Skip to main content
Log in

On the internal dynamics of the Shanghai ranking

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The Academic Ranking of World Universities (ARWU) published by researchers at Shanghai Jiao Tong University has become a major source of information for university administrators, country officials, students and the public at large. Recent discoveries regarding its internal dynamics allow the inversion of published ARWU indicator scores to reconstruct raw scores for 500 world class universities. This paper explores raw scores in the ARWU and in other contests to contrast the dynamics of rank-driven and score-driven tables, and to explain why the ARWU ranking is a score-driven procedure. We show that the ARWU indicators constitute sub-scales of a single factor accounting for research performance, and provide an account of the system of gains and non-linearities used by ARWU. The paper discusses the non-linearities selected by ARWU, concluding that they are designed to represent the regressive character of indicators measuring research performance. We propose that the utility and usability of the ARWU could be greatly improved by replacing the unwanted dynamical effects of the annual re-scaling based on raw scores of the best performers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Aghion, P., Dewatripont, M., Hoxby, C., Mas-Colell, A., & Sapir, A. (2008). Higher aspirations: An agenda for reforming european universities: Bruegel blueprint V, downloaded from aei.pitt.edu on March 2nd 2013. http://aei.pitt.edu/id/eprint/8714.

  • Aghion, P., Dewatripont, M., Hoxby, C., Mas-Colell, A., & Sapir, A. (2010). The governance and performance of universities: Evidence from Europe and the US. Economic Policy, pp 7–59.

  • Arrow, K. (1963). Social choice and individual values. New York: Wiley.

    Google Scholar 

  • Bartlett, M. S. (1954). A note on the multiplying factors for various Chi-square approximations. Journal of the Royal Statistical Society, 16(B), 296–98.

    MATH  MathSciNet  Google Scholar 

  • Billaut, J. C., Bouyssou, D., & Vincke, P. (2010). Should you believe in the Shangai ranking: An MCDM view. Scientometrics, 84(1), 237–263.

    Article  Google Scholar 

  • Butler, D. (2007). Academics strike back at spurious rankings: Universities seek reform of ratings. News; Nature 447, 514–515.

    Article  Google Scholar 

  • Butler, D. (2010). University rankings smarten up. Nature Special Report; Nature 464, pp 16–17.

    Google Scholar 

  • Cliff, N., & Hamburger, C. (1967). The study of sampling errors in factor analysis by means of artificial experiments. Psychological Bulletin, 68, 430–435.

    Article  Google Scholar 

  • Comray, A. L., & Lee, H. (1992). A First Course in Factor Analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Dasgupta, P., & Weale, M. (1992). On measuring the quality of life. World Development, 20(1), 119–131.

    Article  Google Scholar 

  • Dehon, C., McCathie, A., & Verardi, V. (2010). Uncovering excellence in academic rankings: a closer look at the Shanghai ranking. Scientometrics, 83(2), 515–524.

    Article  Google Scholar 

  • Docampo, D. (2011). On using the Shanghai ranking to assess the research performance of university systems. Scientometrics, 86(1), 77–92.

    Article  Google Scholar 

  • Docampo, D. (2013). Reproducibility of the Shanghai academic ranking of world universities results. Scientometrics, 94(2), 515–524.

    Article  Google Scholar 

  • Geese, R. (2004). Is a revision of the international scoring tables overdue?. New Studies in Athletics, 19(3), 9–19.

    Google Scholar 

  • Grammaticos, B. (2007). The physical basis of scoring athletic performance. New Studies in Athletics, 22(3), 47–53.

    MathSciNet  Google Scholar 

  • Guadagnoli, E., & Vellicer, W. (1988). Relation of sample size to the stability of component patterns. Psychological Bulletin, 103, 265–275.

    Article  Google Scholar 

  • Hammond, T. (2007). Rank injustice?: How the scoring method for cross-country running competitions violates major social principles. Public Choice, 133, 359–375.

    Article  Google Scholar 

  • Harder, D. (2001). Apples to oranges. Princeton: Education Plus.

    Google Scholar 

  • Hasktian, A., Rogers, W., & Catell, R. B. (1982). The behavior of numbers factors rules with simulated data. Multivariate Behavioural Research, 17, 193–219.

    Article  Google Scholar 

  • Hazelkorn, E. (2008). Learning to live with league tables and ranking: The experience of institutional leaders. Higher Education Policy, 21, 193–215.

    Article  Google Scholar 

  • IAAF. (2004). International association of athletics federations scoring tables for combined events: Downloaded from the I A A F server on march 14th 2013. URL:http://www.iaaf.org.

  • Ioannidis, J. P. A., Nikolaos, A., Patsopoulos, N., Kavvoura, F., Tatsioni, A., Evangelou, E., & Kouri, A., Contopoulos-Ioannidis, D. G., & Liberopoulos, G. (2007). International ranking systems for universities and institutions: A critical appraisal. BMC Medicine, 5: 30.

    Article  Google Scholar 

  • Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39, 31–36.

    Article  MATH  Google Scholar 

  • Liu, N. C., & Cheng, Y. (2005). Academic ranking of world universities: Methodologies and problems. Higher Education in Europe, 30(2), 127–136.

    Article  Google Scholar 

  • London. (2012). Official results in 100 m (men): Downloaded from the Official London 2012 website server on March 22st 2013. URL http://www.london2012.com/athletics/event/men-100m/index.html.

  • Marginson, M., & van der Wende, M. (2007). To rank or to be ranked: The impact of global rankings in higher education. Journal of Studies in International Education, 11(3/4), 306–329.

    Article  Google Scholar 

  • Morrison, D. (2000). Multivariate Statistical Methods (3rd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Myerson, R., & Weber, R. (1993). A theory of voting equilibria. American Political Science Review, 87, 102–114.

    Article  Google Scholar 

  • Saari, D. (2001). Decisions and elections: explaining the unexpected. New York: Cambridge University Press.

    Book  Google Scholar 

  • Sass, D. (2010). Factor loading estimation error and stability using exploratory factor analysis. Educational and Psychological Measurement, 70:4, 557–577.

    Article  Google Scholar 

  • Sawyer, K., Sankey, H., & Lombardo, R. (2013). Measurability invariance, continuity and a portfolio representation. Measurement, 46, 89–96.

    Article  Google Scholar 

  • Stevens, J. (1996). Applied Multivariate Statistics for the Social Sciences. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). Boston: Pearson Education, Inc. / Allyn and Bacon.

    Google Scholar 

  • Thomson-Reuters. (2012). Methodology for identifying highly-cited researchers: Downloaded from the Thomson Reuters highlycited.com server on March 20th 2013. URL http://www.highlycited.com/methodology/.

  • Trkal, V. (2003). The development of combined event scoring tables and implications for the training of athletes. New Studies in Athletics, 18(4), 7–12.

    Google Scholar 

  • Van Damme, R., Wilson, R., Vanhooydonk, B., & Aerts, P. (2002). Performance constraints in decathletes. Nature, 415, 755–756.

    Article  Google Scholar 

  • Waltman, L., Calero-Medina, C., Kosten, J., Noyons, E. C. M., Tijssen, R. J. W., van Eck, N. J., van Leeuwen, T. N., van Raan, A. F. J., Visser, M. S., & Wouters, P. (2012). The Leiden ranking 2011/2012: Data collection, indicators, and interpretation. Journal of the American Society for Information Science and Technology, 63(12), 2419–2432.

    Article  Google Scholar 

  • WBI. (2012). Knowledge Assessment Methodology: Downloaded from the World Bank server on March 7th 2013. URL http://www.worldbank.org/kam.

  • Westera, W. (2006). Decathlon; Towards a balanced and sustainable performance assessment method. New Studies in Athletics, 211, 39–51.

    Google Scholar 

  • Zientek, L., & Thompson, B. (2007). Applying the bootstrap to the multivariate case: Bootstrap component/factor analysis. Behavior Research Methods, 39(2), 318–325.

    Article  Google Scholar 

  • Zitt, M., & Filliatreau, G. (2007). The world class universities and ranking: Aiming beyond status, Romania: UNESCO-CEPES, Cluj University Press, chap Big is (made) beautiful: Some comments about the Shangai ranking of world-class universities, Part Two, 141–160.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Domingo Docampo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Docampo, D., Cram, L. On the internal dynamics of the Shanghai ranking. Scientometrics 98, 1347–1366 (2014). https://doi.org/10.1007/s11192-013-1143-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-013-1143-0

Keywords

Navigation