What drives university research performance? An analysis using the CWTS Leiden Ranking data

Frenken, Koen; Heimeriks, Gaston; Hoekman, Jarno

doi:10.1016/j.joi.2017.06.006

Cited by 77 publications

(55 citation statements)

References 19 publications

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“…By acknowledging the inevitability of preventing university managers from using rankings, these studies focus their efforts on analyzing underlying factors behind the positioning of universities. In this case, studies tend to either be ranking-specific (e.g., Bornmann et al 2013;Frenken et al 2017;Mutz & Daniel 2015) or compare a set of rankings to identify discrepancies and common elements between them (Moed 2017a;Safón 2013). These studies have found that country differences explain in many cases the scores achieved by universities, even beyond university differences (Bornmann et al 2013), with rankings being geographically biased towards different regions (Moed 2017a).…”

Section: Methodological Aspectsmentioning

confidence: 99%

Mining university rankings: Publication output and citation impact as their basis

Robinson-García

Torres‐Salinas

Herrera‐Viedma

et al. 2019

Research Evaluation

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World University rankings have become well-established tools that students, university managers and policy makers read and use. Each ranking claims to have a unique methodology capable of measuring the 'quality' of universities. The purpose of this paper is to analyze to which extent these different rankings measure the same phenomenon and what it is that they are measuring. For this, we selected a total of seven world-university rankings and performed a principal component analysis. After ensuring that despite their methodological differences, they all come together to a single component, we hypothesized that bibliometric indicators could explain what is being measured. Our analyses show that ranking scores from whichever of the seven league tables under study can be explained by the number of publications and citations received by the institution. We conclude by discussing policy implications and opportunities on how a nuanced and responsible use of rankings can help decision making at the institutional level

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Section: Methodological Aspectsmentioning

confidence: 99%

Mining university rankings: Publication output and citation impact as their basis

Robinson-García

Torres‐Salinas

Herrera‐Viedma

et al. 2019

Research Evaluation

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show abstract

“…World rankings include, among others, Leiden Ranking, Nature Index, and Reuters Ranking publishing annual lists using methodologies and indicators that assess research quality. Leiden Ranking (Leiden University, Netherlands) ranks 1000 HEIs on annual science papers indexed in Web of Science [26] [27]. Nature Index (Springer-Nature Publishers) ranks 100 HEIs on annual science papers indexed in Science Citation Index [28].…”

Section: Ranking Systemsmentioning

confidence: 99%

Critical Treatise on University Ranking Systems

Sayed¹

2019

JSS

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Ranking systems use different methodologies, indicators, and data sources primarily focused on research quality and their results are important for stakeholders to compare universities on global level. Ranking also improves university global image, recruitment, and funding. Despite their importance rankings have flaws and pitfalls that cause controversy and concern. Detailed study of ranking literature indicated that research data shared by universities is a matter of concern in relation to credibility and authenticity. It also indicated that subjective ranking tools can produce erroneous results making them less useful for stakeholders. Some methodologies are also inherent flaws that produce different ranks for the same institution in different rankings. Due to lack of clear differences between universities it is recommended that stakeholders should consider detailed score tables rather than general ranking lists. Stakeholders should also acknowledge university disciplinary focus and education aspects that are not covered by rankings. This treatise is designed to give insight into rankings for academics and stakeholders with the aim of elucidating ranking importance, methodology, and indicators. It also gives comprehensive perspective of influential rankings and general analysis of ranking controversy, concerns, flaws, and pitfalls.

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“…Mathematical Problems in Engineering of publications, cited-publications, and international and industry-university copublications [53]. Furthermore, the goals, results, and achievements of these researches are available to the academic community (Publications, 5-3 ) [54].…”

Section: Evaluation Criteriamentioning

confidence: 99%

Evaluating University Reputation Based on Integral Linear Programming with Grey Possibility

Chen

Esangbedo

2018

Mathematical Problems in Engineering

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As the Chinese economy becomes stronger and stronger, there is increasing interest by some countries to adopt the Chinese approach to grow. The outstanding Chinese growth can be partly attributed to the education quality that is obtainable from the universities in China. The need to evaluate the reputation of Chinese universities is essential. In this paper, a hierarchical model for evaluating universities is proposed. Also, the Integral Linear Programming with Grey Possibility (ILP-GP) method is presented, which does not require the assumption of 0.5 for the grey distinguishing coefficient (ζ=0.5) in Grey Relational Analysis (GRA). In this paper, a numerical example for evaluating the reputation of five universities based on the sample data from 1,565 students in Shaanxi Province, China, is presented, and the most reputable university in this research is consistent with rankings in the literature.

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What drives university research performance? An analysis using the CWTS Leiden Ranking data

Cited by 77 publications

References 19 publications

Mining university rankings: Publication output and citation impact as their basis

Mining university rankings: Publication output and citation impact as their basis

Critical Treatise on University Ranking Systems

Evaluating University Reputation Based on Integral Linear Programming with Grey Possibility

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