Multivariate dynamics of Spanish universities in international rankings

Abstract

Global rankings help boost the international reputation of universities, which thus attempt to achieve good positions on them. These rankings attract great interest each year and are followed attentively by stakeholders in higher education. This paper investigates the trajectory of Spanish universities in the ARWU and THE rankings over the last 5 years using the dynamic biplot technique to study the relationship between a multivariate dataset obtained at more than one time point. The results demonstrate that Spanish universities achieve low positions on international rankings when analyzed using this multivariate and dynamic approach. Indeed, only a small percentage occupy good positions in both studied rankings and stand out in terms of some of the indicators, whereas most achieve weak scores in the global context. Spanish universities should attempt to improve this situation, since the prestige resulting from a good position on these lists will always be beneficial in terms of the visibility of both the universities themselves and the whole Spanish university system.

Full text

e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 1
Multivariate dynamics of Spanish
universities in international rankings
María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-
Galindo; Helena Martín-Rodero; Claudio Ruff-Escobar; María-Purificación Galindo-
Villardón
How to cite this arcle:
Gómez-Marcos, María-Teresa; Ruiz-Toledo, Marcelo; Vicente-Galindo, María-Puricación; Marn-Rodero,
Helena; Ru-Escobar, Claudio; Galindo-Villardón, María-Puricación (2021). “Mulvariate dynamics of Spani-
sh universies in internaonal rankings”. Profesional de la información, v. 30, n. 2, e300210.
hps://doi.org/10.3145/epi.2021.mar.10
Arcle received January 21st 2021
Final acceptance: February 17th 2021
Nota: Este arculo se puede leer en español en:
hp://www.profesionaldelainformacion.com/contenidos/2021/mar/gomez-ruiz-vicente-marn-ru-galindo_es.pdf
María-Teresa Gómez-Marcos *
hps://orcid.org/0000-0002-4368-7012
Universidad de Salamanca
Facultad de Medicina
Departamento de Estadísca
Alfonso X El Sabio, s/n.
37007 Salamanca, Spain
mgomezma@usal.es
Marcelo Ruiz-Toledo
hps://orcid.org/0000-0003-1865-7839
Universidad de Salamanca
Departamento de Estadísca
mruiz@usal.es
Universidad Bernardo O´Higgins
Avenida Viel, 1497. Sanago, Chile
mruiz@ubo.cl
María-Purificación Vicente-Galindo
hps://orcid.org/0000-0002-5854-273X
Universidad de Salamanca
Facultad de Medicina
Departamento de Estadísca
Alfonso X El Sabio, s/n.
37007 Salamanca, Spain
purivg@usal.es
Helena Martín-Rodero
hps://orcid.org/0000-0002-6698-9240
Universidad de Salamanca
Facultad de Medicina
Departamento de Estadísca
Alfonso X El Sabio, s/n.
37007 Salamanca, Spain
helena@usal.es
Claudio Ruff-Escobar
hps://orcid.org/0000-0003-1954-0800
Universidad Bernardo O’Higgins
Avenida Viel, 1497. Sanago, Chile
cru@ubo.cl
María-Purificación Galindo-
Villardón
hps://orcid.org/0000-0001-6977-7545
Universidad de Salamanca
Departamento de Estadísca
Alfonso X El Sabio, s/n.
37007 Salamanca, Spain
pgalindo@usal.es
Abstract
Global rankings help boost the internaonal reputaon of universies, which thus aempt to achieve good posions on
them. These rankings aract great interest each year and are followed aenvely by stakeholders in higher educaon.
This paper invesgates the trajectory of Spanish universies in the ARWU and THE rankings over the last 5 years using
the dynamic biplot technique to study the relaonship between a mulvariate dataset obtained at more than one me
point. The results demonstrate that Spanish universies achieve low posions on internaonal rankings when analyzed
using this mulvariate and dynamic approach. Indeed, only a small percentage occupy good posions in both studied
rankings and stand out in terms of some of the indicators, whereas most achieve weak scores in the global context. Spa-
nish universies should aempt to improve this situaon, since the presge resulng from a good posion on these lists
will always be benecial in terms of the visibility of both the universies themselves and the whole Spanish university
system.

María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-Galindo; Helena Martín-Rodero;
Claudio Ruff-Escobar; María-Purificación Galindo-Villardón
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 2
Keywords
Higher educaon; Internaonalizaon; World class; Universies; Shanghai Ranking; ARWU; THE; Dynamic biplot; Biplot;
Spanish universies.
1. Introducon
The internaonal landscape of higher educaon has experienced a great boost in recent years due to the globalizaon and
commodicaon of knowledge (Knight, 2004). The educaonal market has become universal, borders have disappeared,
and barriers have become blurred. To compete in this new scenario, universies need to improve their global posioning
by designing strategies to increase their visibility and project their oering, capabilies, and appeal (Vázquez-García, 2015).
Internaonalizaon can be dened as the inclusion of the internaonal dimension into a university’s strategy regarding
its teaching, research, and transfer missions, as well as the projecon of its oering and capabilies (Knight, 2004). This
is, therefore, a concept with mulple manifestaons, including the expansion of an organizaon’s visibility, recognion,
and scope of acon. One element to help promote this type of internaonalizaon is university rankings, acng as a sta-
ge on which the compeon to achieve global status is played out (Rodríguez-Espinar, 2018). These classicaons are
now impossible to ignore and are presented as arbiters of universal academic excellence (Vázquez-García, 2015). Their
substanal impact on the internaonalizaon of universies has been the subject of numerous invesgaons (Mar-
ginson, 2012; Ordorika, Rodríguez-Gómez, 2010; De-Wit, 2017; Knight, 2014; Collins; Park, 2016). Although the main
classicaons available worldwide include few indicators that measure the degree of internaonalizaon, achieving a
good posion in them has a great inuence on world presge, which in turn is independent of the degree of internao-
nalizaon exhibited by the funcons of that organizaon (Casani; Rodríguez-Pomeda, 2017).
The rst two rankings to be established were the Academic Ranking of World Universies (ARWU) and the Times Higher
Educaon World University Rankings (THE), and these are sll considered to be two of the best known and most inuen-
al today (Safón, 2012; Marginson, 2007; Locke et al., 2008; Ordorika; Rodríguez-Gómez, 2010; Rauhvargers, 2011).
They were later joined by others such as the QS World University Rankings, which split o from the THE ranking in 2010,
and the SCImago Instuons Ranking and Leiden World Ranking, which focus exclusively on research results.
Academic Ranking of World Universies (ARWU)
The ARWU was published for the rst me in 2003 under the name Shanghai Jiao Tong Academic Ranking of World Uni-
versies, being produced by the Jiao Tong University (China) Center for World-Class Universies (CWCU), which is why it
is popularly known as the Shanghai Ranking. It ranks universies based on four criteria:
- teaching quality (10%)
- academic sta quality (40%)
- research output (40%)
- organizaon size (10%)
Teaching quality is measured by the number of alumni who have received a Nobel Prize or Fields Medal (10%). Fur-
thermore, to measure the quality of the teaching sta, the total number of sta who have won Nobel Prizes in physics,
chemistry, medicine, and economics or Fields Medals in mathemacs (20%) is considered. Similarly, to measure the
quality of the teaching sta, the number of highly cited researchers according to the list published by Clarivate Analycs
(20%) is measured. Because of this indicator, such researchers have become an important asset to their universies and
a frenzied race for their recruitment has ensued (Docampo; Torres-Salinas, 2013).
Research output is determined based on the number of Nature and Science arcles published (20%) and the number of
arcles indexed in the Science Citaon Index Expanded (SCIE) and Social Sciences Citaon Index (SSCI) over the previous
ve years (20%). The nal criterion in the ranking is the size of the organizaon (10%).
The ARWU is the only internaonal ranking that obtains its data independently of the analyzed instuons (Monta-
né-López; Beltrán-Llavador; Teodoro, 2017). The main cricisms leveled at this ranking focus on its research-oriented
indicators (Ordorika, 2015; Tomàs-Folch et al., 2015) and the inclusion of the Nobel Prize winner category, as these
exclude a large number of universies from classicaon (Yong-Amaya; Zambrano-Zambrano; Ruso-Armada, 2018).
Despite this cricism and some reluctance, it has become the basic reference worldwide (Docampo et al., 2012) and is
considered to be the most outstanding academic classicaon on the global stage (Docampo; Cram, 2015).
THE World University Ranking
The next internaonal ranking to emerge in the eld of higher educaon was the THE ranking, created by the company
Times Higher Educaon in 2010. The THE ranking is based on 13 indicators, grouped into ve dimensions:
- teaching (30%)
- research (30%)
- citaons (30%)
- internaonal perspecve (7.5%)
- income from industry (2.5%).

Multivariate dynamics of Spanish universities in international rankings
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 3
The teaching dimension is determined through ve variables, although the survey on the reputaon of teachers and
researchers accounts for half the weighng in this dimension (15%). It also measures:
- teacher-to-student rao (4.5%)
- proporon of doctoral students and graduates (2.25%)
- percentage of doctoral students and professors (6%)
- instuonal income (2.25%).
The research dimension is determined by three variables:
- researcher reputaon, collected via surveys with academics (18%)
- research income per academic (6%)
- scienc output, quaned by the number of publicaons indexed in Scopus per academic (6%).
The research impact is also determined based on the citaons received in publicaons indexed in Scopus (30%).
The two concepts with least weight in this ranking are internaonal perspecve and knowledge transfer. The former is
measured by:
- percentage of internaonal students (2.5%)
- percentage of internaonal sta (2.5%)
- co-authorship of internaonal works published in the last ve years (2.5%).
The laer captures the research income obtained from industry (2.5%).
One of the major cricisms leveled at this ranking is movated by the fact that it is largely based on reputaon surveys
and condenal data provided by universies (Sanz-Casado, 2015). Further cricism stems from the incomplete and
confusing research income component since it is not standardized across countries (Marginson, 2014).
Although considered to be the most inuenal, neither of these internaonal rankings include indicators with a high
weighng for internaonalizaon. Indeed, the ARWU ranking does not include any variables that directly measure this
concept (Delgado-Márquez; Hurtado-Torres; Bondar, 2011), while the THE ranking does include such an internaonali-
zaon indicator but gives it a low weighng in the overall ranking (7.5%). Despite this, both classicaons are considered
key for measuring projecon at the global level and have a strong impact on naonal and instuonal policies and stra-
tegies for the internaonalizaon of higher educaon organizaons (Collins; Park, 2016; De-Wit, 2017).
This link between rankings and internaonalizaon strategies has resulted in dierenaon within naonal systems
through the separaon of an elite sector made up of world-class universies and another consisng of more locally
oriented, naonal establishments (De-Wit; Altbach, 2020). World-class universies are characterized by high-ranking
research, a culture of excellence, and a brand that transcends naonal borders (Douglass, 2014). They are posioned
in the upper echelons of internaonal rankings and are recognized not only by other universies but also outside the
educaon sector. Their reputaon for research and teaching makes it easy for them to operate in a global market and to
internaonalize many of their funcons (Douglass, 2016).
Global rankings are closely followed each year by dierent stakeholders in higher educaon. Achieving a high ranking
sparks great interest, even in Spanish universies. The promoon of internaonalizaon through rankings can lead to
increased visibility and thereby enhance the image of the whole Spanish university system (Pérez-Esparrells, 2017).
The purpose of the current study is to examine the posioning of Spanish instuons in two global rankings, as well
as their trajectory over the last 5 years. The aim is to idenfy the instuons that have managed to be classied in the
global rankings and those that can aspire to compete in the world-class group, as well as to analyze their trajectory and
disncve characteriscs.
2. Methodology
Internaonal rankings have been the subject of numerous invesgaons focusing on the idencaon of the correla-
ons and contribuons of dierent indicators. Techniques such as factor analysis (Luque-Marnez; Faraoni; Doña-To-
ledo, 2018), principal components (Docampo; Cram, 2015), regression analysis (Safón, 2019), and correlaon analysis
(Shehaa; Mahmood, 2016) have been applied to study such classicaons exhausvely. However, it is noted that the
research literature lacks studies focused on the use of dynamic mulvariate methods to observe the internaonal pro-
jecon of universies over me.
To carry out this research, the two oldest and most well-known global rankings were selected, viz. the ARWU and THE
ranking. The ARWU is based on objecve data, while the THE ranking uses reputaon surveys. This also means that
these two classicaon systems can provide a complementary snapshot of university internaonalizaon. The following
websites for the rankings were used as sources for the database design:
- Academic Ranking of World Universies (ARWU)
hp://www.shanghairanking.com
- Times Higher Educaon World University Rankings (THE)
hp://www.meshighereducaon.com/world-university-rankings

María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-Galindo; Helena Martín-Rodero;
Claudio Ruff-Escobar; María-Purificación Galindo-Villardón
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 4
The values of the variables for Spanish universies were collected for the years 2016 to 2020.
Dynamic biplots were selected as the technique to evaluate the relaonship between the mulvariate dataset analyzed
at more than one me point. This technique was proposed by Egido-Miguélez (2015) as an extension of biplot methods
to treat three-way data, oering the advantage that, instead of taking a consensus matrix as a reference, any of the
individual matrices can be chosen and the corresponding trajectories studied. The three-way data of the matrix include:
- rows corresponding to universies
- columns corresponding to the indicators of each ranking
- the situaon at various me points.
The dynamic biplot is developed in two stages:
- biplot analysis of the two-way data matrix for the reference year
- projecon on the biplot graph obtained in the previous stage of the remaining me points to be studied, revealing
their trajectory throughout dierent contexts.
The rst step studies the mulvariate correlaons between variables and individuals, or both, while the second step
captures the dynamic nature of the analysis.
The dynamic biplot technique can be applied using any factorizaon, but the best simultaneous representaon of the
trajectory of variables and points is provided by the HJ-biplot, as it can represent both types of elements with the hi-
ghest quality (Egido-Miguélez, 2015). The HJ-biplot (Galindo-Villardón, 1986) simultaneously represents the universies
and indicators from each ranking on a plane, where the similarity between universies is inversely proporonal to the
Euclidean distance between them. Meanwhile, the angles between indicators enable an assessment of the degree of
covariaon:
- acute angles indicate direct correlaon
- obtuse angles indicate inverse correlaon
- right angles indicate independence.
The length of the vectors approximates the standard deviaon of the indicators.
The order of the orthogonal projecons of each row marker onto a column marker approximates the order of each row ele-
ments (universies) in that column (indicator). The larger
the projecon of a point onto a vector, the more a univer-
sity deviates from the mean of that variable.
The reference axes of the biplot plane on which the uni-
versies and indicators are represented are the principal
components obtained as eigenvectors of the covariance
matrix between indicators. The associated eigenvalues
enable an assessment of the amount of informaon that
each biplot plane explains (the explained variance). The
angle that each indicator makes with the axis of factor 1
and 2 is known as the contribuon of each factor to the
variability of that indicator, whereas the sum of the two
contribuons determines the quality of the representa-
on in the factor plane.
The analysis was carried out using R with the dynBiplot-
GUI package, created by Egido-Miguélez (2015). The dy-
namic biplot technique nds applicaon in the eld of
economics, but to the best of the authors’ knowledge,
it has not been applied to analyze universies based on
their performance in rankings.
3. Results
For both internaonal classicaons, all the Spanish uni-
versies and their weighted indicators were analyzed. To
provide an inial overview, the mean and rate of change
of each university for each of the variables were calcu-
lated. The reference situaon used to construct the bi-
plot was set as the year 2020, corresponding to the most
recent situaon and, therefore, the most interesng for
this study. The data for the reference period were cente-
red and standardized.
Table 1. Universities included in the ARWU and THE rankings
ARWU THE
Barcelona Pompeu Fabra
València Autònoma de Barcelona
Complutense de Madrid Barcelona
Granada Autónoma de Madrid
Autònoma de Barcelona Navarra
Autónoma de Madrid València
País Vasco Complutense de Madrid
Politècnica de València Rovira i Virgili
Pompeu Fabra Alcalá de Henares
Santiago de Compostela País Vasco
Rovira i Virgili Granada
Politècnica de Catalunya La Laguna
 Oviedo
 Politècnica de Catalunya
 Salamanca
 Santiago de Compostela
 A Coruña
 Carlos III de Madrid
 Castilla La Mancha
 Murcia
 Politècnica de València
 Sevilla
 Politécnica de Madrid
 Vigo
Zaragoza

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In the HJ-biplot graphs, the indicators are represented by vectors, while the universies are idened by points, labeled
by their abbreviated name. Table 1 presents the universies that were included in the two rankings over the 5-year pe-
riod, ordered according to their posion in 2020.
There are 12 universies classied in the ARWU, and 25 universies in the THE ranking. Therefore, it is easier for Spanish
instuons to be included in the laer classicaon.
3.1. The ARWU
Table 2 presents the results for the indicators of the ARWU ranking in each year for each university, as well as their mean
and rate of change.
Table 2. ARWU indicators, averages, and rates of change (2016-2020)
University Year Alumni HiCi N & SPUB PCP
Autònoma de Barcelona
2016 0.00 0.00 12.10 45.20 20.70
2017 0.00 0.00 13.20 46.30 21.60
2018 0.00 0.00 11.20 47.80 22.70
2019 0.00 7.30 11.30 48.50 23.40
2020 0.00 9.90 12.30 46.70 23.30
Average   3.44 12.02 46.90 22.34
Rate of change    1.65% 3.32% 12.56%
Autónoma de Madrid
2016 0.00 14.50 10.90 38.40 18.40
2017 0.00 10.90 12.40 39.00 18.70
2018 0.00 9.60 12.80 40.30 19.50
2019 0.00 7.30 12.60 40.70 19.40
2020 0.00 7.00 11.60 40.00 19.30
Average   9.86 12.06 39.68 19.06
Rate of change   -51.72% 6.42% 4.17% 4.89%
Barcelona
2016 0.00 17.80 12.00 50.60 19.90
2017 0.00 15.40 12.30 51.00 20.40
2018 0.00 27.10 12.50 53.30 23.20
2019 0.00 24.30 13.30 51.30 21.90
2020 0.00 22.10 12.90 50.70 21.70
Average   21.34 12.60 51.38 21.42
Rate of change   24.16% 7.50% 0.20% 9.05%
Complutense de Madrid
2016 19.20 0.00 9.10 42.30 13.20
2017 19.00 0.00 9.80 41.90 13.50
2018 19.00 0.00 12.20 44.00 14.50
2019 17.70 10.40 12.60 43.90 14.90
2020 17.20 9.90 11.00 45.10 15.30
Average  18.42 4.06 10.94 43.44 14.28
Rate of change  -10.42%  20.88% 6.62% 15.91%
Granada
2016 0.00 22.90 5.30 40.70 16.00
2017 0.00 24.40 6.20 40.30 16.40
2018 0.00 23.50 4.20 40.80 16.30
2019 0.00 23.20 5.30 41.60 16.10
2020 0.00 21.00 6.30 42.60 16.40
Average   23.00 5.46 41.20 16.24
Rate of change   -8.30% 18.87% 4.67% 2.50%
País Vasco
2016 0.00 0.00 9.20 36.40 14.40
2017 0.00 0.00 11.70 37.30 15.30
2018 0.00 9.60 12.20 38.10 16.60
2019 0.00 0.00 11.60 39.20 16.40
2020 0.00 7.00 12.50 38.80 16.90
Average   3.32 11.44 37.96 15.92
Rate of change    35.87% 6.59% 17.36%

María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-Galindo; Helena Martín-Rodero;
Claudio Ruff-Escobar; María-Purificación Galindo-Villardón
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University Year Alumni HiCi N & SPUB PCP
Politècnica de Catalunya
2016 0.00 14.50 8.00 27.70 15.80
2017 0.00 0.00 6.40 27.70 14.10
2018 0.00 0.00 6.00 27.70 14.20
2019 0.00 0.00 6.70 28.20 14.60
2020 0.00 0.00 4.50 27.80 14.40
Average   2.90 6.32 27.82 14.62
Rate of change    -43.75% 0.36% -8.86%
Politècnica de València
2016 0.00 17.80 7.60 31.80 16.10
2017 0.00 10.90 7.50 32.40 15.30
2018 0.00 9.60 8.90 32.40 15.10
2019 0.00 10.40 8.20 34.20 15.10
2020 0.00 14.00 8.00 34.00 15.80
Average   12.54 8.04 32.96 15.48
Rate of change   -21.35% 5.26% 6.92% -1.86%
Pompeu Fabra
2016 0.00 0.00 19.70 27.20 34.30
2017 0.00 10.90 20.10 27.80 37.70
2018 0.00 13.50 20.10 28.50 39.40
2019 0.00 0.00 19.70 28.90 36.30
2020 0.00 0.00 16.20 28.90 34.90
Average   4.88 19.16 28.26 36.52
Rate of change    -17.77% 6.25% 1.75%
Rovira i Virgili
2016 0.00 10.30 5.30 23.20 21.50
2017 0.00 0.00 4.90 23.80 20.30
2018 0.00 0.00 4.60 23.30 20.30
2019 0.00 7.30 5.20 24.60 22.00
2020 0.00 7.00 4.60 24.70 22.20
Average   4.92 4.92 23.92 21.26
Rate of change   -32.04% -13.21% 6.47% 3.26%
Santiago de Compostela
2016 0.00 14.50 6.20 30.90 14.80
2017 0.00 15.40 6.90 31.30 15.50
2018 0.00 13.50 6.30 32.30 15.70
2019 0.00 7.30 5.80 32.60 14.90
2020 0.00 7.00 6.10 32.50 15.10
Average   11.54 6.26 31.92 15.20
Rate of change   -51.72% -1.61% 5.18% 2.03%
València
2016 0.00 0.00 6.90 41.50 15.00
2017 0.00 0.00 5.50 43.00 15.70
2018 0.00 0.00 5.70 44.30 16.40
2019 0.00 14.70 6.90 45.40 17.20
2020 0.00 12.10 7.10 46.30 17.50
Average   5.36 6.42 44.10 16.36
Rate of change    2.90% 11.507% 16.607%
HiCi (highly cited researchers), N & S (Nature and Science articles), PUB (articles in SCIE and SSCI), PCP (size of organization).
The results presented in Table 2 show that the Universi-
dad Complutense de Madrid was the only university that
managed to achieve a posion on the Alumni indicator
with an average value of 18.42. The Universidad de Gra-
nada obtained the highest average on HiCi (23.00), the
Universitat de Barcelona on PUB (51.38), and the Uni-
versitat Pompeu Fabra on N & S (19.16) and PCP (36.52).
Regarding the rate of change of each variable, the uni-
Global rankings have a great impact on
the prestige and internationalization of
universities. Universities that perform
well in these classifications will have
greater capacity to attract students and
academics from other countries.

Multivariate dynamics of Spanish universities in international rankings
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 7
versies that suered the greatest decreases
were the Autónoma de Madrid and Sanago de
Compostela on HiCi (-51.72%) and Politècnica de
Catalunya on N & S (-43.75%). The greatest posi-
ve variaons were recorded for the Universitat
de València on PUB (11.57%) and the Universitat
Politècnica de València on PCP (17.36%).
The informaon captured in the HJ-biplot is presented in Table 3. Three axes were retained because a very high accumu-
lated inera (91.85%) was achieved, being sucient to characterize with some certainty the posioning of the universi-
es in the ARWU ranking with respect to all the variables considered.
Table 4 presents the contribuon of each factor axis to the variability of the ranking indicators. The variable related to
academics with Nobel Prizes or Fields Medals could not be included because no Spanish university obtained a score on it.
Table 4. Contribution of each factor axis to the variability of the ARWU indicators
Variable Axis 1 Axis 2 Axis 3
Alumni (alumni with Nobel Prize or Fields Medal) 153 6 801
HiCi (highly cited researchers) 708 1 185
N & S (Nature and Science articles) 0 918 11
PUB (articles in SCIE and SSCI) 755 155 7
PCP (size of organization) 266 600 26
Considering the contribuons of each factor to the entries in each column, it was observed that all the variables could
be interpreted in the factor plane 1–2 or 1–3, resulng in a good quality of representaon. PUB and HiCi made a strong
contribuon to axis 1. Regarding N & S, axis 2 provided informaon of interest, while axis 3 made the greatest contribu-
on to axis 3.
Figure 1 shows the HJ-biplot for the 2020 data matrix, providing the best possible knowledge regarding the reference.
A strong and direct correlaon is observed between HiCi and PUB, with the laer variable also covarying directly with N
& S and Alumni. The only indirect correlaon appears between the PCP and HiCi indicators. However, the laer variable
related to highly cited researchers presented independence from Alumni and a very weak connecon with N & S.
Regarding the ranks of the 12 universies analyzed, a good quality of representaon was not obtained for only 2, which
are thus omied from the factor planes. Universies were posioned in dierent parts of the graph, establishing various
groups based on the similarity between their characteriscs.
The Universitat de Barcelona, the best-classied Spanish university in the ARWU ranking, showed high values on the
HiCi and PUB variables, each with a weighng of 20% in the nal ranking. This university appeared close to the Uni-
versidades de València and Granada, which were ranked second and fourth, respecvely. If we compare these po-
Table 3. ARWU explained variance
Axes Eigenvalue Explained
variance
Cumulative
variance
Axis 1 4.55 37.65 37.65
Axis 2 4.30 33.62 71.27
Axis 3 3.37 20.58 91.85
Figure 1. Factor representation HJ-biplots for the ARWU ranking (2020), planes 1-2 and 1-3.

María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-Galindo; Helena Martín-Rodero;
Claudio Ruff-Escobar; María-Purificación Galindo-Villardón
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 8
sions with the averages and rates
of change presented in Table 2, it
is observed that the Universitat
de Barcelona obtained the highest
average value on PUB (51.38) and
the Universidad de Granada on HiCi
(23.00). However, the Universitat de
València obtained a low average on
this laer variable (5.36) because it
failed to make the ranking in the rst
3 years. Table 2 also demonstrates
that the Universitat de Barcelona ex-
hibited its highest rate of change on
HiCi (24.16%), while the Universidad
de Granada experienced a decrease
(−8.30%).
Universitat Pompeu Fabra, ranked
ninth, stood out for its high values
on the PCP indicator, which includes
the size of the organizaon, calcula-
ted as a weighng on all the varia-
bles. Its average was also very high
(36.52) on this indicator (Table 2),
although the rate of change was not
signicant (1.75%). The only univer-
sity that stood out with high values
for alumni with Nobel Prizes or Fields
Medals (Alumni) was the Universi-
dad Complutense de Madrid, ranked
third in the nal ARWU list. The Universitat Autònoma de Barcelona was included based on the number of published
arcles in Nature and Science (N & S), a variable with a weighng of 20% in the ranking. Table 2 demonstrates that its
average on this variable was also high (12.02), albeit below that of the Universitat Pompeu Fabra (19.16), Universitat de
Barcelona (12.60), and Universidad Autónoma de Madrid (12.06). The Universitat Politècnica de València was close to
the highly cited researchers indicator, while the last three instuons listed (Sanago de Compostela, Rovira i Virgili, and
Politècnica de Catalunya) all appeared far from the indicators shown, thus indicang low values. Table 2 shows that the-
se three organizaons exhibited signicant decreases according to the rates of change of some of the ranking indicators.
Figure 2 shows the dynamic biplot, projecng the situaon of each university in each year according to its trajectory.
The Universitat de Barcelona showed the greatest increase in the value of the PUB variable during 2018, with a reducon
in the subsequent two years. The Universitat Autònoma de Barcelona showed the greatest variaon in its trajectory in
terms of the indicators, as it was characterized by PCP in 2016, 2017, and 2018 but approached N & S in subsequent
years. The Universitat Pompeu Fabra showed an irregular trajectory but always characterized by the indicator related to
organizaon size. Over the last two years, the Universitat de València showed considerable progress towards the highly
cited researchers variable, thus approaching the Univer-
sidad de Granada, which exhibited a less pronounced
trajectory. The other instuons generally showed tra-
jectories that approached the variables but remained far
from them.
In plane 1–3, the Universidad Complutense de Madrid
was always characterized by the Alumni variable.
Global rankings are closely followed
each by different stakeholders in higher
education. Achieving a high ranking
sparks great interest, even in Spanish
universities
Figure 2. Dynamic biplot factorial representation of the ARWU ranking, plane 1-2.

Multivariate dynamics of Spanish universities in international rankings
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 9
3.2. The THE Ranking
Table 5 presents the results for the universies in the THE indicators for the dierent years, as well as the mean and rate
of change for each.
Table 5. THE ranking indicators, averages, and rates of change (2016–2020)
University Year Teaching Research Citations Industry Internationa-
lization
Alcalá
2016 17.60 11.20 28.30 43.30 50.00
2017 19.60 11.50 31.80 42.20 55.80
2018 20.40 12.20 45.90 40.50 59.80
2019 30.40 14.50 37.50 41.00 61.60
2020 18.50 15.70 43.20 42.50 59.00
Average  21.30 13.02 37.34 41.90 57.24
Rate of change  5.11% 40.18% 52.65% −1.85% 18.00%
Autònoma de Barcelona
2016 40.30 40.00 83.80 34.90 50.30
2017 39.40 36.40 86.70 39.90 52.30
2018 43.30 36.10 89.50 42.10 60.10
2019 43.90 36.50 92.40 41.30 62.20
2020 40.90 36.10 92.90 44.80 64.30
Average  41.56 37.02 89.06 40.60 57.84
Rate of change  1.49% −9.75% 10.86% 28.37% 27.83%
Autónoma de Madrid
2016 35.60 30.90 46.90 33.00 48.60
2017 32.30 28.30 57.40 35.80 51.60
2018 33.00 28.10 58.40 34.90 49.00
2019 33.90 28.40 64.80 37.80 51.10
2020 40.10 28.70 74.50 38.60 51.50
Average  34.98 28.88 60.40 36.02 50.36
Rate of change  12.64% −7.12% 58.85% 16.97% 5.97%
Barcelona
2016 38.50 37.40 78.90 31.10 49.20
2017 33.70 33.00 81.30 35.30 49.30
2018 32.40 32.50 83.20 34.00 50.60
2019 37.70 32.30 85.10 40.10 52.60
2020 37.30 32.50 87.60 41.20 54.70
Average  35.92 33.54 83.22 36.34 51.28
Rate of change  −3.12% −13.10% 11.03% 32.48% 11.18%
Castilla-La Mancha
2016 18.40 10.30 30.50 29.70 28.60
2017 16.80 10.80 35.30 34.30 30.50
2018 18.10 10.40 28.70 33.70 33.30
2019 20.30 11.70 31.10 35.90 35.20
2020 16.60 12.50 32.70 36.00 37.00
Average  18.04 11.14 31.66 33.92 32.92
Rate of change  −9.78% 21.36% 7.21% 21.21% 29.37%
Carlos III de Madrid
2016 23.20 17.40 24.80 34.80 44.70
2017 24.70 15.90 29.60 37.20 53.10
2018 24.60 15.30 33.60 36.30 56.80
2019 26.40 16.00 37.30 37.80 58.60
2020 24.40 16.30 34.90 38.20 60.20
Average  24.66 16.18 32.04 36.86 54.68
Rate of change  5.17% −6.32% 40.73% 9.77% 34.68%

María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-Galindo; Helena Martín-Rodero;
Claudio Ruff-Escobar; María-Purificación Galindo-Villardón
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University Year Teaching Research Citations Industry Internationa-
lization
Complutense de Madrid
2016 33.20 27.60 31.20 30.90 39.10
2017 30.70 27.10 36.70 36.00 40.10
2018 35.20 27.40 38.50 33.50 41.70
2019 42.40 28.40 42.70 35.60 44.30
2020 35.40 28.90 47.20 36.10 44.00
Average  35.38 27.88 39.26 34.42 41.84
Rate of change  6.63% 4.71% 51.28% 16.83% 12.53%
A Coruña
2016 18.30 10.00 16.60 38.20 23.40
2017 17.60 10.90 23.70 35.50 27.30
2018 19.10 11.20 23.70 34.30 30.60
2019 22.80 12.40 26.10 35.60 30.90
2020 20.30 13.70 32.50 36.30 31.60
Average  19.62 11.64 24.52 35.98 28.76
Rate of change  10.93% 37.00% 95.78% −4.97% 35.04%
Granada
2016 24.30 14.70 45.80 29.40 36.40
2017 21.90 16.80 46.30 33.20 43.10
2018 22.50 19.20 46.80 32.80 50.10
2019 23.50 19.00 48.30 35.00 47.00
2020 19.40 20.90 52.00 35.60 48.10
Average  22.32 18.12 47.84 33.20 44.94
Rate of change  −20.16% 42.18% 13.54% 21.09% 32.14%
La Laguna
2016 16.90 10.00 44.80 28.50 44.70
2017 16.90 9.60 48.50 32.70 47.10
2018 18.10 9.70 57.50 32.40 46.60
2019 24.30 11.50 62.30 35.10 46.70
2020 19.30 11.50 67.80 35.20 46.90
Average  19.10 10.46 56.18 32.78 46.40
Rate of change  14.20% 15.00% 51.34% 23.51% 4.92%
Murcia
2016 19.30 11.70 28.00 29.50 28.30
2017 18.40 12.70 31.00 33.50 32.00
2018 20.10 12.40 32.10 33.00 34.70
2019 27.40 13.20 32.20 35.30 37.60
2020 22.30 13.80 32.60 35.90 38.50
Average  21.50 12.76 31.18 33.44 34.22
Rate of change  15.54% 17.95% 16.43% 21.69% 36.04%
Navarra
2016 31.90 20.80 57.50 63.50 52.60
2017 29.70 23.90 65.30 55.60 55.60
2018 27.90 24.50 74.60 63.90 59.70
2019 34.10 24.20 82.00 66.60 63.20
2020 30.40 27.90 80.30 85.50 65.10
Average  30.80 24.26 71.94 67.02 59.24
Rate of change  −4.70% 34.13% 39.65% 34.65% 23.76%
Oviedo
2016 19.50 10.80 41.90 34.10 36.20
2017 18.30 12.40 44.20 33.40 30.50
2018 27.00 13.50 49.10 34.10 31.90
2019 25.50 14.70 50.80 38.00 34.10
2020 16.80 15.20 54.80 38.50 34.40
Average  21.42 13.32 48.16 35.62 33.42
Rate of change  −13.85% 40.74% 30.79% 12.90% −4.97%

Multivariate dynamics of Spanish universities in international rankings
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 11
University Year Teaching Research Citations Industry Internationa-
lization
País Vasco
2016 18.20 19.60 43.10 30.30 34.90
2017 20.90 14.30 50.20 34.70 37.90
2018 21.00 14.80 51.40 34.80 40.80
2019 20.40 16.50 50.00 36.20 40.10
2020 22.00 17.10 47.30 37.10 41.50
Average  20.50 16.46 48.40 34.62 39.04
Rate of change  20.88% −12.76% 9.74% 22.44% 18.91%
Politècnica de Catalunya
2016 25.20 14.80 44.70 40.90 63.90
2017 27.10 17.50 51.20 41.50 51.40
2018 27.10 17.60 55.30 41.60 53.20
2019 29.70 17.30 53.70 40.90 56.20
2020 23.70 17.20 56.90 41.20 59.10
Average  26.56 16.88 52.36 41.22 56.76
Rate of change  −5.95% 16.22% 27.29% 0.73% −7.51%
Politécnica de Madrid
2016 21.80 14.60 24.50 38.30 39.50
2017 21.90 13.70 30.80 39.10 41.90
2018 23.80 13.60 34.80 43.00 45.00
2019 31.10 13.90 37.90 42.60 47.50
2020 22.60 14.90 37.70 42.40 49.10
Average  24.24 14.14 33.14 41.08 44.60
Rate of change  3.67% 2.05% 53.88% 10.70% 24.30%
Politècnica de València
2016 20.30 12.70 34.30 43.80 32.90
2017 22.10 24.80 43.90 44.30 41.90
2018 24.00 25.40 44.40 43.50 43.60
2019 25.40 12.00 45.20 44.50 47.50
2020 22.10 11.80 41.30 44.80 50.00
Average  22.78 17.34 41.82 44.18 43.18
Rate of change  8.87% −7.09% 20.41% 2.28% 51.98%
Pompeu Fabra
2016 32.90 28.00 90.70 37.20 63.30
2017 30.30 33.00 93.10 40.50 65.10
2018 34.70 38.90 97.10 40.00 62.30
2019 40.00 39.10 95.70 42.40 64.30
2020 37.70 40.10 94.40 44.50 66.50
Average  35.12 35.82 94.20 40.92 64.30
Rate of change  14.59% 43.21% 4.08% 19.62% 5.06%
Rovira i Virgili
2016 20.80 14.80 66.90 30.90 41.50
2017 21.50 15.80 72.10 35.20 45.50
2018 22.20 17.20 76.40 34.50 47.70
2019 24.20 20.20 76.20 36.00 49.10
2020 23.70 21.00 67.60 36.60 51.10
Average  22.48 17.80 71.84 34.64 46.98
Rate of change  13.94% 41.89% 1.05% 18.45% 23.13%
Salamanca
2016 26.10 16.90 25.90 31.60 40.80
2017 23.30 14.40 32.20 35.20 44.50
2018 24.80 13.70 35.50 33.50 47.70
2019 27.80 15.20 33.60 35.60 49.50
2020 26.40 17.50 37.90 37.00 51.40
Average  25.68 15.54 33.02 34.58 46.78
Rate of change  1.15% 3.55% 46.33% 17.09% 25.98%

María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-Galindo; Helena Martín-Rodero;
Claudio Ruff-Escobar; María-Purificación Galindo-Villardón
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University Year Teaching Research Citations Industry Internationa-
lization
Santiago de Compostela
2016 22.90 14.10 46.90 32.30 37.10
2017 19.80 14.90 40.90 35.70 42.40
2018 20.90 15.00 48.20 35.20 44.20
2019 26.80 16.00 50.50 39.00 44.30
2020 21.80 16.60 46.90 40.30 44.70
Average  22.44 15.32 46.68 36.50 42.54
Rate of change  −4.80% 17.73% 0.00% 24.77% 20.49%
Sevilla
2016 21.50 14.90 32.60 36.70 32.00
2017 19.50 13.90 33.10 37.90 34.40
2018 20.90 15.40 35.70 42.80 34.70
2019 27.00 18.80 38.70 36.60 38.40
2020 25.40 18.70 36.50 36.60 38.20
Average  22.86 16.34 35.32 38.12 35.54
Rate of change  18.14% 25.50% 11.96% −0.27% 19.38%
València
2016 22.70 16.90 49.60 31.30 40.50
2017 20.90 18.40 50.50 34.40 41.70
2018 21.90 18.40 56.20 34.40 42.20
2019 28.00 19.60 68.00 36.30 44.90
2020 24.60 20.80 70.80 37.00 47.00
Average  23.62 18.82 59.02 34.68 43.26
Rate of change  8.37% 23.08% 42.74% 18.21% 16.05%
Vigo
2016 18.40 10.50 31.80 38.10 30.70
2017 15.50 11.70 33.20 37.00 36.50
2018 19.40 12.20 32.20 35.70 40.30
2019 26.00 14.80 35.30 39.00 41.70
2020 17.70 14.60 39.50 38.40 41.60
Average  19.40 12.76 34.40 37.64 38.16
Rate of change  −3.80% 39.05% 24.21% 0.79% 35.50%
Zaragoza
2016 20.10 12.70 49.50 36.70 33.50
2017 20.30 12.50 49.70 38.60 35.10
2018 20.50 12.30 50.80 37.10 37.60
2019 27.90 12.40 47.70 38.10 37.00
2020 22.00 13.40 43.80 38.60 39.40
Average  22.16 12.66 48.30 37.82 36.52
Rate of change  9.45% 5.51% −11.52% 5.18% 17.61%
Table 5 shows that the Universitat Autònoma de Bar-
celona achieved the highest averages on teaching
(41.56) and research (37.02). Likewise, the Universi-
tat Pompeu Fabra obtained the highest averages on
citaons (94.20) and internaonalizaon (64.30). In
the variable related to industry, the Universidad de
Navarra achieved the highest average (67.02) and
rate of change (34.65%). The highest percentage ra-
tes of change for the remaining variables were for
the Universidad del País Vasco on teaching (20.88%),
Universitat Pompeu Fabra on research (43.21%),
Universidad de La Coruña on citaons (95.78%), and
Universitat Politècnica de València on internaonali-
zaon (51.98%).
The informaon captured in the HJ-biplot for the
rst two axes is presented in Table 6. Two axes were
Table 6. Explained variance, THE ranking
Axis Eigenvalue Explained
variance
Cumulative
variance
Axis 1 8.98 67.21 67.21
Axis 2 4.67 18.20 85.41
Table 7. Contribution of each factor axis to the variability of the indicators in
the THE ranking
Variable Axis 1 Axis 2
Teaching 764 117
Research 860 78
Citations 787 12
Industry 293 623
Internationalization 656 80

Multivariate dynamics of Spanish universities in international rankings
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 13
retained as a high cumulave inera
was achieved (85.41%), sucient to
characterize with some certainty the
posioning of the universies in the
THE ranking with respect to all the
variables considered.
The rst factor axis contained the
greatest amount of informaon.
Therefore, the horizontal gradient is
the most interesng to explain the
ranking of the universies according
to this mulvariate latent gradient.
Table 7 presents the contribuon of
each factor axis to the variability of
the dierent indicators in this ran-
king.
Considering the contribuons of
each factor to the entries in each
column, all the variables could be
interpreted in the factorial plane
1-2 and showed a good-quality re-
presentaon. Research, citaons,
teaching, and internaonalizaon
made a high contribuon to axis 1.
For industry, the variable related to
knowledge transfer, axis 2 contribu-
ted the most informaon of interest.
Figure 3 shows the HJ-biplot for the
2020 data matrix. A direct and strong
correlaon was observed between
the teaching and research variables,
both of which contribute 30% to the
classicaon. There was also a direct
covariaon between both of these
variables and citaons and interna-
onalizaon. Therefore, four of the
ve indicators in the ranking, with
a total weighng of 97.5%, correla-
ted directly in the biplot. Industry
also showed a direct interrelaon
with the rest of the indicators, ex-
cept educaon, with which it did not
show any connecon. However, no
indirect correlaons appeared be-
tween any of the ranking variables.
Regarding the rows, 8 of the 25 uni-
versies analyzed were not well re-
presented. Universies were posio-
ned in dierent parts of the graph,
and various groups were established
based on the similarity between
their characteriscs.
The Universidades Pompeu Fabra
and Autònoma de Barcelona were
characterized by citaons. Barcelo-
na, Autónoma de Madrid, and Complutense de Madrid stood out in terms of the teaching variable, while the Universidad
de Navarra obtained a high value on the industry variable. The other instuons are grouped in the le part of Figure 3,
not showing good posions on any indicator of this ranking.
Figure 3. HJ-biplot factorial representation for the THE ranking (2020), planes 1-2.
Figure 4. Dynamic biplot factorial representation of the THE ranking, plane 1-2.

María-Teresa Gómez-Marcos; Marcelo Ruiz-Toledo; María-Purificación Vicente-Galindo; Helena Martín-Rodero;
Claudio Ruff-Escobar; María-Purificación Galindo-Villardón
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Comparison with Table 5 reveals that the highest means
on citaons corresponded to the Pompeu Fabra (94.20)
and Autònoma de Barcelona universies (89.06), and
although the rates of change were posive, they were
not very high (4.08%, 10.86%, and 10.86%, respecvely).
The teaching averages of the universies of Barcelona
(35.92), Complutense de Madrid (35.38), and Autónoma
de Madrid (34.98) were high, but the highest value on
this indicator corresponded to the Universitat Autòno-
ma de Barcelona (41.56). Regarding the rates of change, the Universitat de Barcelona was the only university with a
negave value (-3.12%). Finally, the Universidad de Navarra exhibited the highest average (67.02) and greatest increase
(34.65%) on the industry variable.
Figure 4 shows the dynamic analysis that enables a projecon of the situaons of the universies in each year, illustra-
ng their trajectories.
The Universitat Pompeu Fabra, ranked rst in the THE ranking, was characterized in 2016 by research, while in the fo-
llowing year it approached citaons, only to stand out again in 2018 in research, and end again in 2020 with a high value
on citaons. The Universitat Autònoma de Barcelona, ranked second, also showed an upward trajectory that caused a
change in its posion from teaching to research, to end up characterized by citaons in 2020. The trajectories of the next
most highly classied universies, Barcelona and Autónoma de Madrid, approached teaching, which was also approa-
ched by the Universidad Complutense de Madrid. The Universidad de Navarra, aer a decline in 2017 that brought it
closer to internaonalizaon, showed a growing trend towards industry with a very strong increase in the nal year and
a very distant posion. The rest of the universies, albeit with changes in their trajectories, connued with more distant
posions with respect to all the indicators.
4. Conclusions and discussion
This research demonstrates the praccal ulity of the dynamic biplot technique (Egido-Miguélez, 2015) to study the
internaonalizaon of Spanish universies through rankings, as well as to illustrate their trajectories. The HJ-biplot te-
chnique (Galindo-Villardón, 1986) facilitated a graphical representaon of which universies and indicators could be
superimposed in the same reference system with the highest quality of representaon.
The present work examined the Spanish universies classied in the ARWU and THE rankings over the last ve years. A
very high accumulated inera was observed for both lists, which allowed an intuive interpretaon of the graphs.
Dierent covariaons were observed between the variables of the two rankings. In the ARWU ranking, the strongest
direct correlaon was found between two indicators weighing 40% each: highly cited researchers and arcles indexed in
SCIE and SSCI. This laer variable also correlated directly with published arcles in Nature and Science, although more
weakly. In contrast, highly cited researchers was indirectly interrelated with organizaon size and showed lile covaria-
on with arcles in Nature and Science or alumni with Nobel Prizes or Fields Medals.
However, the indicators in the THE ranking appeared to be more linked, and none of them correlated indirectly, with
only knowledge transfer not showing any connecon with teaching. Furthermore, the three dimensions with the largest
weighngs (teaching, research, and citaons) were strongly and directly correlated in the biplot. Likewise, these indica-
tors showed a direct interrelaon with internaonalizaon, and therefore four of the ve THE variables were correlated,
together accounng for a weighng of 97.5% in this ranking. In line with these conclusions, Safón (2019) considered that
internaonal lists include reputaon biases produced by surveys that aect not only teaching but also research perfor-
mance. On the one hand, the editors of the most presgious journals may be inclined to accept more arcles from the
most prominent universies. On the other hand, authors also tend to aribute a higher quality to works published from
these instuons, increasing their citaons. This ulmately means that research and reputaon feed into each other,
and the posion in the rankings derives not only from the current results of the university but also from past reputaon,
which in turn improves current research (Safón; Docampo, 2020).
Twice as many Spanish instuons were classied in the THE ranking for ve consecuve years compared with the
ARWU ranking. In the ARWU ranking, no university ma-
naged to score in the category of academics who won a
Nobel Prize or Fields Medals, a variable with a weight of
20% in the classicaon. The ARWU ranking exhibits a
highly invesgave component and measures outstan-
ding individual performance through awards or highly
cited researchers. Spanish instuons have limited pro-
ducon of this type (Casani; Rodríguez-Pomeda, 2017),
thus hindering their posioning in this ranking.
Twice as many Spanish institutions were
classified in the THE ranking for five con-
secutive years compared with the ARWU
ranking. In the ARWU ranking, no uni-
versity managed to score in the category
of academics who won a Nobel Price or
Fields Medals
Rankings are not the only manifestation
of internationalization, but competing
in them brings with it prestige that is
always beneficial for the organization
as well as the reputation of the Spanish
university system

Multivariate dynamics of Spanish universities in international rankings
e300210 Profesional de la información, 2021, v. 30, n. 2. e-ISSN: 1699-2407 15
All the universies that managed to be classied in the
ARWU ranking also did so in the THE ranking and are
thus considered as centers with high transnaonal visi-
bility. This visibility occurred through dierent variables.
The category of highly cited researchers included the
Universidades de Barcelona, Granada, València, and Po-
litècnica de València. Only one Spanish university, the
Universidad Complutense de Madrid, managed to score in the alumni Nobel Prize or Fields Medal winners category.
Regarding knowledge transfer, the Universidad de Navarra stood out. No Spanish university was classied in the sole
indicator that directly measures internaonalizaon. The dimension valuing educaon, measured largely based on re-
putaon surveys, included the Universidades de Barcelona, Autónoma de Madrid, and Complutense de Madrid. In this
study, however, centers such as the Autónoma de Barcelona, Pompeu Fabra, and Barcelona stood out. The remaining 12
enes classied in the internaonal lists did not obtain high values on any indicator and showed quite similar posions
in the biplots.
It can thus be concluded that Spanish universies show a low level of internaonalizaon, with only a small percentage
having sucient capacity to compete in global rankings. Only 29% of organizaons appear connuously in one of the
two most prominent and inuenal internaonal rankings. Most of the universies have a weak brand with respect to
the global context (Carrillo; Ruão, 2005), and only nine show high values on any of the indicators when considered in a
mulvariate fashion (Autònoma de Barcelona, Autónoma de Madrid, Barcelona, Complutense de Madrid, Granada, Na-
varra, Politècnica de València, Pompeu Fabra, and València). As more universies are added to these classicaons each
year, it will become necessary to analyze their trajectory over me to determine whether the presge and reputaon of
the Spanish university system improve.
Although the concept of internaonalizaon of higher educaon presents many nuances, and global rankings are not its
only manifestaon, one must not forget that they provide opportunies for greater transnaonal visibility (Collins; Park,
2016). All the research universies in the world follow them, worry about their orientaon, and even adapt themselves
in the face of methodological changes and transformaons (Pérez-Esparrells, 2017). Compeng in them brings with it
presge that is always benecial for the organizaon as well as the reputaon of the Spanish university system.
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