Do we need the <i>h</i> index and its variants in addition to standard bibliometric measures? is a research paper published in Journal of the American Society for Information Science and Technology (2009). On theSindex it has a DataRank of 0.730. It has been cited 31 times, with 4 citing works in its 1-hop citation network.
AbstractIn 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.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.520
From this paper's citation signal
Citation Network Contribution
0.210
From 4 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 71% comes from its base citations and 29% from the citation network (4 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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