The method of paired comparison and ranking play an important role in the analysis of preference data. In this study, first we show how asymmetric multidimensional scaling allows to represent in a diagram the preference order that comes out in a paired comparison task concerning architectural facades. A ranking task involving the same stimuli and the same subject sample further enriched the preference analysis, because multidimensional unfolding applied to the ranking data matrix allows to detect the relationships between subjects and architectural facades. The results show that high curved facade is the most preferred, followed by the medium curved, angular and rectilinear ones. Rectilinear stimuli were always the least preferred and not angularity as expected.

Bove, G., Ruta, N., Mastandrea, S. (2019). Preference Analysis of Architectural Façades by Multidimensional Scaling and Unfolding. In Statistical Learning of Complex Data (pp.57-64). Cham : Springer [10.1007/978-3-030-21140-0_6].

Preference Analysis of Architectural Façades by Multidimensional Scaling and Unfolding

Bove G.
;
Mastandrea S.
2019

Abstract

The method of paired comparison and ranking play an important role in the analysis of preference data. In this study, first we show how asymmetric multidimensional scaling allows to represent in a diagram the preference order that comes out in a paired comparison task concerning architectural facades. A ranking task involving the same stimuli and the same subject sample further enriched the preference analysis, because multidimensional unfolding applied to the ranking data matrix allows to detect the relationships between subjects and architectural facades. The results show that high curved facade is the most preferred, followed by the medium curved, angular and rectilinear ones. Rectilinear stimuli were always the least preferred and not angularity as expected.
978-3-030-21139-4
Bove, G., Ruta, N., Mastandrea, S. (2019). Preference Analysis of Architectural Façades by Multidimensional Scaling and Unfolding. In Statistical Learning of Complex Data (pp.57-64). Cham : Springer [10.1007/978-3-030-21140-0_6].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/355926
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