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How to improve the prediction based on citation impact percentiles for years shortly after the publication date?

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How to improve the prediction based on citation impact percentiles for years shortly after the publication date? / Bornmann, Lutz; Leydesdorff, Loet; Wang, Jian.
In: Journal of Informetrics, Vol. 8, No. 1, 31.01.2014, p. 175-180.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Bornmann, L, Leydesdorff, L & Wang, J 2014, 'How to improve the prediction based on citation impact percentiles for years shortly after the publication date?', Journal of Informetrics, vol. 8, no. 1, pp. 175-180. https://doi.org/10.1016/j.joi.2013.11.005

APA

Bornmann, L., Leydesdorff, L., & Wang, J. (2014). How to improve the prediction based on citation impact percentiles for years shortly after the publication date? Journal of Informetrics, 8(1), 175-180. https://doi.org/10.1016/j.joi.2013.11.005

Vancouver

Bornmann L, Leydesdorff L, Wang J. How to improve the prediction based on citation impact percentiles for years shortly after the publication date? Journal of Informetrics. 2014 Jan 31;8(1):175-180. doi: 10.1016/j.joi.2013.11.005

Author

Bornmann, Lutz ; Leydesdorff, Loet ; Wang, Jian. / How to improve the prediction based on citation impact percentiles for years shortly after the publication date?. In: Journal of Informetrics. 2014 ; Vol. 8, No. 1. pp. 175-180.

Bibtex

@article{0843e2482aaa46fd9f6208e9ed94c877,
title = "How to improve the prediction based on citation impact percentiles for years shortly after the publication date?",
abstract = "The findings of Bornmann, Leydesdorff, and Wang (2013b) revealed that the consideration of journal impact improves the prediction of long-term citation impact. This paper further explores the possibility of improving citation impact measurements on the base of a short citation window by the consideration of journal impact and other variables, such as the number of authors, the number of cited references, and the number of pages. The dataset contains 475,391 journal papers published in 1980 and indexed in Web of Science (WoS, Thomson Reuters), and all annual citation counts (from 1980 to 2010) for these papers. As an indicator of citation impact, we used percentiles of citations calculated using the approach of Hazen (1914). Our results show that citation impact measurement can really be improved: If factors generally influencing citation impact are considered in the statistical analysis, the explained variance in the long-term citation impact can be much increased. However, this increase is only visible when using the years shortly after publication but not when using later years.",
keywords = "Citation impact normalization, Percentile, Short citation window",
author = "Lutz Bornmann and Loet Leydesdorff and Jian Wang",
year = "2014",
month = jan,
day = "31",
doi = "10.1016/j.joi.2013.11.005",
language = "English",
volume = "8",
pages = "175--180",
journal = "Journal of Informetrics",
issn = "1751-1577",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

T1 - How to improve the prediction based on citation impact percentiles for years shortly after the publication date?

AU - Bornmann, Lutz

AU - Leydesdorff, Loet

AU - Wang, Jian

PY - 2014/1/31

Y1 - 2014/1/31

N2 - The findings of Bornmann, Leydesdorff, and Wang (2013b) revealed that the consideration of journal impact improves the prediction of long-term citation impact. This paper further explores the possibility of improving citation impact measurements on the base of a short citation window by the consideration of journal impact and other variables, such as the number of authors, the number of cited references, and the number of pages. The dataset contains 475,391 journal papers published in 1980 and indexed in Web of Science (WoS, Thomson Reuters), and all annual citation counts (from 1980 to 2010) for these papers. As an indicator of citation impact, we used percentiles of citations calculated using the approach of Hazen (1914). Our results show that citation impact measurement can really be improved: If factors generally influencing citation impact are considered in the statistical analysis, the explained variance in the long-term citation impact can be much increased. However, this increase is only visible when using the years shortly after publication but not when using later years.

AB - The findings of Bornmann, Leydesdorff, and Wang (2013b) revealed that the consideration of journal impact improves the prediction of long-term citation impact. This paper further explores the possibility of improving citation impact measurements on the base of a short citation window by the consideration of journal impact and other variables, such as the number of authors, the number of cited references, and the number of pages. The dataset contains 475,391 journal papers published in 1980 and indexed in Web of Science (WoS, Thomson Reuters), and all annual citation counts (from 1980 to 2010) for these papers. As an indicator of citation impact, we used percentiles of citations calculated using the approach of Hazen (1914). Our results show that citation impact measurement can really be improved: If factors generally influencing citation impact are considered in the statistical analysis, the explained variance in the long-term citation impact can be much increased. However, this increase is only visible when using the years shortly after publication but not when using later years.

KW - Citation impact normalization

KW - Percentile

KW - Short citation window

U2 - 10.1016/j.joi.2013.11.005

DO - 10.1016/j.joi.2013.11.005

M3 - Journal article

AN - SCOPUS:84890169744

VL - 8

SP - 175

EP - 180

JO - Journal of Informetrics

JF - Journal of Informetrics

SN - 1751-1577

IS - 1

ER -