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Making Count-Based Claims from Sample Data (2019) | Ifpress.com

Unfolding

Ennis, D. M. (2014). IFPress, 17(3) 3-4.

Abstract:

 

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.

 

In 1950, Clyde Coombs proposed the idea that for certain types of variables that drive preference or liking, an ideal point may be useful to explain what has been called “single peaked preferences.” He reasoned that at lower or higher levels of the liking driver for an item, lower liking ratings may arise because the distance between the item and the ideal is larger. As the liking driver’s intensity increases, liking increases to a maximum or satiety point and then decreases, as shown in Figure 2. Although there are variables for which this idea would not apply – fuel efficiency in an automobile, for example – there are many sensory variables that are associated with foods, beverages, personal care, home care, air care and others, for which the response in Figure 2 to increasing intensity is highly applicable.

Figure 1. Compressed image of a folded fan offers only a glimpse of the unfolded image.

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