Name
Abstract | ||
|---|---|---|
We investigate different approaches based on correlation analysis to reduce the complexity of a space of quantitative indicators for the assessment of research performance. The proposed methods group bibliometric indicators into clusters of highly intercorrelated indicators. Each cluster is then associated with a representative indicator. The set of all representatives corresponds to a base of orthogonal metrics capturing independent aspects of research performance and can be exploited to design a composite performance indicator. We apply the devised methodology to isolate orthogonal performance metrics for scholars and journals in the field of computer science and to design a global performance indicator. The methodology is general and can be exploited to design composite indicators that are based on a set of possibly overlapping criteria. © 2009 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1002/asi.v60:10 | JASIST |
Keywords | Field | DocType |
performance indicator,cluster analysis | Cluster (physics),Data mining,Performance indicator,Dimensionality reduction,Information retrieval,Computer science,Citation analysis,Bibliometrics,Correlation analysis | Journal |
Volume | Issue | ISSN |
60 | 10 | 1532-2882 |
Citations | PageRank | References |
14 | 0.94 | 17 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimo Franceschet | 1 | 658 | 39.91 |