Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель развертывания× | Корреспондентский анализ× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Latent structure | Latent structure |
| Год появления≠ | 2005 | 1984 |
| Автор метода≠ | Clyde Coombs; Borg & Groenen | Jean-Paul Benzécri; Michael Greenacre |
| Тип≠ | Preference scaling via ideal-point representation | Exploratory multivariate technique for categorical data |
| Основополагающий источник≠ | Borg, I., & Groenen, P. J. F. (2005). Modern Multidimensional Scaling: Theory and Applications (2nd ed.). Springer. ISBN: 978-0-387-25150-9 | Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2 |
| Другие названия | Ideal Point Model, Preferential Choice Scaling, Coombs Unfolding, Katlanma Modeli | CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi |
| Связанные | 2 | 2 |
| Сводка≠ | The Unfolding Model is a geometric approach to preference analysis that represents both individuals and choice objects (stimuli) as points in a shared low-dimensional space. Originating with Clyde Coombs's foundational 1950 work on preferential choice and rigorously systematized by Borg and Groenen (2005), the model assumes each person prefers the stimulus closest to their personal ideal point, thereby 'unfolding' rank-order preference data into a joint spatial map. | 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. |
| ScholarGateНабор данных ↗ |
|
|