On the use of biplot analysis for multivariate bibliometric and scientific indicators

• Published in: Journal of the American Society for Information Science and Technology Vol. 64; no. 7; pp. 1468 - 1479
• Main Authors: Torres-Salinas, Daniel, Robinson-García, Nicolás, Jiménez-Contreras, Evaristo, Herrera, Francisco, López-Cózar, Emilio Delgado
• Format: Journal Article
• Language: English
• Published: New York, NY Blackwell Publishing Ltd 01-07-2013; Wiley; Wiley Periodicals Inc
• Subjects: Bibliometrics; Bibliometrics. Scientometrics; Bibliometrics. Scientometrics. Evaluation; Columns (structural); Decision making models; Exact sciences and technology; Graph representations; Graphical representations; Indicators; Information and communication sciences; Information science. Documentation; Library and information science. General aspects; Mapping; Mathematical analysis; Matrix; Matrix methods; Methods; Multidimensional Scaling; Multivariate analysis; Principal components analysis; Reading; Scholarly publishing; Science and technology; Sciences and techniques of general use; Scientometrics; Studies; Universities; Visualization; Scientometrics; Research field; Correspondence analysis; Bibliometrics; Bibliometric analysis; Multivariate analysis; Multidimensional scaling; Indicator; Principal component analysis
• ISSN: 1532-2882; 2330-1635; 1532-2890; 2330-1643
• DOI: 10.1002/asi.22837

Bibliometric mapping and visualization techniques represent one of the main pillars in the field of scientometrics. Traditionally, the main methodologies employed for representing data are multidimensional scaling, principal component analysis, or correspondence analysis. In this paper we aim to present a visualization methodology known as biplot analysis for representing bibliometric and science and technology indicators. A biplot is a graphical representation of multivariate data, where the elements of a data matrix are represented according to dots and vectors associated with the rows and columns of the matrix. In this paper, we explore the possibilities of applying biplot analysis in the research policy area. More specifically, we first describe and introduce the reader to this methodology and secondly, we analyze its strengths and weaknesses through 3 different case studies: countries, universities, and scientific fields. For this, we use a biplot analysis known as JK‐biplot. Finally, we compare the biplot representation with other multivariate analysis techniques. We conclude that biplot analysis could be a useful technique in scientometrics when studying multivariate data, as well as an easy‐to‐read tool for research decision makers.