Dissimilarity profile analysis: a case study from Italian universities


Abstract


Dissimilarity profile analysis (DPA) is introduced as an explorative tool for object-oriented data analysis techniques that address the problem of latent dimension extraction by using proximity measures. Potentialities of DPA are shown within a case study from Italian universities, where undergraduate courses are examined with respect to students’ enrolment, career, and degree attainment.

DOI Code: 10.1285/i20705948v5n3p438

Keywords: dimensionality reduction, SMACOF multidimensional scaling

References


. Everitt, B.S., Rabe-Hesketh, S. (1997). The Analysis of Proximity Data. Kendall’s Library of Statistics, 4. London: Arnold.

. Jobson, J.D. (1992). Applied Multivariate Data Analysis. Volume II: Categorical and Multivariate Methods. New York: Springer-Verlag.

. Ministero dell’Università e della Ricerca - Ufficio di Statistica. Indagine sull’Istruzione Universitaria (2012). http://statistica.miur.it/normal.aspx?link=datiuniv

. Ministero dell’Università e della Ricerca (2011). L’Università in cifre 2009/2010. Appendice. http://statistica.miur.it/normal.aspx?link=pubblicazioni

. de Leeuw, J., Mair, P. (2009). Multidimensional scaling using majorization: SMACOF in R. Journal of Statistical Software, 31, 3, 1–30.

. R Development Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org


Full Text: PDF
Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.