Modeling and prediction of the impact factor of journals using open-access databases

Templ, Matthias

doi:10.17713/ajs.v49i5.1186

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26283

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Modeling and prediction of the impact factor of journals using open-access databases
Authors:	Templ, Matthias
et. al:	No
DOI:	10.17713/ajs.v49i5.1186 10.21256/zhaw-26283
Published in:	Austrian Journal of Statistics
Volume(Issue):	49
Issue:	5
Page(s):	35
Pages to:	58
Issue Date:	2020
Publisher / Ed. Institution:	Austrian Statistical Society
ISSN:	1026-597X
Language:	English
Subjects:	Bibliometrics; Journal impact factor; Open-access; Statistical modelling
Subject (DDC):	020: Library and information sciences 070: News media, journalism and publishing
Abstract:	This article is motivated by the work as editor-in-chief of the Austrian Journal of Statistics and contains detailed analyses about the impact of the Austrian Journal of Statistics. The impact of a journal is typically expressed by journal metrics indicators. One of the important ones, the journal impact factor is calculated from the Web of Science (WoS) database by Clarivate Analytics. It is known that newly established journals or journals without membership in big publishers often face difficulties to be included, e.g., in the Science Citation Index (SCI) and thus they do not receive a WoS journal impact factor, as it is the case for example, for the Austrian Journal of Statistics. In this study, a novel approach is pursued modeling and predicting the WoS impact factor of journals using open access or partly open-access databases, like Google Scholar, ResearchGate, and Scopus. I hypothesize a functional linear dependency between citation counts in these databases and the journal impact factor. These functional relationships enable the development of a model that may allow estimating the impact factor for new, small, and independent journals not listed in SCI. However, only good results could be achieved with robust linear regression and well-chosen models. In addition, this study demonstrates that the WoS impact factor of SCI listed journals can be successfully estimated without using the Web of Science database and therefore the dependency of researchers and institutions to this popular database can be minimized. These results suggest that the statistical model developed here can be well applied to predict the WoS impact factor using alternative open-access databases.
URI:	https://digitalcollection.zhaw.ch/handle/11475/26283
Fulltext version:	Published version
License (according to publishing contract):	CC BY 3.0: Attribution 3.0 Unported
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

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Templ, M. (2020). Modeling and prediction of the impact factor of journals using open-access databases. Austrian Journal of Statistics, 49(5), 35–58. https://doi.org/10.17713/ajs.v49i5.1186

Templ, M. (2020) ‘Modeling and prediction of the impact factor of journals using open-access databases’, Austrian Journal of Statistics, 49(5), pp. 35–58. Available at: https://doi.org/10.17713/ajs.v49i5.1186.

M. Templ, “Modeling and prediction of the impact factor of journals using open-access databases,” Austrian Journal of Statistics, vol. 49, no. 5, pp. 35–58, 2020, doi: 10.17713/ajs.v49i5.1186.

TEMPL, Matthias, 2020. Modeling and prediction of the impact factor of journals using open-access databases. Austrian Journal of Statistics. 2020. Bd. 49, Nr. 5, S. 35–58. DOI 10.17713/ajs.v49i5.1186

Templ, Matthias. 2020. “Modeling and Prediction of the Impact Factor of Journals Using Open-Access Databases.” Austrian Journal of Statistics 49 (5): 35–58. https://doi.org/10.17713/ajs.v49i5.1186.

Templ, Matthias. “Modeling and Prediction of the Impact Factor of Journals Using Open-Access Databases.” Austrian Journal of Statistics, vol. 49, no. 5, 2020, pp. 35–58, https://doi.org/10.17713/ajs.v49i5.1186.