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The determinants of research performance in European universities: a large scale multilevel analysis

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Abstract

The paper examines the research performance of European universities in a disaggregated way, using a large array of indicators from Scopus publications, including indicators of volume (number of articles; number of citations) and indicators of quality (percentage of publications in top 10% and top 25% SNIP journals; percentage of citations from top 10% and top 25% journals). These indicators are considered dependent variables in a multi-level estimation framework, in which research performance in a scientific area depends on variables at the level of university and at the level of the external regional environment. The area examined is Medicine, for the 2007–2010 period. The paper exploits for the first time the integration of publication data with the census of European universities (ETER). A large number of hypotheses are tested and discussed.

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Notes

  1. Concerning the sources of the data to which GRBS refers, the 2011 release covers 24,936 source titles of types Journal, Conference Proceedings, and Book Series from Elsevier's Scopus database. Publication types included are articles, reviews, and conference papers. In GRBS, source titles (journals, conference proceedings and book series) are classified into discipline-specific tiered outlets based on their Source Normalized Impact per Paper (SNIP) values in each of the 15 Top level GRBS categories.

  2. The data covers 23 ASJC (All Science Journals Classification) top level disciplines and 251 ASJC sub-disciplines. In addition, the GRBS includes a higher level of broad categories that groups the 23 All Science Journal Classification (ASJC3) top level disciplines into the following 15 broad disciplinary areas: Agricultural & Biological Sciences; Biochemistry, Genetics and Molecular Biology; Chemistry; Computer Science; Earth and Planetary Sciences; Economics and Business Sciences; Engineering; Environmental Sciences; Health Professions & Nursing; Materials Sciences; Mathematics; Medicine; Multidisciplinary; Other Life and Health Sciences; Physics And Astronomy.

  3. For the PUB25F09p indicator, 3 universities present a value of the indicator equal to 1. This value of the indicator (as well as a value equal to 0) causes the transformation introduced by Eq. (1) to be undefined. To overcome this problem, we have estimated the random-intercept models in two ways: (i) by excluding the observations with value of the indicators exactly equal to 0 and exactly equal to 1, and (ii) by applying the adjustment on empirical logit transformation (Cox 1970; Gart and Zweifel 1967):

    $$L\_PUB25F09p_{ij} = \ln \left[ {{\raise0.7ex\hbox{${\left( {PUB25F09p_{ij} + \frac{1}{{2 \cdot \text{pubtF}09_{{\text{ij}}} }}} \right)}$} \!\mathord{\left/ {\vphantom {{\left( {PUB25F09p_{ij} + \frac{1}{{2 \cdot \text{pubtF}09_{{\text{ij}}} }}} \right)} {\left( {PUB25F09p_{ij} + \frac{1}{{2 \cdot \text{pubtF}09_{{\text{ij}}} }}} \right)}}}\right.\kern-0pt} \!\lower0.7ex\hbox{${\left( {PUB25F09p_{ij} + \frac{1}{{2 \cdot \text{pubtF}09_{{\text{ij}}} }}} \right)}$}}} \right]\quad i = 1, \ldots N;\quad j = 1, \ldots J$$

  4. To address this issue we referred to the Mundlak approach (Mundlak 1978) which consists in adding the cluster (group) means of individual variables as additional covariates in the models. The Wald test was used to verify whether the assumption of exogeneity holds for individual regressors (Baltagi 2001) and the inclusion of cluster means would concern those covariates where both the within (individual level covariate) and the between effects (the NUTS2-level mean) are significantly different from zero at the 5% level. We did not find this evidence in our modelled processes. This result leads us to believe that within and between effects of covariates do not significantly different (Rabe-Hesketh and Skrondal 2008).

  5. The presence of singleton cluster at NUTS-2 level which corresponds for 3 countries to the only university in the country (we observed this situation for Bulgaria, Estonia and Slovakia) in our data set conducted us to the decision of focusing the analysis on two-level. On the other hand, the availability of contextual variables disaggregated at this finer level (NUTS-2 level) lead us to prefer the exploration of the variability at this level rather than the country level. Lastly, from a computational perspective when a three-level model was specified by including covariates at institution, NUTS-2 and NUTS-3 levels, the estimation process did not provide a feasible solution.

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Acknowledgements

The authors thank Peter Haddawy and Saeel UL Hassan for their contribution to the construction of the GRBS dataset.

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Authors and Affiliations

  1. Universita degli Studi di Pisa and IRVAPP-FBK, Pisa and Trento, Italy

    Andrea Bonaccorsi

  2. Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF) - University of Tuscia, Viterbo, Italy

    Luca Secondi

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  1. Andrea Bonaccorsi
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  2. Luca Secondi
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Correspondence to Andrea Bonaccorsi.

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Bonaccorsi, A., Secondi, L. The determinants of research performance in European universities: a large scale multilevel analysis. Scientometrics 112, 1147–1178 (2017). https://doi.org/10.1007/s11192-017-2442-7

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