IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/115610.html
   My bibliography  Save this paper

Size, Internationalization and University Rankings: Evaluating and Predicting Times Higher Education (THE) Data for Japan

Author

Listed:
  • McAleer, M.J.
  • Nakamura, T.
  • Watkins, C.

Abstract

International and domestic rankings of academics, academic departments, faculties, schools and colleges, institutions of higher learning, states, regions and countries, are of academic and practical interest and importance to students, parents, academics, and private and public institutions. International and domestic rankings are typically based on arbitrary methodologies and criteria. Evaluating how the rankings might be sensitive to different factors, as well as forecasting how they might change over time, requires a statistical analysis of the factors that affect the rankings. Accurate data on rankings and the associated factors is essential for a valid statistical analysis. In this respect, the Times Higher Education (THE) World University Rankings is one of the three leading and most influential annual sources of international university rankings. Using recently released data for a single country, namely Japan, the paper evaluates the effects of size (specifically, the number of Full-Time Equivalent (FTE) students, or FTE(Size)) and internationalization (specifically, the percentage of international students, or IntStud) on academic rankings using THE data for 2017 and 2018 on 258 national, public (that is, prefectural or city), and private universities. The results show that both size and internationalization are statistically significant in explaining rankings for all universities, as well as separately for private and non-private (that is, national and public) universities, in Japan for each of 2017 and 2018

Suggested Citation

  • McAleer, M.J. & Nakamura, T. & Watkins, C., 2019. "Size, Internationalization and University Rankings: Evaluating and Predicting Times Higher Education (THE) Data for Japan," Econometric Institute Research Papers EI2019-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:115610
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/115610/EI2019-12.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Robert J. W. Tijssen & Alfredo Yegros-Yegros & Jos J. Winnink, 2016. "University–industry R&D linkage metrics: validity and applicability in world university rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 677-696, November.
    2. Nian Cai Liu & Ying Cheng & Li Liu, 2005. "Academic ranking of world universities using scientometrics - A comment to the “Fatal Attraction”," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(1), pages 101-109, July.
    3. Ibrahim Shehatta & Khalid Mahmood, 2016. "Correlation among top 100 universities in the major six global rankings: policy implications," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1231-1254, November.
    4. Henk F. Moed, 2017. "A critical comparative analysis of five world university rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 967-990, February.
    5. Osmo Kivinen & Juha Hedman & Kalle Artukka, 2017. "Scientific publishing and global university rankings. How well are top publishing universities recognized?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 679-695, July.
    6. Stacy Berg Dale & Alan B. Krueger, 2002. "Estimating the Payoff to Attending a More Selective College: An Application of Selection on Observables and Unobservables," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1491-1527.
    7. Fredrik Niclas Piro & Gunnar Sivertsen, 2016. "How can differences in international university rankings be explained?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2263-2278, December.
    8. Jill Johnes, 2018. "University rankings: What do they really show?," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 585-606, April.
    9. Jacek Pietrucha, 2018. "Country-specific determinants of world university rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1129-1139, March.
    10. Corrado Lo Storto, 2016. "Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method," Sustainability, MDPI, vol. 8(2), pages 1-29, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fernando García & Francisco Guijarro & Javier Oliver, 2021. "A Multicriteria Goal Programming Model for Ranking Universities," Mathematics, MDPI, vol. 9(5), pages 1-17, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. McAleer, M.J. & Nakamura, T. & Watkins, C., 2018. "Size, Internationalization and University Rankings: Evaluating Times Higher Education (THE) Data for Japan," Econometric Institute Research Papers EI2018-43, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Mónica Benito & Pilar Gil & Rosario Romera, 2019. "Funding, is it key for standing out in the university rankings?," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 771-792, November.
    3. Manuel Muñoz-Suárez & Natividad Guadalajara & José M. Osca, 2020. "A Comparative Analysis between Global University Rankings and Environmental Sustainability of Universities," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    4. Meng-Chen Zhang & Bo-Wei Zhu & Chao-Meng Huang & Gwo-Hshiung Tzeng, 2021. "Systematic Evaluation Model for Developing Sustainable World-Class Universities: An East Asian Perspective," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    5. Juan Antonio Dip, 2021. "What does U-multirank tell us about knowledge transfer and research?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3011-3039, April.
    6. Vicente Safón, 2019. "Inter-ranking reputational effects: an analysis of the Academic Ranking of World Universities (ARWU) and the Times Higher Education World University Rankings (THE) reputational relationship," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 897-915, November.
    7. Antonio Fernández-Cano & Elvira Curiel-Marin & Manuel Torralbo-Rodríguez & Mónica Vallejo-Ruiz, 2018. "Questioning the Shanghai Ranking methodology as a tool for the evaluation of universities: an integrative review," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 2069-2083, September.
    8. Annamaria Demarinis Loiotile & Francesco De Nicolò & Adriana Agrimi & Loredana Bellantuono & Marianna La Rocca & Alfonso Monaco & Ester Pantaleo & Sabina Tangaro & Nicola Amoroso & Roberto Bellotti, 2022. "Best Practices in Knowledge Transfer: Insights from Top Universities," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    9. Csató, László & Tóth, Csaba, 2020. "University rankings from the revealed preferences of the applicants," European Journal of Operational Research, Elsevier, vol. 286(1), pages 309-320.
    10. Benito, M. & Gil, P. & Romera, R., 2020. "Evaluating the influence of country characteristics on the Higher Education System Rankings’ progress," Journal of Informetrics, Elsevier, vol. 14(3).
    11. Lutz Bornmann & Werner Marx, 2020. "Thomas theorem in research evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 553-555, April.
    12. Csóka, Imola & Sebestyén, Géza & Neszveda, Gábor, 2019. "Tudományos teljesítmény mérése a magyar felsőoktatás gazdasági képzéseiben [Measuring scientific performance of business and economics faculties in Hungarian higher education]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 751-770.
    13. Ernesto Galbán-Rodríguez & Deborah Torres-Ponjuán & Ricardo Arencibia-Jorge, 2021. "Multidimensional quantitative analysis of the Cuban scientific output and its regional context," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2643-2665, March.
    14. Enis Siniksaran & M. Hakan Satman, 2020. "WURS: a simulation software for university rankings—software review," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 701-717, January.
    15. Yating Wen & Xiaodong Zhao & Xingguo Li & Yuqi Zang, 2023. "Explaining the Paradox of World University Rankings in China: Higher Education Sustainability Analysis with Sentiment Analysis and LDA Topic Modeling," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    16. Manjula Wijewickrema, 2021. "Authors’ perception on abstracting and indexing databases in different subject domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3063-3089, April.
    17. Frenken, Koen & Heimeriks, Gaston J. & Hoekman, Jarno, 2017. "What drives university research performance? An analysis using the CWTS Leiden Ranking data," Journal of Informetrics, Elsevier, vol. 11(3), pages 859-872.
    18. Olabode Gbobaniyi & Shalini Srivastava & Abiodun Kolawole Oyetunji & Chiemela Victor Amaechi & Salmia Binti Beddu & Bajpai Ankita, 2023. "The Mediating Effect of Perceived Institutional Support on Inclusive Leadership and Academic Loyalty in Higher Education," Sustainability, MDPI, vol. 15(17), pages 1-25, September.
    19. Nakabayashi, Masaki, 2011. "Schooling, employer learning, and internal labor market effect: Wage dynamics and human capital investment in the Japanese steel industry, 1930-1960s," MPRA Paper 30597, University Library of Munich, Germany.
    20. Cory Koedel, 2009. "Postsecondary Education Structure," Working Papers 0906, Department of Economics, University of Missouri, revised 04 Oct 2010.

    More about this item

    Keywords

    International and domestic rankings; Size; Internationalization; National; public; and private universities; Changes over time.;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • Y1 - Miscellaneous Categories - - Data: Tables and Charts

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ems:eureir:115610. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.