Advanced Search
MyIDEAS: Login

How are journal impact, prestige and article influence related? An application to neuroscience

Contents:

Author Info

  • Chia-Lin Chang
  • Michael McAleer
  • Les Oxley

Abstract

The paper analyzes the leading journals in neurosciences using quantifiable research assessment measures (RAM), highlights the similarities and differences in alternative RAM, shows that several RAM capture similar performance characteristics of highly cited journals, and shows that some other RAM have low correlations with each other, and hence add significant informational value. Alternative RAM are discussed for the Thomson Reuters ISI Web of Science database (hereafter ISI). The RAM that are calculated annually or updated daily include the classic 2-year impact factor (2YIF), 5-year impact factor, immediacy (or zero-year impact factor), Eigenfactor score, article influence score, C3PO (citation performance per paper online), h-index, Zinfluence, PI-BETA (papers ignored by even the authors), 2-year and historical self-citation threshold approval ratings, impact factor inflation, and cited article influence (CAI). The RAM are analyzed for 26 highly cited journals in the ISI category of neurosciences. The paper finds that the Eigenfactor score and PI-BETA are not highly correlated with the other RAM scores, so that they convey additional information regarding journal rankings, that article influence is highly correlated with some existing RAM, so that it has little informative incremental value, and that CAI has additional informational value to that of article influence. Harmonic mean rankings of the 13 RAM criteria for the 26 highly cited journals are also presented. Emphasizing the 2YIF of a journal to the exclusion of other informative RAM criteria is shown to lead to a distorted evaluation of journal performance and influence, especially given the informative value of several other RAM.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://hdl.handle.net/10.1080/02664763.2011.559212
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

Volume (Year): 38 (2011)
Issue (Month): 11 (January)
Pages: 2563-2573

as in new window
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2563-2573

Contact details of provider:
Web page: http://www.tandfonline.com/CJAS20

Order Information:
Web: http://www.tandfonline.com/pricing/journal/CJAS20

Related research

Keywords:

Other versions of this item:

Find related papers by JEL classification:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Chia-Lin Chang & Michael McAleer & Les Oxley, 2011. "Great Expectatrics: Great Papers, Great Journals, Great Econometrics," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 583-619.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2563-2573. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.