TY - JOUR
T1 - Journal impact and proximity
T2 - An assessment using bibliographic features
AU - Ni, Chaoqun
AU - Shaw, Debora
AU - Lind, Sean M.
AU - Ding, Ying
PY - 2013/4
Y1 - 2013/4
N2 - Journals in the Information Science & Library Science category of Journal Citation Reports (JCR) were compared using both bibliometric and bibliographic features. Data collected covered journal impact factor (JIF), number of issues per year, number of authors per article, longevity, editorial board membership, frequency of publication, number of databases indexing the journal, number of aggregators providing full-text access, country of publication, JCR categories, Dewey decimal classification, and journal statement of scope. Three features significantly correlated with JIF: number of editorial board members and number of JCR categories in which a journal is listed correlated positively; journal longevity correlated negatively with JIF. Coword analysis of journal descriptions provided a proximity clustering of journals, which differed considerably from the clusters based on editorial board membership. Finally, a multiple linear regression model was built to predict the JIF based on all the collected bibliographic features.
AB - Journals in the Information Science & Library Science category of Journal Citation Reports (JCR) were compared using both bibliometric and bibliographic features. Data collected covered journal impact factor (JIF), number of issues per year, number of authors per article, longevity, editorial board membership, frequency of publication, number of databases indexing the journal, number of aggregators providing full-text access, country of publication, JCR categories, Dewey decimal classification, and journal statement of scope. Three features significantly correlated with JIF: number of editorial board members and number of JCR categories in which a journal is listed correlated positively; journal longevity correlated negatively with JIF. Coword analysis of journal descriptions provided a proximity clustering of journals, which differed considerably from the clusters based on editorial board membership. Finally, a multiple linear regression model was built to predict the JIF based on all the collected bibliographic features.
UR - http://www.scopus.com/inward/record.url?scp=84876665615&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876665615&partnerID=8YFLogxK
U2 - 10.1002/asi.22778
DO - 10.1002/asi.22778
M3 - Article
AN - SCOPUS:84876665615
SN - 1532-2882
VL - 64
SP - 802
EP - 817
JO - Journal of the American Society for Information Science and Technology
JF - Journal of the American Society for Information Science and Technology
IS - 4
ER -