Is it possible to rank universities using fewer indicators? A study on five international university rankings

2019 ◽  
Vol 71 (1) ◽  
pp. 18-37 ◽  
Author(s):  
Güleda Doğan ◽  
Umut Al
Keyword(s):  
Similarity Measure ◽  
Decision Makers ◽  
Cosine Similarity ◽  
University Rankings ◽  
University Ranking ◽  
Data Set ◽  
Content Type ◽  
Research Questions ◽  
Cosine Similarity Measure ◽  
International University

Purpose The purpose of this paper is to analyze the similarity of intra-indicators used in research-focused international university rankings (Academic Ranking of World Universities (ARWU), NTU, University Ranking by Academic Performance (URAP), Quacquarelli Symonds (QS) and Round University Ranking (RUR)) over years, and show the effect of similar indicators on overall rankings for 2015. The research questions addressed in this study in accordance with these purposes are as follows: At what level are the intra-indicators used in international university rankings similar? Is it possible to group intra-indicators according to their similarities? What is the effect of similar intra-indicators on overall rankings? Design/methodology/approach Indicator-based scores of all universities in five research-focused international university rankings for all years they ranked form the data set of this study for the first and second research questions. The authors used a multidimensional scaling (MDS) and cosine similarity measure to analyze similarity of indicators and to answer these two research questions. Indicator-based scores and overall ranking scores for 2015 are used as data and Spearman correlation test is applied to answer the third research question. Findings Results of the analyses show that the intra-indicators used in ARWU, NTU and URAP are highly similar and that they can be grouped according to their similarities. The authors also examined the effect of similar indicators on 2015 overall ranking lists for these three rankings. NTU and URAP are affected least from the omitted similar indicators, which means it is possible for these two rankings to create very similar overall ranking lists to the existing overall ranking using fewer indicators. Research limitations/implications CWTS, Mapping Scientific Excellence, Nature Index, and SCImago Institutions Rankings (until 2015) are not included in the scope of this paper, since they do not create overall ranking lists. Likewise, Times Higher Education, CWUR and US are not included because of not presenting indicator-based scores. Required data were not accessible for QS for 2010 and 2011. Moreover, although QS ranks more than 700 universities, only first 400 universities in 2012–2015 rankings were able to be analyzed. Although QS’s and RUR’s data were analyzed in this study, it was statistically not possible to reach any conclusion for these two rankings. Practical implications The results of this study may be considered mainly by ranking bodies, policy- and decision-makers. The ranking bodies may use the results to review the indicators they use, to decide on which indicators to use in their rankings, and to question if it is necessary to continue overall rankings. Policy- and decision-makers may also benefit from the results of this study by thinking of giving up using overall ranking results as an important input in their decisions and policies. Originality/value This study is the first to use a MDS and cosine similarity measure for revealing the similarity of indicators. Ranking data is skewed that require conducting nonparametric statistical analysis; therefore, MDS is used. The study covers all ranking years and all universities in the ranking lists, and is different from the similar studies in the literature that analyze data for shorter time intervals and top-ranked universities in the ranking lists. It can be said that the similarity of intra-indicators for URAP, NTU and RUR is analyzed for the first time in this study, based on the literature review.

Multi-criteria decision making method based on improved cosine similarity measure with interval neutrosophic sets

2019 ◽  
Vol 12 (3) ◽  
pp. 414-423 ◽  
Author(s):  
Lunyan Wang ◽  
Qing Xia ◽  
Huimin Li ◽  
Yongchao Cao
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Interval Number ◽  
Cosine Similarity ◽  
Weighted Averaging ◽  
Multi Criteria Decision Making ◽  
Neutrosophic Sets ◽  
Content Type ◽  
Cosine Similarity Measure ◽  
Interval Neutrosophic Sets

Purpose The fuzziness and complexity of evaluation information are common phenomenon in practical decision-making problem, interval neutrosophic sets (INSs) is a power tool to deal with ambiguous information. Similarity measure plays an important role in judging the degree between ideal and each alternative in decision-making process, the purpose of this paper is to establish a multi-criteria decision-making method based on similarity measure under INSs. Design/methodology/approach Based on an extension of existing cosine similarity, this paper first introduces an improved cosine similarity measure between interval neutosophic numbers, which considers the degrees of the truth membership, the indeterminacy membership and the falsity membership of the evaluation values. And then a multi-criteria decision-making method is established based on the improved cosine similarity measure, in which the ordered weighted averaging (OWA) is adopted to aggregate the neutrosophic information related to each alternative. Finally, an example on supplier selection is given to illustrate the feasibility and practicality of the presented decision-making method. Findings In the whole process of research and practice, it was realized that the application field of the proposed similarity measure theory still should be expanded, and the development of interval number theory is one of further research direction. Originality/value The main contributions of this paper are as follows: this study presents an improved cosine similarity measure under INSs, in which the weights of the three independent components of an interval number are taken into account; OWA are adopted to aggregate the neutrosophic information related to each alternative; and a multi-criteria decision-making method using the proposed similarity is developed under INSs.

Does sustainability reporting promote university ranking? Australian and New Zealand evidence

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuan George Shan ◽  
Junru Zhang ◽  
Manzurul Alam ◽  
Phil Hancock
Keyword(s):  
New Zealand ◽  
Generalized Method Of Moments ◽  
Sustainability Reporting ◽  
Ordinary Least Squares ◽  
University Rankings ◽  
University Ranking ◽  
Data Set ◽  
Content Type ◽  
The University ◽  
Reporting Channels

Purpose This study aims to investigate the relationship between university rankings and sustainability reporting among Australia and New Zealand universities. Even though sustainability reporting is an established area of investigation, prior research has paid inadequate attention to the nexus of university ranking and sustainability reporting. Design/methodology/approach This study covers 46 Australian and New Zealand universities and uses a data set, which includes sustainability reports and disclosures from four reporting channels including university websites, and university archives, between 2005 and 2018. Ordinary least squares regression was used with Pearson and Spearman’s rank correlations to investigate the likelihood of multi-collinearity and the paper also calculated the variance inflation factor values. Finally, this study uses the generalized method of moments approach to test for endogeneity. Findings The findings suggest that sustainability reporting is significantly and positively associated with university ranking and confirm that the four reporting channels play a vital role when communicating with university stakeholders. Further, this paper documents that sustainability reporting through websites, in addition to the annual report and a separate environment report have a positive impact on the university ranking systems. Originality/value This paper contributes to extant knowledge on the link between university rankings and university sustainability reporting which is considered a vital communication vehicle to meet the expectation of the stakeholder in relevance with the university rankings.

scholarly journals Consistency Fuzzy Sets and a Cosine Similarity Measure in Fuzzy Multiset Setting and Application to Medical Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ezgi Türkarslan ◽  
Jun Ye ◽  
Mehmet Ünver ◽  
Murat Olgun
Keyword(s):  
Correlation Coefficient ◽  
Similarity Measure ◽  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Uncertain Data ◽  
Cosine Similarity ◽  
Comparison Analysis ◽  
Average Value ◽  
Truth Function ◽  
Cosine Similarity Measure

The main purpose of this study is to construct a base for a new fuzzy set concept that is called consistency fuzzy set (CFS) which expresses the multidimensional uncertain data quite successfully. Our motive is to reduce the complexity and difficulty caused by the information contained in the truth sequence in a fuzzy multiset (FMS) and to present the data of the truth sequence in a more understandable and compact manner. Therefore, this paper introduces the concept of CFS that is characterized with a truth function defined on a universal set 0,1 2 . The first component of the truth pair of a CFS is the average value of the truth sequence of a FMS and the second component is the consistency degree, that is, the fuzzy complement of the standard deviation of the truth sequence of the same FMS. The main contribution of a CFS is the reflection of both the level of the average of the data that can be expressed with the different sequence lengths and the degree of the reasonable information in data via consistency degree. To develop this new concept, this paper also presents a correlation coefficient and a cosine similarity measure between CFSs. Furthermore, the proposed correlation coefficient and cosine similarity measure are applied to a multiperiod medical diagnosis problem. Finally, a comparison analysis is given between the obtained results and the existing results in literature to show the efficiency and rationality of the proposed correlation coefficient and cosine similarity measure.

Comparison of Two Classrooms

2014 ◽  
pp. 302-320
Author(s):  
Barış Ergen
Keyword(s):  
Similarity Measure ◽  
Regional Planning ◽  
Environmental Science ◽  
Cosine Similarity ◽  
Urban And Regional Planning ◽  
Cosine Similarity Measure ◽  
Science Classes ◽  
International Conventions

This chapter investigates how students attending environmental science classes in the Department of Urban and Regional Planning at Bozok University in 2010-2011 and 2011-2012 fall semesters learn concepts related to environmental science through a comparison of two different classes, using the Cosine Similarity Measure (CSM) method. The study demonstrates that the students lack the necessary knowledge about the concepts used in urban and regional planning literature and international conventions.

scholarly journals Crop Recommender System for the Farmers using Mamdani Fuzzy Inference Model

2018 ◽  
Vol 7 (4.15) ◽  
pp. 277 ◽  
Author(s):  
Madhusree Kuanr ◽  
Bikram Kesari Rath ◽  
Sachi Nandan Mohanty
Keyword(s):  
Fuzzy Logic ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Fuzzy Inference ◽  
Weather Condition ◽  
Cosine Similarity ◽  
Inference Model ◽  
Large Pool ◽  
Cosine Similarity Measure

Recommender systems provide suggestions to the users for choosing particular items from a large pool of items. The purpose of this study is to design a collaborative recommender system for the farmers for recommending giving prior idea regarding a crop which is suitable according to the location of the farmer based on weather condition of the previous months. The proposed system also recommends other seeds, pesticides and instruments according to the preferences in farming and location of the farmers while purchasing the seeds through online. It uses cosine similarity measure to find the similar user according the location of the farmer and fuzzy logic for predicting the yield of rice crop for Kharif season in state Odisha, India. The proposed system is implemented in Mamdani Fuzzy Inference model. The results reveal that it provides prior idea regarding a crop before sowing of seeds.  

A cosine similarity measure for multi-criteria group decision making under neutrosophic soft environment

2020 ◽  
Vol 39 (5) ◽  
pp. 7863-7880
Author(s):  
Yuanxiang Dong ◽  
Xiaoting Cheng ◽  
Weijie Chen ◽  
Hongbo Shi ◽  
Ke Gong
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Group Decision Making ◽  
Group Decision ◽  
Evidence Theory ◽  
Cosine Similarity ◽  
Uncertain Information ◽  
Soft Sets ◽  
Cosine Similarity Measure ◽  
Credibility Degree

In actual life, uncertain and inconsistent information exists widely. How to deal with the information so that it can be better applied is a problem that has to be solved. Neutrosophic soft sets can process uncertain and inconsistent information. Also, Dempster-Shafer evidence theory has the advantage of dealing with uncertain information, and it can synthesize uncertain information and deal with subjective judgments effectively. Therefore, this paper creatively combines the Dempster-Shafer evidence theory with the neutrosophic soft sets, and proposes a cosine similarity measure for multi-criteria group decision making. Different from the previous studies, the proposed similarity measure is utilized to measure the similarity between two objects in the structure of neutrosophic soft set, rather than two neutrosophic soft sets. We also propose the objective degree and credibility degree which reflect the decision makers’ subjective preference based on the similarity measure. Then parameter weights are calculated by the objective degree. Additionally, based on credibility degree and parameter weights, we propose the modified score function, modified accuracy function, and modified certainty function, which can be employed to obtain partial order relation and make decisions. Later, we construct an aggregation algorithm for multi-criteria group decision making based on Dempster’s rule of combination and apply the algorithm to a case of medical diagnosis. Finally, by testing and comparing the algorithm, the results demonstrate that the proposed algorithm can solve the multi-criteria group decision making problems effectively.

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