On the internal dynamics of the Shanghai ranking

Docampo, Domingo; Cram, L. E.

doi:10.1007/s11192-013-1143-0

Cited by 34 publications

(26 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Among the existing methods, the most important are the U-Multirank approach [1], the Times Higher Education (THE) [2], the Shanghai ranking [5], the Leiden ranking [6], the CHE ranking [7], and the Taiwanese college navigator method [8]. U-Multirank is an EU funded project that proposes a multidimensional ranking approach, considering the following dimensions of the academic endeavors: teaching -learning, research, knowledge transfer, international orientation and national engagement.…”

Section: A Multidimensional Ranking Of Heismentioning

confidence: 99%

Creating the Context for Exploiting Linked Open Data in Multidimensional Academic Ranking

Triperina

Sgouropoulou

Xydas

et al. 2015

Int. J. Recent Contrib. Eng. Sci. IT

View full text Add to dashboard Cite

Abstract-Academia is a complex socio-technical system with multiple aspects and constituents that involve various stakeholders. In order to address stakeholders' needs and to assist the institutional accountability, this complexity should be considered during the development of academic services. We have designed a dynamic multidimensional ranking approach, easily modifiable to address user requirements, so as to assess and compare the university performance with a clear view to the support of effective institutional strategic planning and policy making. Our approach comprises the following components: the AcademIS ontology to model the academic domain and its multiple dimensions, the AcademIS Information System to manage and display the academic information, published in Linked Open Data format and the visual-aided Multiple Criteria Decision Making component, to evaluate and rank the performance of the academic units. The data are aggregated from several sources, in different formats, LODified by our system, and presented to the user by the interface to ultimately assist the decision making process.

show abstract

Section: A Multidimensional Ranking Of Heismentioning

confidence: 99%

Creating the Context for Exploiting Linked Open Data in Multidimensional Academic Ranking

Triperina

Sgouropoulou

Xydas

et al. 2015

Int. J. Recent Contrib. Eng. Sci. IT

View full text Add to dashboard Cite

show abstract

“…The component analysis can therefore be performed on the covariance matrix, which is preferable for statistical reasons (Morrison 2000;Stevens 1996). Using SPSS, we begin with a principal components analysis of the reference data set along the lines of Dehon et al (2010), Docampo (2011) and Docampo and Cram (2014). The reference data presents the set of correlations exhibited in Table 4 with their corresponding p values.…”

Section: Compress the Scaled Raw Datamentioning

confidence: 99%

“…These include the mapping of the concept of good performance to indicators, forming a measure and its scoring for each indicator, the correspondence of performance and scores across the different indicators, and the balancing of the components in the aggregate (Grammaticos 2007). Docampo and Cram (2014) contend that ARWU addresses these issues through a repeatable and acceptable procedure for assigning scores to performance on the six indicators, and for combining them all into a single measure. Moreover the measurement validity of the ARWU raw data is guaranteed, since the Shanghai ranking results can now be accurately reproduced (see Docampo 2013).…”

Section: Introductionmentioning

confidence: 99%

“…It also arises in the HiCi and S&N indicators of ARWU, where one more Nature/Science publication or Highly Cited researcher makes little difference to the total score of a highly ranked university, while it may have great importance for those interested in improving the rank of a lower-ranked university. Our previous study of the internal dynamics of the Shanghai ranking (Docampo and Cram 2014) concluded that the Shanghai ranking is, and arguably should be, a regressive scoredriven table owing to the expansion of the values of the differences between the raw scores across highly-ranked universities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the effects of institutional size in university classifications: the case of the Shanghai ranking

Docampo

Cram

2014

Scientometrics

Self Cite

View full text Add to dashboard Cite

University rankings frequently struggle to delineate the separate contributions of institutional size and excellence. This presents a problem for public policy and university leadership, for example by blurring the pursuit of excellence with the quest for growth. This paper provides some insight into the size/excellence debate by exploring the explicit contribution of institutional size to the results of the Shanghai ranking indicators. Principal components analysis of data from the Shanghai ranking (2013 edition) is used to explore factors that contribute to the variation of the total score. The analysis includes the five non-derived ARWU indicators (Alumni, Award, HiCi, S&N and PUB) and uses the number of equivalent full-time academic staff (FTE) as a measure of size. Two significant but unequal factors are found, together explaining almost 85 % of the variance in the sample. A factor clearly associated with the size of the institution explains around 30 % of the variance. To sharpen the interpretation of the smaller factor as a measure of the effect of size, we extend the analysis to a larger set of institutions to eliminate size-dependent selection effects. We also show that eliminating outlying universities makes little difference to the factors. Our inferences are insensitive to the use of raw data, compared with the compressed and scaled indicators used by ARWU. We conclude that around 30 % of the variation in the ARWU indicators can be attributed to variation in size. Clearly, sizerelated factors cannot be overlooked when using the ranking results. Around 55 % of the variation arises from a component which is uncorrelated with size and which measures the quality of research conducted at the highest levels. The presence of this factor encourages further work to explore its nature and origins.

show abstract

“…It scores and ranks the world's leading research universities using numerical measures related to the research achievements of some associated individuals and to the amount of institutional research output. ARWU claims that its raw data are accessible, it releases annual revisions to scores and rankings, and it generates a hierarchy roughly aligned with perceptions of elite research universities (Docampo and Cram 2014). A first-hand account of the data sources as well as the conceptual drivers of the ranking can be found in Liu and Cheng (2005).…”

Section: Introductionmentioning

confidence: 99%

The effect of university mergers on the Shanghai ranking

2015

View full text Add to dashboard Cite

The growing influence of the idea of world-class universities and the associated phenomenon of international academic rankings are intriguing issues for contemporary comparative analyses of higher education. Although the Academic Ranking of World Universities (ARWU or the Shanghai ranking) was originally devised to assess the gap between Chinese universities and world-class universities, it has since been credited with roles in stimulating higher education change on many scales, from increasing the labor value of individual high-performing scholars to wholesale renovation of national university systems including mergers. This paper exhibits the response of the ARWU indicators and rankings to institutional mergers in general, and specifically analyses the universities of France that are engaged in a major amalgamation process motivated in part by a desire for higher international rankings.

show abstract

On the internal dynamics of the Shanghai ranking

Cited by 34 publications

References 26 publications

Creating the Context for Exploiting Linked Open Data in Multidimensional Academic Ranking

Creating the Context for Exploiting Linked Open Data in Multidimensional Academic Ranking

On the effects of institutional size in university classifications: the case of the Shanghai ranking

The effect of university mergers on the Shanghai ranking

Contact Info

Product

Resources

About