2. Factorial analysis
After performing correlation analysis, a value of 5,629.85 (P< .001) for a chi-squared approximation of BS and 0.700 for KMO confirmed the suitability of factorial analysis. In this sense, we have identified six components that could explain the previously observed correlation data. These factors are labelled as F1, F2, F3, F4, F5 and F6 (Figure 2). Positive and negative results are indicated with green and red colour lines, respectively. The factor F1 shows the relation between WoS citations and the number of Mendeley readers. These two variables appear together, probably describing the traditional scholarly impact of TE research. The remaining factors most likely account for a different type of scientific impact in TE research.
In this regard, F3 acts as a common factor for Google and Policy mentions suggesting a relation between governmental and legal actions in TE and a widespread search source such as Google. Blogs, News and Facebook mention form a separate factor (F5). These three web-based platforms are related to a social dimension of TE diffusion and probably articulate a common factor concerning the public communication of scientific research. Finally, the number of mentions on Twitter are strongly tied to a unique factor (F4). This fact could be explained by a particular structure and behaviour of TE research within the Twitter social network.