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.
Download this technical report here:
Not a Colleague? Click here to join for free!
This technical report also appears in our book, Tools and Applications of Sensory and Consumer Science.