Simulation of the h index use at university departments within the bibliometrics-based heuristics framework: Can the indicator be used to compare individual researchers?

Lutz Bornmann, Christian Ganser and Alexander Tekles

Journal of Informetrics, 2022, vol. 16, issue 1

Abstract: Bornmann and Marewski (2019) have adapted the concept of fast-and-frugal heuristics to scientometrics in order to study and guide the application of bibliometrics in research evaluation. Bibliometrics-based heuristics (BBHs) are simple decision strategies for evaluative purposes based on bibliometric indicators. One aim of the heuristics research program is to develop methods for studying the use of BBHs in research evaluation. Many deans probably evaluate rough performance differences between researchers in their departments based on h index values. Bornmann, Ganser, Tekles, and Leydesdorff (2020) developed the Stata command h_index and R package hindex which can be deployed in a fast and frugal way to decide on the following question: can the h index be used to compare all researchers in a university department, or are the citation cultures so different between sub-groups in the department that not all researchers can be compared with one another? The command and package can be used for simulations that might answer the question before extensive processes of data collection start. If the citation cultures are very different in the sub-groups, the researchers should be compared with field-normalized indicators (instead of the h index). This paper shows how the h_indexcommand and hindexpackage can be employed for the decision on the h index use in the BBH.

Keywords: Bibliometrics; Bibliometrics-based heuristics; Bibliometrics-based decision trees; h index (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:16:y:2022:i:1:s1751157721001085

DOI: 10.1016/j.joi.2021.101237

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