly recognized as a key factor in creating better medical research and healthcare. Using a sample of more than 1.5 million medical research papers, our study examined the potential link between women’s participation in medical science and attention to gender- and sex-related factors in disease-specific research. Adjusting for variations across countries, disease topics and medical research areas, we compared the participation of women authors in studies that do and do not involve gender and sex analysis. Overall, our results show a robust positive correlation between women’s authorship and a study’s likelihood of engaging gender and sex analysis. These findings corroborate discussions of how women’s participation in medical science links to research outcomes, and illustrate the mutual benefits of promoting both women’s scientific advancement and the integration of gender and sex analysis into medical research.
The present two-part article introduces matrix comparison as a formal means of evaluation in informetric studies such as cocitation analysis. In this first part, the motivation behind introducing matrix comparison to informetric studies, as well as two important issues influencing such comparisons, are introduced and discussed. The motivation is spurred by the recent debate on choice of proximity measures and their potential influence upon clustering and ordination results. The two important issues discussed here are matrix generation and the composition of proximity measures. The approach to matrix generation is demonstrated for the same data set, i.e., how data is represented and transformed in a matrix, evidently determines the behavior of proximity measures. Two different matrix generation approaches, in all probability, will lead to different proximity rankings of objects, which further lead to different ordination and clustering results for the same set of objects. Further, a resemblance in the composition of formulas indicates whether two proximity measures may produce similar ordination and clustering results. However, as shown in the case of the angular correlation and cosine measures, a small deviation in otherwise similar formulas can lead to different rankings depending on the contour of the data matrix transformed. Eventually, the behavior of proximity measures, that is whether they produce similar rankings of objects, is more or less dataspecific. Consequently, the authors recommend the use of empirical matrix comparison techniques for individual studies to investigate the degree of resemblance between proximity measures or their ordination results. In part two of the article, the authors introduce and demonstrate two related statistical matrix comparison techniques the Mantel test and Procrustes analysis, respectively. These techniques can compare and evaluate the degree of monotonicity between different proximity measures or their ordination results. As such, the Mantel test and Procrustes analysis can be used as statistical validation tools in informetric studies and thus help choosing suitable proximity measures.
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