विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| प्रत्यक्षणात्मक और वरीयता मानचित्रण× | Correspondence Analysis× | |
|---|---|---|
| क्षेत्र | सांख्यिकी | सांख्यिकी |
| परिवार | Latent structure | Latent structure |
| उद्भव वर्ष≠ | 1979 | 1984 |
| प्रवर्तक≠ | John Hauser & Frank Koppelman | Jean-Paul Benzécri; Michael Greenacre |
| प्रकार≠ | Multivariate spatial representation | Exploratory multivariate technique for categorical data |
| मौलिक स्रोत≠ | Hauser, J. R., & Koppelman, F. S. (1979). Alternative perceptual mapping techniques: Relative accuracy and usefulness. Journal of Marketing Research, 16(4), 495–506. DOI ↗ | Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2 |
| उपनाम | Perceptual Mapping, Preference Mapping, Attribute-Based Mapping, Algısal Haritalama | CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi |
| संबंधित≠ | 3 | 2 |
| सारांश≠ | 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. | 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. |
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