| Literature DB >> 29713258 |
Armen Yuri Gasparyan1, Marlen Yessirkepov2, Akmaral Duisenova2, Vladimir I Trukhachev3, Elena I Kostyukova4, George D Kitas1,5.
Abstract
Numerous quantitative indicators are currently available for evaluating research productivity. No single metric is suitable for comprehensive evaluation of the author-level impact. The choice of particular metrics depends on the purpose and context of the evaluation. The aim of this article is to overview some of the widely employed author impact metrics and highlight perspectives of their optimal use. The h-index is one of the most popular metrics for research evaluation, which is easy to calculate and understandable for non-experts. It is automatically displayed on researcher and author profiles on citation databases such as Scopus and Web of Science. Its main advantage relates to the combined approach to the quantification of publication and citation counts. This index is increasingly cited globally. Being an appropriate indicator of publication and citation activity of highly productive and successfully promoted authors, the h-index has been criticized primarily for disadvantaging early career researchers and authors with a few indexed publications. Numerous variants of the index have been proposed to overcome its limitations. Alternative metrics have also emerged to highlight 'societal impact.' However, each of these traditional and alternative metrics has its own drawbacks, necessitating careful analyses of the context of social attention and value of publication and citation sets. Perspectives of the optimal use of researcher and author metrics is dependent on evaluation purposes and compounded by information sourced from various global, national, and specialist bibliographic databases.Entities:
Keywords: Bibliographic Databases; Bibliometrics; Citations; Publications; Research Evaluation; h-index
Mesh:
Year: 2018 PMID: 29713258 PMCID: PMC5920127 DOI: 10.3346/jkms.2018.33.e139
Source DB: PubMed Journal: J Korean Med Sci ISSN: 1011-8934 Impact factor: 2.153
Fig. 1Number of Scopus-indexed items citing J. Hirsch's landmark article on the h-index in 2005–2018 (as of February 10, 2018).
Main strengths and limitations of some author-level metrics
| Metrics | Strengths | Limitations |
|---|---|---|
| Easily calculated, bi-dimensional metric for measuring publication and citation impact of highly productive researchers | Not suitable for early career researchers and those with a small number of publications; can be manipulated by self-citations; does not fluctuate | |
| Author Impact Factor | Focuses on publication and citation activity at different 5-year periods; fluctuates over time | Five-year time window can be narrow for authors in slowly developing disciplines |
| Gives more weight to highly cited items and helps visualize an individual's impact when the | Unlike the | |
| Focuses on highly cited items and helps distinguish highly productive authors with identical | Unsuitable for individuals with small publication and citation counts | |
| PageRank index | Considers weight of citations, does not increase with growing (self)citations from low-impact sources | Calculations are based on a version of PageRank algorithm, which is not easily understandable to non-experts; values of the index are highly dependent on visibility and promotion of cited items |
| Total publications | True reflection of productivity, which can be recorded by sourcing information from bibliographic databases and summing up the number of works published annually | Type of articles and their quality are not taken into account; manipulations by publishing low-quality and nonsense items can boost publication records |
| Total citations | Simple measure of an individual's influence; reflect citing authors' interest to published items | Context of citations and weight of cited articles are overlooked |
Pointers for employing researcher and author impact metrics
| • | No single metric, and especially the universally applicable |
| • | Individual profiles at several, including national and specialist bibliographic databases should be analyzed to comprehensively evaluate global and local components of an individual's research productivity. |
| • | Various published works reflect priorities of research productivity across academic disciplines (e.g., conference papers in physics, journal articles in medicine, monographs in humanities). |
| • | A mere number of researcher publications, citations, and related metrics should not be viewed as a proxy of the quality of their scholarly activities. |
| • | No any thresholds of number of publications, citations, and related metrics can be employed for distinguishing productive researchers from non-productive peers. Any such threshold (e.g., 50 articles) is arbitrary. |
| • | Optimal metrics for research evaluation should be simple, intuitive, and easily understandable for non-experts. |
| • | Quantitative indicators should complement, but not substitute, expert evaluation. |
| • | All author-level metrics are confounded by academic discipline, geography, (multi)authorship, time window, and age of researchers. |
| • | Productivity of early career researchers with a few publications and seasoned authors with established academic career and a large number of scholarly works should be evaluated separately. |
| • | Comprehensive research evaluation implies understanding of the context of all traditional and alternative metrics. When manipulation of publication and citation counts is suspected, the evaluation should preferably source information from higher-rank periodicals. |