{"id":11061,"date":"2022-01-31T11:33:43","date_gmt":"2022-01-31T16:33:43","guid":{"rendered":"https:\/\/dev-cu-sites.pantheonsite.io\/library\/?page_id=11061"},"modified":"2025-05-07T08:53:05","modified_gmt":"2025-05-07T12:53:05","slug":"author-metrics","status":"publish","type":"page","link":"https:\/\/sites.clarkson.edu\/library\/research-tools-collections\/guides\/research-impact-metrics\/author-metrics\/","title":{"rendered":"Author-Level Metrics"},"content":{"rendered":"\n

Author-level metrics<\/strong>\u00a0are citation metrics\u00a0that measure the bibliometric impact\u00a0of individual authors.\u00a0H-index\u00a0<\/strong>is the best known author-level metric, but there are also g-index, i10-index, author impact factor, and author-level Eigenfactor. <\/p>\n\n\n\n

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H-Index<\/a><\/h4><\/div>
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The h-index<\/strong> attempts to measure both the productivity and impact of the published work of a scientist or scholar.  The calculation was suggested by an American physicist JE Hirsch and it can be summed up as:<\/p>\n\n\n\n

A scientist has an index h if h of his\/her Np papers has at least h citations each, and the other (Np h) papers have no more than h citations each.<\/p>\n\n\n\n

In other words, to have an h-index of 5, an author has to have 5 publications, each receiving at least 5 citations.<\/p>\n\n\n\n

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The criticisms of the h-index include the fact that it does not account for highly cited papers (your h-index is the same whether you most highly cited paper has a 100 or 10 citations.  Another criticism is that it does not account for the career span of the author \u2013 because it is a simple function of productivity and impact, authors with longer career spans (and more publications will always have higher scores).To help with these weaknesses h-index variants had been proposed.<\/p>\n\n\n\n

There are a number of sources where you can find your h-index.  Please note that the value of the index may vary depending on the source of information (number of indexed publications, time span, etc.)  For instance, h-index derived from Google Scholar tend to be higher than that based on Web of Science or SCOPUS data.  <\/p>\n\n\n\n

Available via free software Publish or Perish<\/a>, Scopus<\/a>, or Google Scholar<\/a>.\u00a0<\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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G-Index<\/a><\/h4><\/div>
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The g-index<\/strong>\u00a0is a variant of the h-index that, in its calculation, gives credit\u00a0for\u00a0the most highly cited papers in a data set. \u00a0In the words of Leo Egghe,\u00a0its inventor: <\/p>\n\n\n\n

\u201cHighly cited papers are, of course, important for the determination of the value h of the h-index. But once a paper is selected to belong to the top h papers, this paper is not \u201cused\u201d any more in the determination of h, as a variable over time. Indeed, once a paper is selected to the top group, the h-index calculated in subsequent years is not at all influenced by this paper\u2019s received citations further on: even if the paper doubles or triples its number of citations (or even more) the subsequent h-indexes are not influenced by this.\u201d <\/p>\n\n\n\n

The g-index is always the same as or higher than the h-index.<\/p>\n\n\n\n

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Available via free software Publish or Perish<\/a>.<\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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i10-Index<\/a><\/h4><\/div>
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The i10-index<\/strong> is a metric created by Google Scholar and used in Google\u2019s My Citations feature. <\/p>\n\n\n\n

i10-Index = the number of publications with at least 10 citations.  <\/p>\n\n\n\n

Available via Google Scholar<\/a>.\u00a0<\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Author Impact Factor<\/a><\/h4><\/div>
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An individual impact factor is a measure of the average frequency with which your recent articles have been cited in a particular year.<\/p>\n\n\n\n

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For more information\u00a0see:\u00a0Pan, RK;\u00a0Fortunato, S\u00a0(2014).\u00a0\u201cAuthor Impact Factor: Tracking the dynamics of individual scientific impact\u201d<\/a>.\u00a0Scientific Reports<\/a><\/i>\u00a04: 4880.<\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Author-level Eigenfactor<\/a><\/h4><\/div>
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Author-level Eigenfactor<\/strong> is an attempt to use Eigenfactor methodology (originally developed to rank journals), to create a ranking list of authors in SSRN.  <\/p>\n\n\n\n

For description of the study and list of ranked authors see:  West, JD; Jensen, MC; Dandrea, RJ; Gordon, GJ; Bergstrom, CT. (2013). \u201cAuthor-level Eigenfactor metrics: Evaluating the influence of authors, institutions, and countries within the social science research network community<\/a>\u201c. Journal of the American Society for Information Science and Technology<\/a><\/i> 64<\/b> (4): 787\u2013801. <\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

Metric Tracking Databases<\/h4>\n\n\n
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g<\/span>