Interpreting correlations between citation counts and other indicators

Thelwall, Mike

dc.contributor.author	Thelwall, Mike
dc.date.accessioned	2016-06-27T13:51:18Z
dc.date.available	2016-06-27T13:51:18Z
dc.date.issued	2016-05-09
dc.identifier.citation	Thelwall, M. (2016) Interpreting correlations between citation counts and other indicators. Scientometrics 108 (1), pp 337–347.
dc.identifier.issn	0138-9130
dc.identifier.doi	10.1007/s11192-016-1973-7
dc.identifier.uri	http://hdl.handle.net/2436/614827
dc.description	This is an accepted manuscript of an article published by Springer in Scientometrics on 09/05/2016, available online: https://doi.org/10.1007/s11192-016-1973-7 The accepted version of the publication may differ from the final published version.
dc.description.abstract	Altmetrics or other indicators for the impact of academic outputs are often correlated with citation counts in order to help assess their value. Nevertheless, there are no guidelines about how to assess the strengths of the correlations found. This is a problem because this value affects the conclusions that should be drawn. In response, this article uses experimental simulations to assess the correlation strengths to be expected under various different conditions. The results show that the correlation strength reflects not only the underlying degree of association but also the average magnitude of the numbers involved. Overall, the results suggest that due to the number of assumptions that must be made in practice it will rarely be possible to make a realistic interpretation of the strength of a correlation coefficient.
dc.format	application/pdf
dc.language.iso	en
dc.publisher	Springer
dc.relation.url	http://link.springer.com/10.1007/s11192-016-1973-7
dc.subject	Citation analysis
dc.subject	correlations
dc.subject	Altmetrics
dc.subject	Indicators
dc.subject	Discretised lognormal
dc.subject	Simulation
dc.title	Interpreting correlations between citation counts and other indicators
dc.type	Journal article
dc.identifier.journal	Scientometrics
dc.date.accepted	2016-05-03
rioxxterms.funder	University of Wolverhampton
rioxxterms.identifier.project	Unfunded
rioxxterms.version	AM
rioxxterms.licenseref.uri	https://creativecommons.org/CC BY-NC-ND 4.0
rioxxterms.licenseref.startdate	2017-05-08
dc.source.volume	108
dc.source.issue	1
dc.source.beginpage	337
dc.source.endpage	347
refterms.dateFCD	2018-10-19T09:24:43Z
refterms.versionFCD	AM
refterms.dateFOA	2017-05-08T00:00:00Z
html.description.abstract	Altmetrics or other indicators for the impact of academic outputs are often correlated with citation counts in order to help assess their value. Nevertheless, there are no guidelines about how to assess the strengths of the correlations found. This is a problem because this value affects the conclusions that should be drawn. In response, this article uses experimental simulations to assess the correlation strengths to be expected under various different conditions. The results show that the correlation strength reflects not only the underlying degree of association but also the average magnitude of the numbers involved. Overall, the results suggest that due to the number of assumptions that must be made in practice it will rarely be possible to make a realistic interpretation of the strength of a correlation coefficient.