Informatics Publishing Limited

S. Savithri ¹, Gangan Prathap ¹

Affiliations
1 CSIR-National Institute for Interdisciplinary Science and Technology, Thiruvananthapuram 695 019, IN

Abstract

The latest (2014) release of the SCImago Institutions Rankings (SIR) allows to compare the research performance of leading higher education institutions in India and China using an end-to-end bibliometric performance analysis procedure. Six carefully chosen primary and secondary bibliometric indicators summarize the chain of activity: input-output-excellence- outcome-productivity. From principal component analysis it is established that the primary indicators are orthogonal and represent size-dependent quantity and size-independent quality/productivity dimensions respectively. Using this insight two-dimensional maps can be used to visualize the results.

Keywords

Bibliometrics Indicators, Higher Education Institutions, Principal Component Analysis, Research Performance.

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Gangan Prathap ¹, P. Nishy ¹, S. Savithri ¹

Affiliations
1 CSIR National Institute for Interdisciplinary Science and Technology, Thiruvananthapuram 695 019, IN

Abstract

We identify two orthogonal journal performance indicators from the points of view of size-dependence and principal component analysis using graph-theoretic constructs from social network analysis. One, the power-weakness ratio is a size-independent recursive proxy for the quality of the journal's performance in the network. The second, the number of references (out-links) that the journal makes to all journals in the network is the size-dependent proxy for the size of the journal (a quantity metric). In an input-output sense, the number of references becomes the measure of the input and the number of citations received by the journal from all journals in the network becomes the size-dependent measure of the output. The power- weakness ratio of citations to references before recursive iteration becomes the non-network measure of popularity and the power-weakness ratio of weighted citations and weighted references after recursive iteration becomes the network measure of prestige of the journal. It is also possible to propose first-order and second-order measures of influence which are products of the quality and quantity parameter space. We also show that the influence weight that emerges from a Pinski-Narin or Google PageRank formulation is a size-dependent measure of prestige that is orthogonal to the power-weakness ratio. We illustrate the concepts using two simple artificial two- and threejournal networks and a real-life example of a subgraph of 10 well-known statistical journals with network data collected from the Web of Science.

Keywords

Bibliometrics, Journal Performance, Power–Weakness Ratio, Social Network Analysis.

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