El análisis reticular de coincidencias
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Universidad de Salamanca
info
ISSN: 1139-5737
Year of publication: 2018
Issue: 39
Pages: 103-128
Type: Article
More publications in: Empiria: Revista de metodología de ciencias sociales
Abstract
The goal of this paper is the proposal of a new framework for the study of data structures based on the combination of several types of multivariate and social network analysis. By means of these techniques we obtain the most frequent events in a given set of scenarios as well as those events that tend to occur with them. In this respect we can define several coincidence gradients for the events under study. Ranging from zero to total coincidence and passing through statistically probable coincidences with predetermined confidence levels. The appearance structure of the set of events studied according to the selected coincidence gradient can be conveniently represented by a graph. In addition to its rationale, three free software programs are shown so that any user could apply this framework: coin, netcoin and webcoin. This type of procedure can be applied to the exploratory analysis of questionnaires, to the study of semantic networks, to the revision of databases and even to the comparison of different techniques of statistical analysis of interdependence. This is made posible by using factorial and classificatory methods and different methods for representing graphs based on attraction-repulsion forces, like those of Fruchterman-Reingold and Kamada-Kawai.
Bibliographic References
- AFIFI, A.A., S. MAY y V.A. CLARK (2012): Practical Multivariate Analysis, Boca Raton, CRC Press.
- AGRAWAL, R., T. IMILINSKI y A. SWAMI (1993): Mining Association Rules between Sets of Items in Large Databases. Ponencia presentada en SIGMOD ‘93 Proceedings of the 1993 ACM SIGMOD International Conference on Management of data, Washington.
- BERNARDO, J.M. y A.F.M. SMITH (2000): Bayesian Theory. Chichester, UK, Wiley.
- BOOL, G. (2003): An Investigation of the Laws of Thought, Amherst, NY, Prometheus book.
- BREIMAN, L. (2001): “Statistical Modeling: the Two Cultures”, Statistical Science, 16(3), 199-131.
- CARLIN, B.P. y T.A. LOUIS (2000): Bayes and Empirical Bayes Methods for Data Analysis, Boca Raton, FL, Chapman & Hall/CRC.
- CHERNOFF, H. (1973): “The Use of Faces to Represent Points in K-Dimensional Space Graphically”, Journal of the American Statistical Association, 68(342), 361-368.
- DAGAN, I., L. LEE y F. PEREIRA (1999): “Similarity-Based Models of Word Cooccurrence Probabilities”, Machine Learning, 34(1), 43-69.
- ESCOBAR, M. (1999): Análisis gráfico/exploratorio, Madrid, La Muralla.
- ESCOBAR, M. (2009): “Redes semánticas en textos periodísticos: propuestas técnicas para su representación”, Empiria, 17, pp. 13-39.
- ESCOBAR, M. (2015): “Studying coincidences with network analysis and other multivariate tools”, The Stata Journal, 15(4), pp. 1118-1156.
- ESCOBAR, M. y J. GÓMEZ ISLA (2015): “La expresión de la identidad a través de la imagen: los archivos fotográficos de Miguel de Unamuno y Joaquín Turina”, Revista Española de Investigaciones Sociológicas, 152, pp. 23-46.
- EVERITT, B. S. (2003): The Cambridge Dictionary of Statistics, Cambridge, Cambridge University Press.
- EVERITT, B.S. y G. DUNN (2001): Applied Multivariate Data Analysis, London, Arnold.
- FLACH, P. (2012): Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge, Cambridge University Press.
- FRUCHTERMAN, T.M.J. y E.M. REINGOLD (1991): “Graph Drawing by ForceDirected Placement”, Software: Practice and Experience, 21(11), 1129-1164.
- GABRIEL, K. R. (1971): “The Biplot Graphical Display of Matrices with Application to Principal Component Analysis”, Biometrika, 58, 453-467.
- GOWER, J.C. (1985): “Measures of similarity, dissimilarity and distance”, Encyclopedia of Statistical Sciences Vol. 5, New York, Wiley, pp. 397-405.
- GREEN, W. H. (2012): Econometric Analysis, Boston, Pearson.
- GRIFFITH, D. M., J.A. VEECH y C. MARSH (2016): “Cooccur: Probabilistic Species Co-occurrence Analysis in R”, Journal of Statistical Software, 69(2), 1-17.
- GUJARATI, D.N. y D.C. PORTER (2008): Basic Econometrics. Boston, McGraw Hill.
- HABERMAN, E. (1973): “The Analysis of Residuals in Cross-classified Tables”, Biometrics, 29, pp. 205-220.
- HASTIE, T., R. TIBSHIRANI y J. FRIEDMAN (2009): The Elements of Statistical Learning, New York, Springer.
- HUBÁLEK, Z. (1982): “Coefficients of Association and Similarity, Based on Binary (presence-absence) Data: An Evaluation”, Biological Reviews, 578, 669-689.
- JAMES, G. et al. (2013): An Introduction to Statistical Learning, New York, Springer.
- KAMADA, T. Y S. KAWAI (1989): “An Algorithm for Drawing General Undirected Graphs”, Information Processing Letters, 31(1), 7-15.
- KENDALL, M. G. (1975): Multivariate Analysis, London, Griffin.
- MATSUO, Y. Y M. ISHIZUKA (2002): “Keyword Extraction from a Document Using Word Co-Occurrence Statistical Information”, Transactions of the Japanese Society for Artificial Intelligence, 17(3), 217-223.
- MEDINA, I. et al. (2017): Análisis cualitativo comparado, Madrid, CIS.
- MYERSON, R. B. (1991): Game Theory: Analysis of Conflict, Cambridge (Mass.), Harvard University Press.
- RAGIN, C. C. (1987): The Comparative Method. Berkeley, University of California Press.
- RAGIN, C. C. (2000): Fuzzy-Set Social Science. Chicago, University of Chicago Press.
- SANDERSON, J. (2000): “Testing Ecological Patterns. A Well-known Algorithm from Computer Science Aids the Evaluation of Species Distributions”, American Scientist, 88, 332-339.
- SCOTTT, J. (2017): Social Network Analysis, Los Ángeles, Sage.
- QUINTANILLA, M. Á., ESCOBAR, M., ESCRIBANO, M., & SABBATINI, M. (2005). Cultura biotecnológica en España: Análisis e interpretación de datos, Madrid, Genoma España.
- WASSERMAN, S., y FAUST, K. (1994): Social Network Analysis, Cambridge, Cambridge University Press.
- WITTEN, I. H. y E. FRANK (2005): Data Mining. Practical Machine Learning Tools and Techniques, Amsterdam, Elsevier.
- WOOLDRIDGE, J.M. (2016): Introductory Econometrics: A Modern Approach, Australia, Cengage Learning.
- ZHAO, Y. (2013): R and Data Mining: Examples and Case Studies. San Diego, Elsevier.