Exploratory factor analysis

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1. Introduction

Hirsch (2005) introduced a new indicator for the assessment of the research performance of scientists. The proposed h-index is intended to measure simultaneously the quality and sustainability of scientific output, as well as, to some extent, the diversity of scientific research. The specific index attracted interest immediately and received great attention in the scientometrics literature. Not only it has found a wide use in a very short time, but also a series of articles were subsequently published either proposing modifications of the original h-index for its improvement, or implementations of the newly proposed index.

The h-index (sometimes called the Hirsch index or the Hirsch number) is based on the distribution of citations received by a given researcher’s publications. By definition:

“A scientist has index h if h of his Np papers have at least h citations each, and the other (Np – h) papers have at most h citations each”.

The index is designed to improve simpler measures such as the total number of citations or publications, to distinguish truly influential (in terms of citations) scientists from those who simply publish many papers. Among the advantages of this index is its simplicity, the fact that it encourages researchers to produce high quality work, the fact that it can combine citation impact with publication activity and that is also not affected by single papers that have many citations.

Besides its popularity, a lot of criticism has been raised, too (see, e.g., Adler, Ewing, & Taylor, 2009; Schreiber, 2007a; Vinkler, 2007; Meho, 2007), and various modifications and generalizations have appeared (see, e.g., Egghe, 2006a; Jin, Liang, Rousseau, & Egghe, 2007; Schreiber, 2007b, 2008b; Sidiropoulos, Katsaros, & Manolopoulos 2006; Tol, 2009).

The h-index is robust to the numbers of citations received by the papers belonging to the h-core (i.e. the papers receiving h or more citations). To relax this “robustness”, various modifications have appeared in the literature, e.g. the g-index (Egghe, 2006), the A-index (Jin, 2006), the R-index (Jin et al., 2007), and the hw-index (Egghe and Rousseau, 2008). Since the suggestion of the Hirsch index a lot more h-type variants have been devised in order to overcome this “robustness” [e.g. the g-index (Egghe, 2006), the A-index (Jin, 2006), the R-index (Jin et al., 2007), and the hw-index (Egghe and Rousseau, 2008)]. However, more and more voices argue against the usefulness of all these measures (see e.g. Bornmann et al., 2009b; Adler, Ewing, & Taylor, 2009; Schreiber, 2007a; Vinkler, 2007; Meho, 2007). In the same vein, van Noorden (2010) states that “many metrics correlate strongly with one another, suggesting that they are capturing much of the same information about the data they describe”.

After a comparison of some of the more important variants, Bornmann, Mutz, and Daniel (2008) by performing exploratory factor analysis on a set of some of the most important h-type indices, including the h-index, conclude that indices can be categorized into two basic categories: those that came to the conclusion that essentially there are two types of indices,

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