Сравнение на методи

	Множествен анализ на съответствията (MCA)	Кореспондентен анализ
Област	Статистика	Статистика
Семейство	Latent structure	Latent structure
Година на възникване	2006	1984
Създател	Greenacre & Blasius	Jean-Paul Benzécri; Michael Greenacre
Тип	Multivariate exploratory ordination	Exploratory multivariate technique for categorical data
Основополагащ източник	Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1-58488-628-0	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
Други названия	MCA, Homogeneity Analysis, Multiple Nominal Component Analysis, Çoklu Uyum Analizi	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
Свързани	2	2
Резюме	Multiple Correspondence Analysis (MCA) is a multivariate ordination technique designed to explore and visualize associations among three or more categorical variables simultaneously. By mapping both observations and variable categories onto a shared low-dimensional space, MCA reveals hidden structure in nominal or ordinal survey data. The method was comprehensively systematized and extended by Michael Greenacre and Jorg Blasius in their 2006 edited volume, building on earlier geometric data analysis traditions developed in France by Jean-Paul Benzecri during the 1960s and 1970s.	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.