Analysis and Thurstonian Scaling of Applicability Scores

Check-all-that-apply (CATA) lists are commonly used in both survey research and sensory science. A related technique, referred to in this paper as applicability scoring, requires respondents to respond positively or negatively to the item of interest – applicability scoring differs from CATA as CATA only requires a check when the item applies to the object being scored. Both hypothesis testing and scale estimation for applicability scoring of sequentially tested products are considered in this paper. For the former, we demonstrate the use of McNemar's test and for the latter, we present a Thurstonian model. Using applicability scores for scale estimation is important because a connection can then be made to other methods through a common framework, allowing cross-comparison and validation. In addition, applicability scoring provides a sensitive method for assessing product differences and may be particularly useful when an attribute cannot be conveniently expressed in a rating or 2-alternative forced choice (2-AFC) format. Practical Applications The first use of applicability scoring in survey research was by Sudman and Bradbury and its first use in sensory science was by Loh and Ennis. In survey research, the method is more appropriate than CATA lists in telephone surveys and may lead to deeper processing of the items. In sensory science, 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-AFC 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.