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Citation Analysis & Journal Rankings: Definitions

Information on how to use library resources for citation analysis. This includes information about impact factors, journal rankings, altmetrics and how to find who has cited an article.

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Impact Factors are a metric used to determine the importance of a publication to a specific field of research. The Impact Factor reveals the number of times a publication has been cited. For instance if an article has a high Impact Factor, it has a high value and has been cited my multiple authors in that field. Likewise, a low Impact Factor reveals that the article is of little value in the field.

For example, if a journal has an impact factor of 15.84, this means that, on average, the articles published in this journal are cited approximately 16 times each in the two previous years:

 

Equations borrowed from Dalhousie University Library. 

H-Index
The H-index is a popular measure of publishing impact, where an author's H-index is represented by the number of papers (h) with a citation number ≥ h.

  • For example, a scientist with an H-index of 14 has published numerous papers, 14 of which have been cited at least 14 times.

Understanding the H-Index

  • H-Index typically varies by source (e.g., different values in Google Scholar, Scopus, and Web of Science).
    H-Index is not field-normalized and is not an accurate comparison of productivity across disciplines.
  • H-Index is weighted positively towards mid and late-career researchers as publications have had more time to accrue citations.

How to Look Up Your H-Index

Note: H-index values often vary significantly by database, as each database contains different sources (e.g., peer-reviewed articles, book chapters, or grey literature) and thus different citation counts.

  • Google Scholar
    • Click "Google Scholar" above.
    • In the main search box, enter author's name or search by article title.
    • In the search results page, click on the author's name to view their Google Scholar profile (note- not all authors have Google Scholar profiles; underlined author names indicate that a profile page exists).

Alternate H-Indexes

  • The G-index was created by Leo Egghe in 2006 to give more weight to authors' highly cited articles.
  • The i10-index is an author-level metric created by Google Scholar and used in Google's My Citations feature.

Citation analysis is the study of the impact and assumed quality of an article, an author, or an institution based on the number of times works and/or authors have been cited by others.

Why is citation analysis important?

  • To find out how much impact a particular article has had by showing which authors based some work upon it or cited it as an example within their own papers. 
  • To find out more about a field or topic; i.e. by reading the papers that cite a seminal work in that area. 
  • To determine how much impact a particular author has had by looking at the number of times his/her work has been cited by others.

Journal Rankings are the quantitative analysis of peer reviewed journals used to gain a complete depiction of a scholar's impact in their field. Journal rankings are based on three measures:

  • The number of publications
  • The number of times the author's research has been cited
  • The importance of the journal that published the research.

The impact of a journal will indicate where an author should submit their research. 

*Benefits and Limitations of Impact Metrics

Quantitative publication metrics offer a relatively quick and seemingly concrete measure of research impact and are used widely in the assessment of academic health sciences research. However, metrics can lead to over-simplification and sometimes serve as direct proxies for impact at the expense of other valuable considerations.

Several efforts have led to frameworks that promote the application of assessment metrics as one important aspect of a broader assessment process.

  • The Leiden Manifesto
    Ten principles emanating from the International Conference on Science and Technology Indicators (Leiden University, Netherlands). Encourages the use of robust quantitative and qualitative data "with sensitivity to the aim and nature of the research that is evaluated."