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More precise methods for national research citation impact comparisons

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  • Fairclough, Ruth
  • Thelwall, Mike

Abstract

Governments sometimes need to analyse sets of research papers within a field in order to monitor progress, assess the effect of recent policy changes, or identify areas of excellence. They may compare the average citation impacts of the papers by dividing them by the world average for the field and year. Since citation data is highly skewed, however, simple averages may be too imprecise to robustly identify differences within, rather than across, fields. In response, this article introduces two new methods to identify national differences in average citation impact, one based on linear modelling for normalised data and the other using the geometric mean. Results from a sample of 26 Scopus fields between 2009 and 2015 show that geometric means are the most precise and so are recommended for smaller sample sizes, such as for individual fields. The regression method has the advantage of distinguishing between national contributions to internationally collaborative articles, but has substantially wider confidence intervals than the geometric mean, undermining its value for any except the largest sample sizes.

Suggested Citation

  • Fairclough, Ruth & Thelwall, Mike, 2015. "More precise methods for national research citation impact comparisons," Journal of Informetrics, Elsevier, vol. 9(4), pages 895-906.
  • Handle: RePEc:eee:infome:v:9:y:2015:i:4:p:895-906
    DOI: 10.1016/j.joi.2015.09.005
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    6. Confraria, Hugo & Mira Godinho, Manuel & Wang, Lili, 2017. "Determinants of citation impact: A comparative analysis of the Global South versus the Global North," Research Policy, Elsevier, vol. 46(1), pages 265-279.
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    8. Thelwall, Mike, 2016. "The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 110-123.
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    10. Thelwall, Mike & Sud, Pardeep, 2016. "National, disciplinary and temporal variations in the extent to which articles with more authors have more impact: Evidence from a geometric field normalised citation indicator," Journal of Informetrics, Elsevier, vol. 10(1), pages 48-61.
    11. Kousha, Kayvan & Thelwall, Mike & Abdoli, Mahshid, 2018. "Can Microsoft Academic assess the early citation impact of in-press articles? A multi-discipline exploratory analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 287-298.
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    13. Amalia Mas-Bleda & Mike Thelwall, 2016. "Can alternative indicators overcome language biases in citation counts? A comparison of Spanish and UK research," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2007-2030, December.
    14. Nunkoo, Robin & Hall, C. Michael & Rughoobur-Seetah, Soujata & Teeroovengadum, Viraiyan, 2019. "Citation practices in tourism research: Toward a gender conscientious engagement," Annals of Tourism Research, Elsevier, vol. 79(C).
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    19. Hou, Jianhua & Wang, Dongyi & Li, Jing, 2022. "A new method for measuring the originality of academic articles based on knowledge units in semantic networks," Journal of Informetrics, Elsevier, vol. 16(3).
    20. Saarela, Mirka & Kärkkäinen, Tommi & Lahtonen, Tommi & Rossi, Tuomo, 2016. "Expert-based versus citation-based ranking of scholarly and scientific publication channels," Journal of Informetrics, Elsevier, vol. 10(3), pages 693-718.
    21. Mike Thelwall, 2017. "Avoiding obscure topics and generalising findings produces higher impact research," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 307-320, January.
    22. Thelwall, Mike, 2016. "The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression," Journal of Informetrics, Elsevier, vol. 10(2), pages 336-346.
    23. Thelwall, Mike, 2018. "Dimensions: A competitor to Scopus and the Web of Science?," Journal of Informetrics, Elsevier, vol. 12(2), pages 430-435.

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