COMP: uma métrica para avaliar a compatibilidade entre as informações fornecidas pelos rankings e aquelas percebidas pelos usuários

Autores

DOI:

https://doi.org/10.18265/1517-03062015v1n41p113-127

Palavras-chave:

Informações dos rankings, Percepção dos usuários, Categorias top, Métrica, Cluster.

Resumo

Este estudo propôs a criação de uma métrica – chamada COMP – para mensurar a relação entre as informações disponibilizadas pelos rankings e aquelas percebidas pelos usuários. Para isso, estudos de psicologia foram utilizados para dar sustentação teórica às propriedades definidas para a métrica. Matematicamente, foram utilizados métodos determinísticos e probabilísticos no cálculo, entre eles média ponderada, distância de Mahalanobis, o método de clustering Affinity Propagation e a medida de correlação Tau de Kendall. Os resultados mostraram-se válidos e confiáveis através da aplicação em rankings simulados. Rankings reais foram utilizados para exemplificar aplicações práticas da métrica. Entende-se que essa proposta de mensuração contribui para evidenciar um aspecto até então pouco considerado em contextos de rankings.

Downloads

Não há dados estatísticos.

Referências

ABU SHARKH, M.; GOUGH, I. Global welfare regimes: a cluster analysis. Global Social Policy, 23 mar. 2010. v. 10, n. 1, p. 27–58.

ALLEN, V.L.; WILDER, D.A. Group Categorization and Attribution of Belief Similarity. Small Group Behavior v. 10, n. 1, p. 73–80 , 1979.

APELTSIN, L. et al. Improving the quality of protein similarity network clustering algorithms using the network edge weight distribution. Bioinformatics (Oxford, England), 2011. v. 27, n. 3, p. 326–33.

BANDYOPADHYAY, S.; SAHA, S. Unsupervised classification - similarity measures, classical and metaheuristic approaches, and applications. [S.l.]: [s.n.], 2013.

BRENNER, L.; ROTTENSTREICH, Y.; SOOD, S. Comparison, Grouping, and Preference. Psychological Science v. 10, n. 3, p. 225–229 , 1999.

BONETT, D. G.; WRIGHT, T. A. Sample size requirements for estimating pearson, kendall and spearman correlations. Psychometrika, 2000. v. 65, n. 1, p. 23–28.

BRENNER, L.; ROTTENSTREICH, Y.; SOOD, S. Comparison, grouping, and preference. Psychological Science, 1999. v. 10, n. 3, p. 225–229.

BUSH, R. A. et al. Role of reputation in top pediatric specialties rankings. Pediatrics, 2011. v. 128, n. 6, p. 1168–1172.

CARARE, O. The impact of bestseller rank on demand: evidence from the app market. International Economic Review, 2012. v. 53, n. 3, p. 717–742.

CAVUSGIL, S. T.; KIYAK, T.; YENIYURT, S. Complementary approaches to preliminary foreign market opportunity assessment: country clustering and country ranking. Industrial Marketing Management, 2004. v. 33, n. 7, p. 607–617.

CENTER FOR WORLD-CLASS UNIVERSITIES OF SHANGHAI JIAO TONG UNIVERSITY (CWCU). Academic ranking of world universities – 2012. ShanghaiRanking Consultancy, [S.l.], 2012.

CHEN, C.-M. Classification of scientific networks using aggregated journal-journal citation relations in the journal citation reports. Journal of the American Society for Information Science and Technology, 2008. v. 59, n. 14, p. 2296–2304.

COOK, D. A; BECKMAN, T. J. Current concepts in validity and reliability for psychometric instruments: theory and application. The American journal of medicine, 2006. v. 119, n. 2, p. 166.e7–16.

COUPLAND, Nikolas. How frequent are numbers? Language & Communication v. 31, n. 1, p. 27–37 , 2011.

DAHAENE, Stanislas; MEHLER, Jacques. Cross-linguistic regularities in the frequency of number words. Cognition v. 43, p. 1–29 , 1992.

DE LUSIGNAN, S et al. End-digit preference in blood pressure recordings of patients with ischaemic heart disease in primary care. Journal of human hypertension v. 18, n. 4, p. 261–5 , 2004.

DOWNING, S. M. Reliability : on the reproducibility of assessment data. Medical Education, 2004. v. 38, p. 1006–1012.

DUBES, R. C. How many clusters are best? - an experiment. Pattern Recognition, 1987. v. 20, n. 6, p. 645–663.

FERNANDES, S. An empirical approach of the distinctive aspects for socioeconomic development. International Journal of Social Economics, 2013. v. 40, n. 11, p. 956–970.

FIELD, A. Discovering statistics using spss. 3 ed. [S.l.]: SAGE, 2009.

FRASER INSTITUTE. Economic freedom of the world. Dental economics - oral hygiene. [S.l.]: [s.n.], 2012.

FREY, B. J.; DUECK, D. Clustering by passing messages between data points. Science (New York, N.Y.), 2007. v. 315, n. 5814, p. 972–6.

HAIR, J. F. et al. Multivariate data analysis. 5th. ed. New Jersey.: Prentice-Hall, 1998.

HONG-WEI, L. Community detection by affinity propagation with various similarity measures. in. [S.l.]: IEEE, 2011. p. 182–186.

HORN, A. S.; HENDEL, D. D.; FRY, G. W. Ranking the international dimension of top research universities in the united states. Journal of Studies in International Education, 2007. v. 11, n. 3-4, p. 330–358.

HORNIK, J.; CHERIAN, J.; ZAKAY, D. The influence of prototypic values on the validity of studies using time estimates. Journal of the Market Research Society , 1994.

HOYLAND, B.; MOENE, K.; WILLUMSEN, F. The tyranny of international index rankings. Journal of Development Economics, 2012. v. 97, n. 1, p. 1–14.

ISAAC, M. S.; SCHINDLER, R. M. The top-ten effect: consumers’ subjective categorization of ranked lists. Journal of Consumer Research, 2013.

J. MCQUEEN. Some methods for classification and analysis of multivariate observations. [S.l.]: [s.n.], 1967. p. 281–297.

JAIN, A. K. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 2010. v. 31, n. 8, p. 651–666.

JAIN, A. K.; MURTY, M. N.; FLYNN, P. J. Data clustering : a review. ACM Computing Surveys, 1999. v. 31, n. 3, p. 264–323.

JANSEN, C.J.M.; POLLMANN, M.M.W. On Round Numbers : Pragmatic Aspects of Numerical Expressions. Journal of Quantitative Linguistics v. 8, n. 3, p. 187–201 , 2001.

JÖNS, H.; HOYLER, M. Global geographies of higher education: the perspective of world university rankings. Geoforum, 2013. v. 46, p. 45–59.

KAUFMAN, E. L. et al. The discrimination of visual number. The American Journal of Psychology, 1949. v. 62, n. 4, p. 498–525.

KEMP, C.; TENENBAUM, J. B. The discovery of structural form. Proceedings of the National Academy of Sciences of the United States of America, 2008. v. 105, n. 31, p. 10687–92.

KETTENRING, J. R. The practice of cluster analysis. Journal of Classification, 2006. v. 23, n. 1, p. 3–30.

KIDDLE, S. J. et al. Temporal clustering by affinity propagation reveals transcriptional modules in arabidopsis thaliana. Bioinformatics (Oxford, England), 2010. v. 26, n. 3, p. 355–62.

KUBOVY, Michael; WAGEMANS, Johan. Grouping by Proximity and Multistability in Dot Lattices: A Quantitative Gestalt Theory. Psychological Science v. 6, n. 4, p. 225–234 , 1995.

LASKI, Elida V; SIEGLER, Robert S. Is 27 a big number? Correlational and causal connections among numerical categorization, number line estimation, and numerical magnitude comparison. Child development v. 78, n. 6, p. 1723–43 , 2007.

LECLERC, F.; HSEE, C. K.; NUNES, J. C. Narrow focusing: why the relative position of a good in its category matters more than it should. Marketing Science, 2005. v. 24, n. 2, p. 194–205.

LEDOLTER, J.; ABRAHAM, B. Parsimony and its importance in time series forecasting. Technometrics, 1981. v. 23, n. 4, p. 411–414.

LEONE, M.; SUMEDHA; WEIGT, M. Clustering by soft-constraint affinity propagation: applications to gene-expression data. Bioinformatics (Oxford, England), 2007. v. 23, n. 20, p. 2708–15.

LOCKSLEY, Anne; ORTIZ, Vilma; HEPBURN, Christine. Social categorization and discriminatory behavior: Extinguishing the minimal intergroup discrimination effect. Journal of Personality and Social Psychology v. 39, n. 5, p. 773–783 , 1980.

MADDOX, Keith B. et al. Social influences on spatial memory. Memory & Cognition v. 36, n. 3, p. 479–494 , 2008.

MAESSCHALCK, R. DE; JOUAN-RIMBAUD, D.; MASSART, D. L. The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 2000. v. 50, n. 1, p. 1–18.

MAHALANOBIS, P. C. On the generalized distance in statistics. Calcutta: [s.n.], 1936. V. 2, p. 49–55.

MAKI, R H. Why do categorization effects occur in comparative judgment tasks? Memory & cognition v. 10, n. 3, p. 252–64 , 1982.

MARCOT, B. G. Metrics for evaluating performance and uncertainty of bayesian network models. Ecological Modelling, 2012. v. 230, p. 50–62.

MARWELL, G. Departmental demography and reputational success: the fall and rise of top sociology departments 1950–1980. The American Sociologist, 2012. v. 43, n. 3, p. 294–309.

MAVRI, M. Classifying greek banks based on bank ranking index (bri). Benchmarking: An International Journal, 2013. v. 20, n. 5, p. 607–624.

MCLACHLAN, G. F. Mahalanobis distance. Resonance, 1999. n. June, p. 20–26.

MIGON, E. X. F. G.; CUNHA, R. M. Descobrindo a forma estrutural: uma apreciação empírica da (in)segurança africana sob a perspectiva da ciência cognitiva. In: Seminário Brasileiro de Estudos Estratégicos Internacionais, 2013, Porto Alegre. O Atlântico Sul como Eixo da Inserção Internacional do Brasil. Porto Alegre: [s.n.], 2013.

MILLER, G. A. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 1956. v. 63, p. 81–97.

MILLER, T.; HOLMES, K. R.; FEULNER, E. J. 2013 index of economic freedom. Washington, D.C.; New York: [s.n.], 2013.

MISHRA, Arul; MISHRA, Himanshu. Border bias: the belief that state borders can protect against disasters. Psychological science v. 21, n. 11, p. 1582–6 , 2010.

NAUDÉ, P.; HENNEBERG, S.; JIANG, Z. Varying routes to the top: identifying different strategies in the mba marketplace. Journal of the Operational Research Society, 2009. v. 61, n. 8, p. 1193–1206.

NOLEN, A. L. The content of educational psychology: an analysis of top ranked journals from 2003 through 2007. Educational Psychology Review, 2009. v. 21, n. 3, p. 279–289.

ONDA, K. et al. Country clustering applied to the water and sanitation sector: a new tool with potential applications in research and policy. International journal of hygiene and environmental health, 2014. v. 217, n. 2-3, p. 379–85.

ÖNSEL, ?. et al. A new perspective on the competitiveness of nations. Socio-Economic Planning Sciences, 2008. v. 42, n. 4, p. 221–246.

OTTEN, S. “Me and us” or “us and them”? The self as a heuristic for defining minimal ingroups. European review of social psychology v. 13, n. 1, p. 1–33 , 2002.

PITSILIS, G.; ZHANG, X.; WANG, W. Clustering recommenders in collaborative filtering using explicit trust information. [S.l.]: Springer Berlin Heidelberg, 2011. p. 82–97.

POPE, Devin; SIMONSOHN, Uri. Round numbers as goals: evidence from baseball, SAT takers, and the lab. Psychological science v. 22, n. 1, p. 71–9 , 2011.

PROBABILISTIC AND STATISTICAL INFERENCE GROUP (PSIG). Affinity propagation faq. University of Toronto, [S.l.], 2014. Disponível em: <http://www.psi.toronto.edu/affinitypropagation/faq.html>.

RAD, A.; NADERI, B.; SOLTANI, M. Clustering and ranking university majors using data mining and ahp algorithms: a case study in iran. Expert Systems with Applications, 2011. v. 38, n. 1, p. 755–763.

RENDE, S.; DONDURAN, M. Neighborhoods in development: human development index and self-organizing maps. Social Indicators Research, 2011. v. 110, n. 2, p. 721–734.

REVENUE WATCH INSTITUTE (RWI). The 2013 resource governance index - a measure of transparency and accountability in the oil, gas and mining sector. [S.l.]: [s.n.], 2013.

RODE, M.; COLL, S. Economic freedom and growth. which policies matter the most? Constitutional Political Economy, 2011. v. 23, n. 2, p. 95–133.

SEHGAL, A. R. The role of reputation in u. s. news & world report’s rankings of the top 50 american hospitals. [S.l.]: [s.n.], 2010. p. 521–525.

SHARMA, R.; GIVENS-SKEATON, S. Ranking the top 100 firms according to gender diversity. Advancing Women in Leadership Journal, 2010. v. 30, n. 3.

SHEPARD, Roger N; KILPATRIC, Dan W; CUNNINGHAM, James P. The Internal Representation of Numbers. Cognitive Psychology v. 7, p. 82–138 , 1975.

TAMMI, T. The competitive funding of university research: the case of finnish science universities. Higher Education, 2009. v. 57, n. 5, p. 657–679.

TARRANT, M. A.; MANFREDO, M. J. Digit preference, recall bias, and nonresponse bias in self reports of angling participation. Leisure Sciences v. 15, n. 3, p. 231–238 , 1993.

TEGARDEN, D. P.; TEGARDEN, L. F.; SHEETZ, S. D. Cognitive factions in a top management team: surfacing and analyzing cognitive diversity using causal maps. Group Decision and Negotiation, 2007. v. 18, n. 6, p. 537–566.

THE FUND FOR PEACE (TFFF). Failed states index 2012. Washington, DC, USA: [s.n.], 2012.

______. Conflict assessment framework manual (cast). Washington, D.C.: [s.n.], 2014a.

______. Reviews and studies of the cast methodology and the failed states index. [S.l.], 2014b. Disponível em: <http://ffp.statesindex.org/reviews>.

THOMSON REUTERS. Times higher education world university rankings 2012-2013. Thomson Reuters, [S.l.], 2013. Disponível em: <http://www.timeshighereducation.co.uk/world-university-rankings/2012-13/world-ranking>

TVERSKY, Barbara. Distortions in Cognitive Maps. Geoforum v. 23, n. 2, p. 131–138 , 1992.

UNITED NATIONS DEVELOPMENT PROGRAMME (UNDP). Human development report 2013 - the rise of the south: humans progress in a diverse world. New York: [s.n.], 2013a.

______. Human development report 2013 - technical notes. New York: [s.n.], 2013b.

VALADKHANI, A.; VILLE, S. Ranking and clustering of the faculties of commerce research performance in australia. Applied Economics, 2010. v. 42, n. 22, p. 2881–2895.

VAN OEFFELEN, M P; VOS, P G. Configurational effects on the enumeration of dots: counting by groups. Memory & cognition v. 10, n. 4, p. 396–404 , 1982.

WANG, J. J.-Y.; BENSMAIL, H. Unified framework for representing and ranking. Pattern Recognition, 2013. v. 47, p. 2293–2300.

WARD, J. H. J. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 1963. v. 58, p. 236–244.

WILSON, A. Women trailblazers: strategies to secure top ranking positions often dominated by men. [S.l.]: Capella University, 2009.

XIAO, H.; GUO, P. Iris image analysis based on affinity propagation algorithm. [S.l.]: Springer Berlin Heidelberg, 2009. p. 943–949.

YANG, P.; TAO, L. Perspective: ranking of the world’s top innovation management scholars and universities. Journal of Product Innovation Management, 2012. v. 29, n. 2, p. 319–331.

ZHANG, J. et al. Analysis of activity in fmri data using affinity propagation clustering. Computer methods in biomechanics and biomedical engineering, 2011. v. 14, n. 3, p. 271–281.

Downloads

Publicado

2018-06-29

Edição

Seção

Ciências Sociais Aplicadas