The Accuracy of Confidence Intervals for Field Normalised Indicators
AbstractWhen comparing the average citation impact of research groups, universities and countries, field normalisation reduces the influence of discipline and time. Confidence intervals for these indicators can help with attempts to infer whether differences between sets of publications are due to chance factors. Although both bootstrapping and formulae have been proposed for these, their accuracy is unknown. In response, this article uses simulated data to systematically compare the accuracy of confidence limits in the simplest possible case, a single field and year. The results suggest that the MNLCS (Mean Normalised Log-transformed Citation Score) confidence interval formula is conservative for large groups but almost always safe, whereas bootstrap MNLCS confidence intervals tend to be accurate but can be unsafe for smaller world or group sample sizes. In contrast, bootstrap MNCS (Mean Normalised Citation Score) confidence intervals can be very unsafe, although their accuracy increases with sample sizes.
CitationThelwall, M. & Fairclough, R. (2017) The accuracy of confidence intervals for field normalised indicators, Journal of Informetrics, 11 (2) 530-540.
JournalJournal of Informetrics
DescriptionThis is an accepted manuscript of an article published by Elsevier in Journal of Informetrics on 07/04/2017, available online: https://doi.org/10.1016/j.joi.2017.03.004 The accepted version of the publication may differ from the final published version.
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- Creative Commons
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/