Before you unfold a fan, as shown in Figure 1,
you can see a compressed set of images stretched out on
a line from the center to the periphery. These images may
appear as nothing more than ordered blotches. You can
imagine what you might see when you unfold the fan, but
almost certainly the real image will confound your imagination.
Liking and other hedonic measures, expressed
as ordered means, are like the blotches on an unopened
fan. We will not know what the drivers of liking space
looks like and how the items are arranged in it until we
unfold the data.
In a previous paper, we reviewed some of the more common
methods for generating spatial maps of hedonic data
and considered the extent to which they are based on a
well-defined process. We concluded that the use of a model
based on the process that respondents use to generate
hedonic data, rather than relying on models that contain
no such process considerations, is important to obtaining
a meaningful interpretation of hedonic data. In addition,
by following a process-based approach, researchers can
evolve their thinking about what their data means by testing
and improving their models. One of our recommendations
was to consider ideal point ideas in hedonic models,
particularly those that incorporate uncertainty into the
location of items and ideals.
This technical report appears as:
Ennis, D. M. (2014). Unfolding. IFPress, 17(3) 3-4.
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