Trend Models on the Academic Ranking of World Universities

Farrah Pei-Chen Chang ； Liang-Yuh Ouyang

《International Journal of Information and Management Sciences》 29卷1期 (2018/03) Pp. 35-56

https://doi.org/10.6186/IJIMS.2018.29.1.2

摘要

The Academic Ranking of World Universities (ARWU) has provided annual global rankings of universities since 2003, making it the earliest of its kind. ARWU draws on six indicators to measure the academic performance of universities. Top 500 universities are ranked each year since 2004 by linear combinations of the six indicators. This paper uses a natural log regression model, called the Score-Rank Model, to present the relationship between the score variable and the rank variable for each year from 2004 to 2016. This paper also presents the Trend Model, built by a two-stage process; first, a linear regression model between two parameters (at and bt in year t) is established; and second, an ARIMA model is built to obtain the value of bt. The Trend Model can be used to forecast the overall score of a particular rank, or the rank of a specific overall score for future years. It is shown that the Trend Model is valid in an empirical study using ranking data from 2005 to 2015 to forecast the overall scores of the top 500 ranks in 2016. When comparing the forecast results with the real ranking outcomes of 2016 in a graph, it presents two very similar and almost overlapping curves.

關鍵字

Academic Ranking of World Universities ； natural log regression model ； ARIMA model ； trend model ； coefficient of determination

參考文獻

ARWU. (2003-2016). Academic Ranking of World Universities 2003 - Academic Ranking of World Universities 2016. Retrieved from http://www.shanghairanking.com/ARWU2003.html http://www.shanghairanking.com/ARWU2016.html

ARWU. (2016). Academic Ranking of World Universities 2016 - Methodology. Retrieved from http://www.shanghairanking.com/ARWU-Methodology-2016.html

Bauer, J., Leydesdorff, L. and Bornmann, L. (2015). Highly-cited papers in Library and Informa- tion Science (LIS) : Authors, Institutions, and Network Structures, Journal of the Association for Information Science and Technology, Vol.67, No.12, 3095-3100.

Dehon, C., McCathie, A. and Verardi, V. (2010). Uncovering excellence in academic rankings: a closer look at the Shanghai Ranking. Scientometrics, Vol.83, No.2, 515-524.

Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, London, UK.