Multivariate mapping techniques are frequently and commonly used to visualize the large amount of data generated in sensory and consumer testing experiments. Since it is desirable to summarize data using as simple a model as possible, multidimensional solutions that capture the relevant information with fewer dimensions are usually prioritized. Moreover, it is less challenging to communicate results in two dimensions. Thus, many analyses are conducted and summarized in two dimensions and this approach is often appropriate. However, using only two dimensions can ignore important and relevant information contained in higher dimensions. In this report, we illustrate how an extra dimension is sometimes needed to capture relevant information when the multidimensional unfolding method, Landscape Segmentation Analysis® (LSA), is applied, so that the proper dimensionality is used to uncover the drivers of liking space.
Colleagues can download this technical report here:
Rousseau, B. (2013). How to Diagnose the Need for 3D Unfolding. IFPress, 16(3) 3-4.
How to Diagnose the Need for 3D Unfolding
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This technical report also appears in our book, Tools and Applications of Sensory and Consumer Science.