Comparar métodos

	Mapeo Perceptual y de Preferencias	Biplot: Representación Simultánea de Filas y Columnas en Datos Multivariados	Análisis de Correspondencias
Campo	Estadística	Estadística	Estadística
Familia	Latent structure	Latent structure	Latent structure
Año de origen	1979	1971	1984
Autor original	John Hauser & Frank Koppelman	Ruben Gabriel	Jean-Paul Benzécri; Michael Greenacre
Tipo	Multivariate spatial representation	Multivariate graphical display	Exploratory multivariate technique for categorical data
Fuente seminal	Hauser, J. R., & Koppelman, F. S. (1979). Alternative perceptual mapping techniques: Relative accuracy and usefulness. Journal of Marketing Research, 16(4), 495–506.	Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
Alias	Perceptual Mapping, Preference Mapping, Attribute-Based Mapping, Algısal Haritalama	Gabriel biplot, PCA biplot, JK biplot, Çift grafik	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
Relacionados	3	2	2
Resumen	Perceptual and preference mapping is a family of multivariate techniques that simultaneously positions competing objects—brands, products, or stimuli—and respondent preferences within a common low-dimensional space. Introduced systematically by Hauser and Koppelman (1979), the approach lets researchers visualize how consumers perceive attribute-level similarities among objects and which attributes drive individual or segment-level choice. It is widely used in market research, sensory science, and strategic positioning analysis.	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.	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.