English  |  正體中文  |  简体中文  |  Items with full text/Total items : 57615/91160 (63%)
Visitors : 13536553      Online Users : 342
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/114871

    Title: Trend Models on the Academic Ranking of World Universities
    Authors: Chang, F. P. C.;Ouyang, L. Y.
    Keywords: Academic Ranking of World Universities;natural log regression model;ARIMA model;trend model;coefficient of determination
    Date: 2018-03
    Issue Date: 2018-08-10 12:10:35 (UTC+8)
    Abstract: 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.
    Relation: International Journal of Information and Management Sciences 29(1), p.35-56.
    DOI: 10.6186/IJIMS.2018.29.1.2
    Appears in Collections:[Department of Management Sciences] Journal Article

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback