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

Authors

  • Daniel Torres-Salinas,

    1. EC3 Research Group: Evaluación de la Ciencia y la Comunicación Científica, Centro de Investigación Médica Aplicada, Avenida Pio XII, 55, Universidad de Navarra, Pamplona, Spain
    Search for more papers by this author
  • Nicolás Robinson-García,

    1. EC3 Research Group: Evaluación de la Ciencia y la Comunicación Científica, Facultad de Comunicación y Documentación, Colegio Máximo de Cartuja s/n, Universidad de Granada, Granada, Spain
    Search for more papers by this author
  • Evaristo Jiménez-Contreras,

    1. EC3 Research Group: Evaluación de la Ciencia y la Comunicación Científica, Facultad de Comunicación y Documentación, Colegio Máximo de Cartuja s/n, Universidad de Granada, Granada, Spain
    Search for more papers by this author
  • Francisco Herrera,

    1. Departamento de Ciencias de la Computación e I. A., CITIC-UGR, c/. Daniel Saucedo Aranda, s/n, Universidad de Granada, Granada, Spain
    Search for more papers by this author
  • Emilio Delgado López-Cózar

    1. EC3 Research Group: Evaluación de la Ciencia y la Comunicación Científica, Facultad de Comunicación y Documentación, Colegio Máximo de Cartuja s/n, Universidad de Granada, Granada, Spain
    Search for more papers by this author

Abstract

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.

Ancillary