השוואת שיטות

	ניתוח התאמה	מיפוי רב-ממדי (MDS)
תחום	סטטיסטיקה	סטטיסטיקה
משפחה	Latent structure	Latent structure
שנת המקור	1984	1952–1964
הוגה השיטה	Jean-Paul Benzécri; Michael Greenacre	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
סוג	Exploratory multivariate technique for categorical data	Dimensionality reduction / visualization
מקור מכונן	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.
כינויים	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi	MDS, metric MDS, non-metric MDS, proximity scaling
קשורות	2	5
תקציר	Correspondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-Paul Benzécri in France during the 1960s–1970s and brought to an English-language audience by Michael Greenacre in 1984, CA decomposes the chi-square statistic of a cross-tabulation to produce a low-dimensional joint display — called a biplot — in which rows and columns are represented as points whose proximities reflect their associations.	Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data.