臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：世界大學學術排名之研究
作者：張倍禎 引用關係
作者(外文)：Farrah Pei-Chen Chang
校院名稱：淡江大學
系所名稱：管理科學學系博士班
指導教授：歐陽良裕
學位類別：博士
出版日期：2018
主題關鍵詞：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；Academic Ranking of World Universities；natural log regression model；ARIMA model；coefficient of determination；stepwise regression model；stationary R-squared
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:5
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。
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.
Abramo, G., & D’Angelo, C. A. (2014). How do you define and measure research productivity? Scientometrics, 101, 1129-1144.

Altbach, P. G. (2004). Globalisation and the university: Myths and realities in an unequal world. Tertiary Education and Management, 10(1), 3-25.

Altbach, P. G. (2008). The complex roles of universities in the period of globalization. In Global University Network for Innovation (GUNI) (Ed.), Higher Education in the World 3: Higher Education: New Challenges and Emerging Roles for Human and Social Development (3-14). UK: Palgrave MacMillan.

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

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

Bauer, J., Leydesdorff, L., & Bornmann, L. (2015). Highly-cited papers in Library and Information Science (LIS): Authors, Institutions, and Network Structures. Journal of the Association for Information Science and Technology, 67(12), 3095-3100.

Billaut, J. C., Bouyssou, D., & Vincke, P. (2010). Should you believe in the Shanghai ranking? Scientometrics, 84(1), 237-263.

Cantwell, B., & Maldonado-Maldonado, A. (2009). Four stories: Confronting contemporary ideas about globalization and internationalization in high education. Globalisation, Societies and Education, 7(3), 289-306.

Claassen, C. (2015). Measuring university quality. Scientometrics, 104(3), 793-807.

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

Docampo, D. (2013). Reproducibility of the Shanghai academic ranking of world universities. Scientometrics, 94(2), 567-587.

Docampo, D., & Cram, L. (2015). On the effects of institutional size in university classifications: The case of the Shanghai ranking. Scientometrics, 102(2), 1325-1346.

Docampo, D., Egret, D., & Cram, L. (2015). The effect of university mergers on the Shanghai ranking. Scientometrics, 104(1), 175-191.
 
Florian, R. (2007). Irreproducibility of the results of the Shanghai academic ranking of world universities. Scientometrics, 72(1), 25-32.

Goetzmann, M. (2014). Shanghai Academic Ranking: A French Controversy, 2013-8-29, La Jeune Politique. Retrieved from http://lajeunepolitique.com

Huang, M. (2011). A comparison of three major academic rankings for world universities: From a research evaluation perspective. Journal of Library and Information Studies, 9(1), 1-25.

Jeremic, V., Bulajic, M., Martic, M., & Radojicic, Z. (2011). A fresh approach to evaluating the academic ranking of world universities. Scientometrics, 87, 587-596.

Jovanovic, M., Jeremic, V., Savic, G., Bulajic, M., & Martic, M. (2012). How does the normalization of data affect the ARWU ranking? Scientometrics, 93, 319-327.

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

Liu, N. C. (2009). The story of academic ranking of world universities. International Higher Education, 54(Winter), 2-3.

Luque-Martínez, T., & Del Barrio-García, S. (2016). Constructing a synthetic indicator of research activity. Scientometrics, 108(3), 1049-1064.

Marginson, S. (2005). There must be some way out of here. Tertiary Education Management Conference. Perth: Keynote address.

Marginson, S. (2017). Do rankings drive better performance? International Higher Education, 89(Spring), 6-8.

Piro, F. N., & Sivertsen, G. (2016). How can differences in international university rankings be explained? Scientometrics, 109(3), 2263-2278.

QS Top Universities. (2016). Can financial investment buy university success? Retrieved from https://www.topuniversities.com/university-rankings-articles/world-university-rankings/can-financial-investment-buy-university-success

QS Top Universities. (2017). QS World University Rankings – methodology. Retrieved from https://www.topuniversities.com/qs-world-university-rankings/methodology

Romanov, D., Drozdov, A., & Gerashchenko, A. (2017). Citation-based criteria of the significance of the research activity of scientific teams. Scientometrics, 112(3), 1179-1202.

Sadlak, J. (2009). Ranking in higher education: Its place and impact. The Europa World of Learning 2010 (60th ed.). London: Taylor & Francis Ltd.

Salmi, J. (2009). The challenge of establishing world-class universities. In J. Sadlack, & N. C. Liu (Eds.), The World-Class University as Part of a New Higher Education Paradigm: From Institutional Qualities to Systemic Excellence (23-68). Romania: UNESCO-CEPES, Cluj University Press.

Times Higher Education. (2016). World University Rankings 2016-17 Methodology. Retrieved from https://www.timeshighereducation.com/world-university-rankings/methodology-world–university-rankings-2016-2017

Torrisi, B. (2014). A multidimensional approach to academic productivity. Scientometrics, 99(3), 755-783.

Van Raan, A. (2005a). Fatal attraction: Conceptual and methodological problems in the ranking of universities by bibliometric methods. Scientometrics, 62(1), 133-143.

Van Raan, A. (2005b). Challenges in ranking of Universities. Invited paper for the 1st International Conference on World-Class Universities. Shanghai: Shanghai Jaio Tong University, June 16-18.

Yonezawa, A. (2015). Will the ranking game continue after a decade? International Higher Education, 80, Spring, 19-20.

Zitt, M., & Filliatreau, G. (2007). Big is (made) beautiful: Some comments about the Shanghai ranking of world-class universities. In J. Sadlack, & N. C. Liu (Eds.), The World Class University and Ranking: Aiming beyond Status (141-160). Romania: UNESCO-CEPES, Cluj University Press.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：939（臺灣：932，外國：7） ／ 本年度總使用人次：2042760 ／ 訪客人次（自102年09月）：67861904
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上