2026-04-17T04:25:31Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/8982025-12-13T00:31:33Zcom_2072_6col_2072_452953

00925njm 22002777a 4500 dc Greenacre, Michael author 2017-07-26T10:51:19Z 2017-07-26T10:51:19Z 2001-03-01 2017-07-23T02:06:03Z We 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. correspondence analysis international social survey program (issp) matched matrices principal component analysis singular-value decomposition Statistics, Econometrics and Quantitative Methods Analysis of matched matrices