Derived Preference and Difference from Applicability Scoring
Taught by Dr. Daniel Ennis and Dr. Benoît Rousseau
Applicability scoring involves declaring that a statement applies or does not apply to an item. In survey research the method is called “forced choice CATA” and was shown to lead to deeper processing of the statements than regular CATA. In product testing, the method offers a convenient way of collecting and analyzing data on product differences for attributes that may not be easily expressed as ratings or in a 2-alternative format. Since the method is used sequentially, it can be used for more than two products. When the attribute is liking (the item scored is “I like this product”), the method allows the separation of like both from like neither which is not provided using a preference question with a no preference option. This capability therefore provides more information about the acceptability of both products than can be obtained from a preference test.
In this webinar, we explore the possibility of deriving preference information from applicability scoring and we compare it with data from typical preference testing with a no preference option. We will illustrate how applicability scores can be used to derive pairwise preference information. This approach provides greater discrimination sensitivity compared to direct paired preference and can also be applied to non-hedonic statements.
This webinar is intended for a general audience of sensory professionals and graduate students. No detailed technical knowledge is assumed.
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