เปรียบเทียบวิธี

	การวิเคราะห์การสอดคล้องพหุ (Multiple Correspondence Analysis - MCA)	ไบพลอต: การแสดงผลแถวและคอลัมน์ในข้อมูลหลายตัวแปรพร้อมกัน
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Latent structure	Latent structure
ปีกำเนิด	2006	1971
ผู้ริเริ่ม	Greenacre & Blasius	Ruben Gabriel
ประเภท	Multivariate exploratory ordination	Multivariate graphical display
แหล่งต้นตำรับ	Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1-58488-628-0	Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.
ชื่อเรียกอื่น	MCA, Homogeneity Analysis, Multiple Nominal Component Analysis, Çoklu Uyum Analizi	Gabriel biplot, PCA biplot, JK biplot, Çift grafik
ที่เกี่ยวข้อง	2	2
สรุป	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.	A biplot is a low-dimensional graphical representation of a multivariate data matrix that simultaneously displays both the observations (rows) and the variables (columns) as points or vectors in the same plot. Introduced by Ruben Gabriel in 1971, the technique decomposes the data matrix into a rank-2 approximation using singular value decomposition, allowing the approximate value of any data entry to be read as the inner product of the corresponding row and column markers.