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

TL;DR

This paper introduces Biplot analysis as an effective visualization method for multivariate bibliometric data, demonstrating its application and advantages over traditional techniques in scientometrics.

Contribution

It presents Biplot analysis as a novel visualization approach for bibliometric indicators and compares it with existing methods through practical case studies.

Findings

Biplot analysis effectively visualizes multivariate bibliometric data.

It offers advantages over traditional methods like PCA and MDS.

Biplot is user-friendly for research decision makers.

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 Multi-Dimensional Scaling, Principal Component Analysis or Correspondence Analysis. In this paper we aim at presenting 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 the Biplot analysis in the research policy area. More specifically we will first describe and introduce the reader to this methodology and secondly, we will analyze its strengths and weaknesses through three different study cases: 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 and an easy-to-read tool for research decision makers.