Greenacre, MichaelUniversitat Pompeu Fabra. Departament d'Economia i Empresa2017-07-262017-07-262001-03-01Journal of Applied Statistics, 30, 10, (2004), pp. 1101-1113http://hdl.handle.net/10230/898We consider the joint visualization of two matrices which have common rows and columns, for example multivariate data observed at two time points or split accord-ing to a dichotomous variable. Methods of interest include principal components analysis for interval-scaled data, or correspondence analysis for frequency data or ratio-scaled variables on commensurate scales. A simple result in matrix algebra shows that by setting up the matrices in a particular block format, matrix sum and difference components can be visualized. The case when we have more than two matrices is also discussed and the methodology is applied to data from the International Social Survey Program.application/pdfengL'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commonsinfo:eu-repo/semantics/workingPapercorrespondence analysisinternational social survey program (issp)matched matricesprincipal component analysissingular-value decompositionStatistics, Econometrics and Quantitative Methodsinfo:eu-repo/semantics/openAccess