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Zeitschriftenartikel zum Thema „Ranking data“

Autor: Grafiati

Veröffentlicht am 1. Juli 2021

Zuletzt aktualisiert am 10. März 2023

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1

Banihashemi, Sayyid Ali, und Mohammad Khalilzadeh. „A new approach for ranking efficient DMUs with data envelopment analysis“. World Journal of Engineering 17, Nr. 4 (04.06.2020): 573–83. http://dx.doi.org/10.1108/wje-03-2020-0092.

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Purpose Classical models of data envelopment analysis (DEA) calculate the efficiency of decision-making units do not differentiate between efficient units. The purpose of this paper is to present a new method for ranking efficient units and compare it with the other methods presented in this field. Design/methodology/approach In this paper, a new method is presented for ranking efficient units. To validate the proposed method, a real case, which was studied by Li et al. (2016) is examined and the rankings of the efficient units are compared with four other methods including the Andersen and Petersen’s super-efficiency, game theory and the concept of Shapley value and the technique for order of preference by similarity to ideal solution methods. Findings The results show that there is a high correlation between the rankings of efficient units obtained by the new proposed method and the other methods such as Andersen and Petersen’s super-efficiency, game theory and Shapley value methods. Originality/value The problem of ranking efficient units with the DEA method is an important issue for researchers. Extensive studies have been proposed to provide methods for ranking efficient units. This paper proposes a simple and fast method for ranking efficient units that achieves better results.

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Zar, Jerrold H. „Ranking data with BASIC“. Behavior Research Methods, Instruments, & Computers 17, Nr. 1 (Januar 1985): 142. http://dx.doi.org/10.3758/bf03200918.

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3

Jestes, Jeffrey, Jeff M. Phillips, Feifei Li und Mingwang Tang. „Ranking large temporal data“. Proceedings of the VLDB Endowment 5, Nr. 11 (Juli 2012): 1412–23. http://dx.doi.org/10.14778/2350229.2350257.

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4

Grossman, J. P., und Gregory Minton. „Inversions in ranking data“. Discrete Mathematics 309, Nr. 20 (Oktober 2009): 6149–51. http://dx.doi.org/10.1016/j.disc.2009.04.030.

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5

Ivanov, A. A., und N. P. Yashina. „Big Data Analysis in Multi-Criteria Choice Problems“. Моделирование и анализ данных 12, Nr. 2 (2022): 5–19. http://dx.doi.org/10.17759/mda.2022120201.

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The problem of multi-criteria choice with non-uniform scales of criteria is considered. A model of a multicriteria choice problem is described, the main elements of which are sets of alternatives and quality criteria, as well as algorithms that allow ranking alternatives without prior reduction of the criteria scales to homogeneous ones. Algorithms for constructing aggregated ranking of alternatives are based on the construction of preference matrices by criteria containing information on the degree of superiority of one alternative over another. Propositions are proved that allow ranking alternatives with assessments according to two quality criteria. Algorithms for indexing alternatives are proposed that allow ranking alternatives for an arbitrary number of criteria. The best aggregated ranking is determined by the total distance to the rankings of alternatives by criteria. All algorithms have polynomial computational complexity, which makes it possible to work with large arrays of initial information. A software system for ranking alternatives in problems with big data has been developed. The initial information is stored in Excel tables, which makes it easy to take into account the limitations on the criteria scales. The operation of the software system is demonstrated by the example of choosing the best version of a drone for purchase in order to observe the terrain, shoot it and transmit information to the operator.

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Doğan, Güleda, und Umut Al. „Is it possible to rank universities using fewer indicators? A study on five international university rankings“. Aslib Journal of Information Management 71, Nr. 1 (21.01.2019): 18–37. http://dx.doi.org/10.1108/ajim-05-2018-0118.

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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.

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Wang, Jingyan, und Nihar B. Shah. „Ranking and Rating Rankings and Ratings“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 09 (03.04.2020): 13704–7. http://dx.doi.org/10.1609/aaai.v34i09.7126.

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Cardinal scores collected from people are well known to suffer from miscalibrations. A popular approach to address this issue is to assume simplistic models of miscalibration (such as linear biases) to de-bias the scores. This approach, however, often fares poorly because people's miscalibrations are typically far more complex and not well understood. It is widely believed that in the absence of simplifying assumptions on the miscalibration, the only useful information in practice from the cardinal scores is the induced ranking. In this paper we address the fundamental question of whether this widespread folklore belief is actually true. We consider cardinal scores with arbitrary (or even adversarially chosen) miscalibrations that is only required to be consistent with the induced ranking. We design rating-based estimators and prove that despite making no assumptions on the ratings, they strictly and uniformly outperform all possible estimators that rely on only the ranking. These estimators can be used as a plug-in to show the superiority of cardinal scores over ordinal rankings for a variety of applications, including A/B testing and ranking. This work thus provides novel fundamental insights in the eternal debate between cardinal and ordinal data: It ranks the approach of using ratings higher than that of using rankings, and rates both approaches in terms of their estimation errors.

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Rashid, Mahbub. „DesignIntelligence and the Ranking of Professional Architecture Programs: Issues, Impacts, and Suggestions“. Architecture 2, Nr. 3 (05.09.2022): 593–615. http://dx.doi.org/10.3390/architecture2030032.

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This paper studies the annual rankings of professional architectural degree programs by DesignIntelligence (DI). It uses a literature review and the statistical analysis of DI rankings and program-specific data to explore the limitations of the ranking system and its impacts on programs and public opinion. According to the findings of the study, the limitations of this system are related to the data it uses, the methods it uses to collect the data, and the way it uses the data for ranking purposes. Still, the ranking system can force architectural programs into a costly campaign for better ranks. It can also mislead prospective students in choosing programs that may not match their expectations. Additionally, it does not provide a reliable assessment of the capacity of a program to serve the profession and produce public good. It is suggested that a more objective, reliable, and relevant ranking system is needed for professional architecture degree programs. For this, the ranking system should emphasize criteria and methods different from the current DI system of rankings and should allow users to personalize rankings based on their perspectives, needs, and priorities.

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Alvo, Mayer, und Kadir Ertas. „Graphical methods for ranking data“. Canadian Journal of Statistics 20, Nr. 4 (Dezember 1992): 469–82. http://dx.doi.org/10.2307/3315616.

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10

Brady, Henry E. „Dimensional Analysis of Ranking Data“. American Journal of Political Science 34, Nr. 4 (November 1990): 1017. http://dx.doi.org/10.2307/2111470.

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11

Poon, Wai-Yin, und Wai Chan. „Influence analysis of ranking data“. Psychometrika 67, Nr. 3 (September 2002): 421–36. http://dx.doi.org/10.1007/bf02294994.

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12

Franz, Thomas, und Steffen Staab. „Ranking und Clustering Linked Data“. Datenbank-Spektrum 10, Nr. 2 (29.08.2010): 67–72. http://dx.doi.org/10.1007/s13222-010-0016-4.

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13

Bacon-Shone, John. „Ranking Methods for Compositional Data“. Applied Statistics 41, Nr. 3 (1992): 533. http://dx.doi.org/10.2307/2348087.

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14

Kundu, Anirban, Guanxiong Xu und Chunlin Ji. „Data Specific Ranking in Cloud“. International Journal of Cloud Applications and Computing 4, Nr. 4 (Oktober 2014): 32–41. http://dx.doi.org/10.4018/ijcac.2014100103.

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In this paper, the major goal is to achieve searching in distributed network with proper ranking. Heterogeneous framework is going to be utilized using existing machines and high-end workstations. Data redundancy is responsible for enhancing dynamic scheduling in run-time. Dynamic scheduling is going to be exploited determining possible sub-network and related servers for specific user targeted tasks using active and busy ranking status of servers. Minimization of time and maximization of speed are objectives of proposed system structure.

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Espejo, Mariano Ruiz. „Statistical Methods for Ranking Data“. International Statistical Review 83, Nr. 1 (April 2015): 172–73. http://dx.doi.org/10.1111/insr.12095_11.

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16

Hua, Ming, Jian Pei und Xuemin Lin. „Ranking queries on uncertain data“. VLDB Journal 20, Nr. 1 (06.07.2010): 129–53. http://dx.doi.org/10.1007/s00778-010-0196-4.

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17

Biolcati Rinaldi, Ferruccio, Daniele Checchi, Chiara Guglielmetti, Silvia Salini und Matteo Turri. „Ranking e valutazione: il caso delle classifiche delle universitŕ“. RIV Rassegna Italiana di Valutazione, Nr. 41 (Mai 2009): 81–114. http://dx.doi.org/10.3280/riv2008-041006.

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- Abstract The paper consists of two parts. The first is more general: it introduces to university ranking, shows the leading international ranking, discusses the uses people make of rankings. The second focuses on Italian ranking Censis-la Repubblica developing two different kinds of analyses: after considering indicators validity and reliability, principal components analysis and cluster analysis are applied to a partial replication of Censis-la Repubblica data. A list of points to pay attention comes out of these analyses: it can be useful when defining rankings of complex institutions such as universities.Key words: ranking, university ranking, Censis-la Repubblica, validity and reliability, normalisation and combination of indicators.

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Cramer, Kenneth M., und Stewart Page. „Cluster Analysis and Rankings of Canadian Universities: Misadventures with Rank-based Data and Implications for the Welfare of Students“. Applied Multivariate Research 12, Nr. 3 (13.01.2009): 183. http://dx.doi.org/10.22329/amr.v12i3.659.

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We present a data-based perspective concerning the Maclean’s magazine (November 17, 2003) rankings of Canadian universities, including two cluster analyses and other nonparametric analyses. These data are similar to those in recent university ranking exercises conducted by other magazines, such as U.S. News. In many cases, the cluster procedure showed that universities actually resemble and relate to each other in a manner different from their formal classification and final rank ordering by Maclean’s. Several pitfalls in ranking procedures, related to unreliable relationships among specific indices underlying the final ranks, are outlined. Comparisons are made also with the most recent student satisfaction rankings for 47 Canadian universities, published in November, 2003, by the Toronto Globe and Mail. The latter rankings do not reliably reflect the general results of the Maclean’s data. In their present format, and although they have become increasingly publicized and promoted, it remains difficult for the Maclean’s data to be consistently or empirically useful to students. Ranking exercises have unintended, though increasingly predictable, consequences, which likely bear heavily upon the intellectual and personal well being of students.

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Critchlow, Douglas E., und Joseph S. Verducci. „An Omnibus Test for Systematic Changes in Judges’ Rankings“. Journal of Educational Statistics 17, Nr. 1 (März 1992): 1–26. http://dx.doi.org/10.3102/10769986017001001.

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Paired rankings arise when each subject in a study independently ranks a set of items, undergoes a treatment, and afterwards ranks the same set of items. For such data, a statistical test is proposed to detect if the subjects’ posttreatment rankings have moved systematically toward some unknown ranking or set of rankings. The null hypothesis for this test is that each subject’s post-treatment ranking is symmetrically distributed about his pretreatment ranking. The exact and asymptotic null distributions of the test statistic are simulated and compared, and the power of the test is studied. Using paired rankings from an experimental course in literary criticism, we also offer some graphical methods for representing such data that help us to interpret the test results.

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Nassa, Anil Kumar, und Jagdish Arora. „Revisiting Ranking of Academic Institutions“. DESIDOC Journal of Library & Information Technology 41, Nr. 1 (11.02.2021): 5–19. http://dx.doi.org/10.14429/djlit.41.1.16673.

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The article provides an overview of ranking systems including its historical evolution, use of rankings by different stakeholders, ranking indicators, merits and demerits of different ranking systems and performance of Indian universities in in past one decade in global ranking systems. The article briefly describes nine global ranking systems and compares them based on weightage assigned to different categories of indicators and source of data used for ranking of HEIs. Lastly, article provides statistical inter-correlation amongst various ranking systems as well as intra-correlation within ranking systems at interval of five years (2011:2015; and 2016:2020) and 10 years (2011-2020).

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Lang, Liqian, Yan Wang, Qinghua Chen und Tao Zheng. „University ranking based on faculty hiring network with minimum weighted violation rankings“. International Journal of Modern Physics C 30, Nr. 07 (Juli 2019): 1940017. http://dx.doi.org/10.1142/s0129183119400175.

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University ranking arouses widespread interest among the society and the scientific community. It can cause resources to be allocated to the entity which has a higher ranking to make tremendous uneven distribution of resources such as funds, faculty, students and so on. Every year various controversial university rankings are issued by different institutions or individuals. However, they have to deal with a huge amount of data and cumbersome computing in their research. Furthermore, during the process of calculation, some key indicators are unreliable, subjective, and difficult to obtain or compute so that their results are easily questioned. An accurate and objective university ranking is important and necessary, but it still remains to be solved. In 2015, Clauset et al. creatively studied university rankings based on faculty hiring network with graduation-employment flow data. They used the minimum violation ranking (MVR) method to get a university ranking which has a high correlation with U.S. News & World Report (USN) and National Research Council (NRC) Ranking, implying a strong consistency between them. This method costs less and is also objective. Inspired by this thought, this paper proposed a new ranking algorithm with minimum weighted violation rankings derived through maximum likelihood estimation. This assumption is more reasonable, and the results are commendably consistent with the rankings of renowned agencies. This more general method is more flexible than non-weighted calculation. More importantly, this work revealed the essential mechanism of MVR by deriving maximum likelihood.

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Gaisenok, V. A., O. A. Naumovich und V. V. Samokhval. „Correlational Relationships between Various International Rankings for Higher Education Institutions“. Higher Education in Russia 27, Nr. 12 (18.01.2019): 20–28. http://dx.doi.org/10.31992/0869-3617-2018-27-12-20-28.

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The task of the investigation was to study the interrelations of the results of university rankings presented by various ranking agencies. Data on the number of the participants in the World Rankings has been presented for the Webometrics Ranking of World Universities (WRWU), Academic Ranking of World Universities (ARWU), Quacquarelli Symonds (QS), Times Higher Education (THE), U-Multirank, Round University Ranking (RUR), the Moscow International Rating “Three University Missions”, SCImago Institutions Rankings (SIR).The correlations have been studied by the methods of mathematical statistics for the results of the Moscow International Rating “Three University Missions” and the RUR, QS, THE, and the WRWU rankings; of the QS rankings with THE and the WRWU rankings, as well as for the two WRWU rankings published in January 2018 and July 2017. The linear regression formulas linking the positions of universities in different rankings have been obtained. The results of the Moscow International Rating “Three University Missions” moderately correlate with the results of the RUR rankings, the correlation with WRWU is low. The correlations for the QS ranking with THE and the WRWU rankings are moderate. Very high correlation has been detected only for the two WRWU rankings, which raises questions about the feasibility of conducting them with an interval of six months.

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Amir-Ahmadi, Pooyan, und Thorsten Drautzburg. „Identification and inference with ranking restrictions“. Quantitative Economics 12, Nr. 1 (2021): 1–39. http://dx.doi.org/10.3982/qe1277.

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We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

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Amir-Ahmadi, Pooyan, und Thorsten Drautzburg. „Identification and inference with ranking restrictions“. Quantitative Economics 12, Nr. 1 (2021): 1–39. http://dx.doi.org/10.3982/qe1277.

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We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

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Martyniuk, T. B., L. V. Krupelnitskyi und B. I. Krukivskyi. „REGULAR COMPUTATIONAL STRUCTURE FOR DATA RANKING“. Information technology and computer engineering 52, Nr. 3 (2021): 70–76. http://dx.doi.org/10.31649/1999-9941-2021-52-3-70-76.

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Chang, Yi, Anlei Dong, Pranam Kolari, Ruiqiang Zhang, Yoshiyuki Inagaki, Fernanodo Diaz, Hongyuan Zha und Yan Liu. „Improving recency ranking using twitter data“. ACM Transactions on Intelligent Systems and Technology 4, Nr. 1 (Januar 2013): 1–24. http://dx.doi.org/10.1145/2414425.2414429.

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Yiu, Man Lung, Hua Lu, Nikos Mamoulis und Michail Vaitis. „Ranking Spatial Data by Quality Preferences“. IEEE Transactions on Knowledge and Data Engineering 23, Nr. 3 (März 2011): 433–46. http://dx.doi.org/10.1109/tkde.2010.119.

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Clements, Margaret. „Ranking Data Sets - an easy method“. Teaching Statistics 13, Nr. 1 (März 1991): 11. http://dx.doi.org/10.1111/j.1467-9639.1991.tb00143.x.

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Böckenholt, Ulf. „Thurstonian representation for partial ranking data“. British Journal of Mathematical and Statistical Psychology 45, Nr. 1 (Mai 1992): 31–49. http://dx.doi.org/10.1111/j.2044-8317.1992.tb00976.x.

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Eftekhar, Milad, und Nick Koudas. „Partitioning and ranking tagged data sources“. Proceedings of the VLDB Endowment 6, Nr. 4 (Februar 2013): 229–40. http://dx.doi.org/10.14778/2535570.2488330.

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Hewett, Rattikorn, und Phongphun Kijsanayothin. „Tumor classification ranking from microarray data“. BMC Genomics 9, Suppl 2 (2008): S21. http://dx.doi.org/10.1186/1471-2164-9-s2-s21.

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Palma, Marco A. „Improving the prediction of ranking data“. Empirical Economics 53, Nr. 4 (22.09.2016): 1681–710. http://dx.doi.org/10.1007/s00181-016-1169-2.

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Xu, Hang, Mayer Alvo und Philip L. H. Yu. „Angle-based models for ranking data“. Computational Statistics & Data Analysis 121 (Mai 2018): 113–36. http://dx.doi.org/10.1016/j.csda.2017.12.004.

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Bonomi, Luca, und Xiaoqian Jiang. „Patient ranking with temporally annotated data“. Journal of Biomedical Informatics 78 (Februar 2018): 43–53. http://dx.doi.org/10.1016/j.jbi.2017.12.007.

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DAVID, H. A. „Ranking from unbalanced paired-comparison data“. Biometrika 74, Nr. 2 (1987): 432–36. http://dx.doi.org/10.1093/biomet/74.2.432.

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Huang, Wenjie. „Data-driven satisficing measure and ranking“. Journal of the Operational Research Society 71, Nr. 3 (25.04.2019): 456–74. http://dx.doi.org/10.1080/01605682.2019.1599779.

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Kakarala, Ramakrishna. „Interpreting the Phase Spectrum in Fourier Analysis of Partial Ranking Data“. Advances in Numerical Analysis 2012 (27.06.2012): 1–15. http://dx.doi.org/10.1155/2012/579050.

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Whenever ranking data are collected, such as in elections, surveys, and database searches, it is frequently the case that partial rankings are available instead of, or sometimes in addition to, full rankings. Statistical methods for partial rankings have been discussed in the literature. However, there has been relatively little published on their Fourier analysis, perhaps because the abstract nature of the transforms involved impede insight. This paper provides as its novel contributions an analysis of the Fourier transform for partial rankings, with particular attention to the first three ranks, while emphasizing on basic signal processing properties of transform magnitude and phase. It shows that the transform and its magnitude satisfy a projection invariance and analyzes the reconstruction of data from either magnitude or phase alone. The analysis is motivated by appealing to corresponding properties of the familiar DFT and by application to two real-world data sets.

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Hadad, Yossi, Lea Friedman und Aviad A. Israeli. „EVALUATING HOTEL ADVERTISEMENTS EFFICIENCY USING DATA ENVELOPMENT ANALYSIS“. Journal of Business Economics and Management 6, Nr. 3 (30.09.2005): 145–53. http://dx.doi.org/10.3846/16111699.2005.9636103.

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This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.

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Hadad, Yossi, Lea Friedman und Aviad A. Israeli. „EVALUATING HOTEL ADVERTISEMENTS EFFICIENCY USING DATA ENVELOPMENT ANALYSIS“. Journal of Business Economics and Management 5, Nr. 3 (30.09.2004): 133–41. http://dx.doi.org/10.3846/16111699.2004.9636077.

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This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.

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Page, Stewart. „Rankings of Canadian Universities, 1995: More Problems in Interpretation“. Canadian Journal of Higher Education 26, Nr. 2 (31.08.1996): 47–57. http://dx.doi.org/10.47678/cjhe.v26i2.183237.

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A critical perspective is presented in regard to rankings of Canadian universities by Maclean's magazine, November 20, 1995. Present comments are based, in part, on a previous analysis (Page, 1995) of the Maclean's rankings from 1993 and 1994. Several pitfalls in the ranking procedures, and results of some analyses of the 1995 ranking data, are briefly outlined.

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Węgrzyn, Tomasz. „The TMAI Model – Performance Of Portfolios Constructed On The Base Of Correlated And Uncorrelated Financial Ratios“. Folia Oeconomica Stetinensia 14, Nr. 2 (01.12.2014): 125–39. http://dx.doi.org/10.1515/foli-2015-0002.

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Abstract The author studies portfolio performance. Companies are chosen to portfolios due to their position in the ranking that is constructed on the base of the chosen financial ratios. There are three rankings constructed on different number of financial ratios. Each ranking is constructed on the base of synthetic measure of development. The TMAI ranking is constructed on the base of 48 financial ratios, the TMAI_gr1 ranking is constructed on the base of 14 financial ratios that can be correlated and the TMAI_gr2 ranking is constructed on the base of 8 uncorrelated financial ratios. The author uses data of companies listed on the Warsaw Stock Exchange between 2005 and 2011. The rankings and portfolios are built separately for each year. As a result, it can be stated that the Portfolio 3 in the TMAI_gr1 ranking is the best portfolio for investors who are maximizing the Sharpe ratio.

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Cheng, Min-Yuan, Cheng-Wei Su, Ming-Hsiu Tsai und Kuo-Shian Lin. „DATA PREPROCESSING FOR ARTIFICIAL NEURAL NETWORK APPLICATIONS IN PRIORITIZING RAILROAD PROJECTS – A PRACTICAL EXPERIENCE IN TAIWAN“. Journal of Civil Engineering and Management 18, Nr. 4 (11.09.2012): 483–94. http://dx.doi.org/10.3846/13923730.2012.699914.

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Financial constraints necessitate the tradeoff among proposed railroad projects, so that the project priorities for implementation and budget allocation need to be determined by the ranking mechanisms in the government. At present, the Taiwan central government prioritizes funding allocations primarily using the analytic hierarchy process (AHP), a methodology that permits the synthesizing of subjective judgments systematically and logically into objective consensus. However, due to the coopetition and heterogeneity of railway projects, the proper priorities of railroad projects could not be always evaluated by the AHP. The decision makers prefer subjective judgments to referring to the AHP evaluation results. This circumstance not only decreased the AHP advantages, but also raised the risk of the policies. A method to consider both objective measures and subjective judgments of project attributes can help reduce this problem. Accordingly, combining the AHP with the artificial neural network (ANN) methodologies would theoretically be a proper solution to bring a ranking predication model by creating the obscure relations between objective measures by the AHP and subjective judgments. However, the inconsistency between the AHP evaluation and subjective judgments resulted in the inferior soundness of the AHP/ANN ranking forecast model. To overcome this problem, this study proposes the data preprocessing method (DPM) to calculate the correlation coefficient value using the subjective and objective ranking incidence matrixes; according to the correlation coefficient value, the consistency between the AHP rankings and subjective judgments of railroad projects can be evaluated and improved, so that the forecast accuracy of the AHP/ANN ranking forecast model can also be enhanced. Based on this concept, a practical railroad project ranking experience derived from the Institute of Transportation of Taiwan is illustrated in this paper to reveal the feasibility of applying the DPM to the AHP/ANN ranking prediction model.

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Hopland, Arnt Ove, und Sturla Kvamsdal. „On the ranking of critical success factors“. Journal of Facilities Management 16, Nr. 1 (05.02.2018): 26–37. http://dx.doi.org/10.1108/jfm-04-2017-0012.

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Purpose The purpose of this paper is to discuss rankings of critical success factors (CSFs) from survey data, both with respect to what information such rankings should be based on and how to evaluate and interpret uncertainty from sampling errors. Design/methodology/approach The authors conducted a survey on CSFs in public facilities management. The survey data set covers two-thirds of all Norwegian local governments and 80 per cent of the Norwegian population. The authors analyze the data using basic statistics and bootstrap sampling techniques. Findings Rankings of CSFs are sensitive to the information one chooses to collect in the survey. With the survey data, the authors show that the typical approach of inquiring about importance of various factors leads to a different ranking of factors than if one inquires about cost efficiency. The authors further consider a ranking that reflects all information in the data; the authors look in particular at a ranking with equal weights to importance and cost efficiency. The authors also find that many factors, when controlling for sampling error, should be ranked equally, and that further considerations need to be consulted when priorities are decided. Originality/value The authors demonstrate the effect of cost efficiency and uncertainty considerations on rankings of CSFs for facilities management. The study paves the way for a broader and more comprehensive perspective on CSFs and what these factors should and could reflect.

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Bagherzadeh Valami, Hadi, und Reza Raeinojehdehi. „Ranking units in Data Envelopment Analysis with fuzzy data“. Journal of Intelligent & Fuzzy Systems 30, Nr. 5 (02.04.2016): 2505–16. http://dx.doi.org/10.3233/ifs-151756.

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Klimasara, Edward, und Andrzej P. Wierzbicki. „Examples of Objectiﬁed Multiple Criteria Ranking in the Selection of Infrastructural Projects“. Journal of Telecommunications and Information Technology 8 (28.12.2018): 18–25. http://dx.doi.org/10.26636/jtit.2018.126718.

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The paper addresses the issue of multiple criteria rankings of infrastructural projects (buildings, roads, etc.). Although the amount of literature devoted to this subject is considerable, all methods proposed produce subjective rankings, dependent on a direct or indirect deﬁnition of weighting coeﬃcients applicable to subsequent evaluation criteria. Infrastructural projects are usually selected and approved collegially, however, by a group of decision makers with preferences that may potentially diﬀer signiﬁcantly. Therefore, an objectiﬁed ranking, independent from subjectively deﬁned weighting coeﬃcients, is needed for infrastructural projects. Such a ranking is proposed, analyzed and applied by the authors of this paper. This ranking depends originally only on the multiple objective evaluation data, i.e. the values of evaluation criteria related to decision scenarios or alternatives. Such an approach does not render a fully objective ranking, since one of this kind does not exist at all. Even the choice of the ranking method is a subjective decision, but it is objectiﬁed to the extent possible. The paper presents several examples of multiple criteria evaluation of infrastructural projects, derived from literature, and compares subjective rankings published in literature with objectiﬁed rankings that are independent of weighting coeﬃcients.

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Kilburn, Ashley, Brandon Kilburn, Kevin Hammond und Denise Williams. „The Pursuit Of Excellence: 3rd-Party Rankings And Positioning Of Online Programs Through Quality And Value“. American Journal of Business Education (AJBE) 13, Nr. 1 (01.01.2020): 19–26. http://dx.doi.org/10.19030/ajbe.v13i1.10327.

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Third-party ranking agencies (e.g., US News and World Report, Center for Online Education, CollegeChoice.net) produce rankings that are a popular publicly accessible search option for potential students as they sort online degree options. While higher education administration is keen to the possibility that most or all ranking systems risk methodological flaws, potential students are still on board with rankings and seem significantly steered by their results (Barkhorn, 2014). Monitoring rankings has become a positioning consideration for academic institutions. The disparity amongst ranking factors across time and across assessor, along with the mismatch between ranking methods used and suggested methodologies from research on higher education begs the question: which attributes are colleges and universities supposed to leverage? This study reports a fully mediated SEM using data from online student feedback across multiple quality touch points: faculty, LMS and course on student loyalty as mediated by perceived value.

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Myers, Noah, Neal Snow, Scott L. Summers und David A. Wood. „Accounting Institution Citation-Based Research Rankings by Topical Area and Methodology“. Journal of Information Systems 30, Nr. 3 (01.11.2015): 33–62. http://dx.doi.org/10.2308/isys-51343.

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ABSTRACT This study creates citation-based rankings for accounting institutions by topical areas (AIS, audit, financial, managerial, tax, and other) and methodologies (archival, analytical, experimental, and other) extending prior count-based ranking studies that disaggregate rankings by topic and methodology. We report separate rankings for different year windows (previous six years, 12 years, and since 1990) and only give institutions credit for authors who currently work for the institution. We show that disaggregated citation-based rankings are important as the correlations for some topic areas and methodologies with an overall ranking are modest. We also show that the correlation for citation-based and count-based rankings can differ significantly in some situations suggesting the importance of considering both types of rankings in decision making. Data Availability: Requests for data may be made to the authors.

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Laird, Nan M., und Thomas A. Louis. „Empirical Bayes Ranking Methods“. Journal of Educational Statistics 14, Nr. 1 (März 1989): 29–46. http://dx.doi.org/10.3102/10769986014001029.

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Ranking problems arise in setting priorities for investigations, in providing a simple summary of performance, in comparing objects in a manner robust to measurement scale, and in a wide variety of other applications. Commonly, rankings are computed from measurements that depend on the true attribute. Using the Gaussian model, we propose and compare methods for using these measurements to estimate the ranks of the underlying attributes and show that those based on an empirical Bayes model produce estimates that differ from ranking observed data. These differences result both from the effect of shrinking posterior means towards a common value by an amount that depends on the precision of individual measurements and from the Bayes processing of the posterior distribution to produce estimates that account for the uncertainty in the distribution of the ranks. We illustrate different ranking methods using data on school achievement reported by Aitkin and Longford (1986) . Mathematical and empirical results highlight the importance of using appropriate ranking methods and identify issues requiring further research.

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Chiang, Austin Lee, Loren Galler Rabinowitz, Akhil Kumar und Walter Wai-Yip Chan. „Association Between Institutional Social Media Involvement and Gastroenterology Divisional Rankings: Cohort Study“. Journal of Medical Internet Research 21, Nr. 9 (06.09.2019): e13345. http://dx.doi.org/10.2196/13345.

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Background Patients often look to social media as an important tool to gather information about institutions and professionals. Since 1990, United States News and World Report (USNWR) has published annual rankings of hospitals and subspecialty divisions. It remains unknown if social media presence is associated with the USNWR gastroenterology and gastrointestinal (GI) surgery divisional rankings, or how changes in online presence over time affects division ranking. Objective The objective of this study was to determine if social media presence is associated with USNWR gastroenterology and GI surgery divisional rankings and to ascertain how changes in online presence over time affect division rankings. Methods Social media presence among the top 30 institutions listed in the 2014 USNWR gastroenterology and GI surgery divisional rankings were assessed using Pearson’s correlation coefficients and multivariate analysis, controlling for covariates. Linear and logistic regression using data from 2014 and 2016 USNWR rankings were then used to assess the association between institutional ranking or reputation score with any potential changes in numbers of followers over time. Sensitivity analysis was performed by assessing the area under the receiver operating characteristic curve to determine the follower threshold associated with improved or maintained ranking, which was done by dichotomizing changes in followers at values between the 7000 and 12,000 follower mark. Results Twitter follower count was an independent predictor of divisional ranking (β=.00004; P<.001) and reputation score (β=–.00002; P=.03) in 2014. Academic affiliation also independently predicted USNWR division ranking (β=5.3; P=.04) and reputation score (β=–7.3; P=.03). Between 2014 and 2016, Twitter followers remained significantly associated with improved or maintained rankings (OR 14.63; 95% CI 1.08-197.81; P=.04). On sensitivity analysis, an 8000 person increase in Twitter followers significantly predicted improved or maintained rankings compared to other cutoffs. Conclusions Institutional social media presence is independently associated with USNWR divisional ranking and reputation score. Improvement in social media following was also independently associated with improved or maintained divisional ranking and reputation score, with a threshold of 8000 additional followers as the best predictor of improved or stable ranking.

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Kosztyán, Zsolt T., Zsuzsanna Banász, Vivien V. Csányi und András Telcs. „Rankings or leagues or rankings on leagues? - Ranking in fair reference groups“. Tertiary Education and Management 25, Nr. 4 (17.10.2019): 289–310. http://dx.doi.org/10.1007/s11233-019-09028-x.

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Abstract There are several well-known rankings of universities and higher education systems. Numerous recent studies question whether it is possible to compare universities and countries of different constitutions. These criticisms stand on solid ground. It is impossible to create a one-dimensional ordering that faithfully compares complex systems such as universities or even higher education systems. We would like to convince the reader that using well-chosen elements of a family of state-of-the-art data mining methods, namely, bi-clustering methods, can provide an informative picture of the relative positions of universities/higher education systems. Bi-clustering methods produce leagues of comparable entities alongside the indicators, which produce a similar grouping of them. Within leagues, partial rankings could be specified and furthermore can serve as a proper basis for benchmarking.

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