世界大學學術排名之研究

A Study on the Academic Ranking of World Universities

張倍禎(Farrah Pei-Chen Chang)

指導教授 ： 歐陽良裕

淡江大學/商管學院/管理科學學系博士班/博士(2018年)

https://doi.org/10.6846/TKU.2018.00377

摘要

世界大學學術排名(ARWU)自2003年起每年提供全球大學排名，是全球最早的大學排名。ARWU使用六個指標衡量大學的學術表現。自2004年起，全球最佳500所大學即透過六個指標之線性組合予以排名。本文針對ARWU探討二個部分。第一部分為採用自然對數迴歸，建構得分－名次模型，呈現2004年至2016年每年的得分變數與名次變數之間的關係。接著，也提出一個經由兩階段所建立的趨勢模型，用來預測未來得分與名次間之關係：第一階段，建立包含兩個參數（在t年的at及bt）的線性迴歸模型；第二階段，建立一個ARIMA模型以預測bt值。趨勢模型可用於預測某一特定名次所對應的得分，或預測某一特定得分在未來年度的名次。經實證研究，使用2005年至2015年的排名數據資料透過趨勢模型預測2016年前500名大學的得分，結果顯示該趨勢模型是有效的。此外，將2016年所預測的排名結果與2016年實際排名結果進行比較時，亦呈現出兩條非常相近且幾乎重疊的曲線。本文第二部分為採用逐步迴歸分析，建立2004年至2016年排名之五個逐步迴歸模型，以簡化ARWU排名指標與計分公式。所建立之五個模型中，有三個模型具最佳配適度。經實證研究，這三個模型所產生之三項計分公式均適於取代原計分公式，且獲得相似之得分結果。

關鍵字

世界大學學術排名 ； 自然對數迴歸模型 ； ARIMA模型 ； 判定係數 ； 逐步迴歸模型 ； 平穩R平方

並列摘要

The Academic Ranking of World Universities (ARWU) has provided annual global rankings of universities since 2003, making it the earliest of its kind. The 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 presents two parts on the study of the ARWU. In the first part, we used 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. Then, we also presented the Trend Model, built by a two-stage process, to forecast the relationship of the variables in future years. In the first stage, a linear regression model between two parameters (at and bt in year t) was established; in the second stage, an ARIMA model was built to obtain the value of bt. The Trend Model can be used to forecast the total score of a particular rank, or the rank of a specific total 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 total 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. In the second part of the paper, in attempt to simplify the indicators of the ARWU, we used a stepwise regression analysis for each ranking year and constructed five Stepwise Regression Models from 2004 to 2016. Among the five models, we found three models that had better model fitting. Furthermore, the new scoring formulas generated from the three Modified Stepwise Regression Models are all adequate to replace the original scoring formula. As shown in our empirical study, the three modified scoring formulas all produced very similar results when compared with the original outcomes.

並列關鍵字

Academic Ranking of World Universities ； natural log regression model ； ARIMA model ； coefficient of determination ； stepwise regression model ； stationary R-squared

參考文獻

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

The ARWU. (2004-2016). Academic Ranking of World Universities 2004 – Academic Ranking of World Universities 2016. Retrieved from http://www.shanghairanking.com/ARWU2004.html -http://www.shanghairanking.com/ARWU2016.html