dc.contributor.author Greenacre, Michael J.
dc.contributor.author Pardo Avellaneda, Rafael
dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
dc.date.accessioned 2012-07-11T02:07:36Z
dc.date.available 2012-07-11T02:07:36Z
dc.date.issued 2005-09-15T23:53:45Z
dc.identifier.uri http://hdl.handle.net/10230/721
dc.description.abstract In the analysis of multivariate categorical data, typically the analysis of questionnaire data, it is often advantageous, for substantive and technical reasons, to analyse a subset of response categories. In multiple correspondence analysis, where each category is coded as a column of an indicator matrix or row and column of Burt matrix, it is not correct to simply analyse the corresponding submatrix of data, since the whole geometric structure is different for the submatrix . A simple modification of the correspondence analysis algorithm allows the overall geometric structure of the complete data set to be retained while calculating the solution for the selected subset of points. This strategy is useful for analysing patterns of response amongst any subset of categories and relating these patterns to demographic factors, especially for studying patterns of particular responses such as missing and neutral responses. The methodology is illustrated using data from the International Social Survey Program on Family and Changing Gender Roles in 1994.
dc.language.iso eng
dc.rights.uri Aquest document està subjecte a una llicència d'ús de Creative Commons, amb la qual es permet copiar, distribuir i comunicar públicament l'obra sempre que se'n citin l'autor original, la universitat i el departament i no se'n faci cap ús comercial ni obra derivada, tal com queda estipulat en la llicència d'ús (http://creativecommons.org/licenses/by-nc-nd/2.5/es/)
dc.subject.other Categorical data, correspondence analysis, questionnaire survey
dc.title Multiple Correspondence Analysis of a Subset of Response Categories
dc.type info:eu-repo/semantics/workingPaper
dc.date.modified 2012-07-10T07:27:34Z

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