Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Atvēršanas modelis× | Atbilstības analīze× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 2005 | 1984 |
| Autors≠ | Clyde Coombs; Borg & Groenen | Jean-Paul Benzécri; Michael Greenacre |
| Tips≠ | Preference scaling via ideal-point representation | Exploratory multivariate technique for categorical data |
| Pirmavots≠ | 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 |
| Citi nosaukumi | Ideal Point Model, Preferential Choice Scaling, Coombs Unfolding, Katlanma Modeli | CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi |
| Saistītās | 2 | 2 |
| Kopsavilkums≠ | 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. |
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