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The Institute for Perception |
| Developing Advanced Research Tools for Human Perceptual Measurement |
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JUST-ABOUT-RIGHT SCALES
A Thurstonian model to analyze JAR scale data is introduced. The notion of a 'norm' is established so that subsequent JAR results can be used to run appropriate statistical analyses.
PROBABLISTIC MULTIDIMENSIONAL SCALING
This method shows how the use of same-different judgments permits the generation of multivariate maps showing product similarities and differences. Its reliance on variances (probabilistic) and individual biases gives it a decisive advantage over the traditional (deterministic) multidimensional scaling approach.
MULTIVARIATE PREFERENCE MAPPING
A Thurstonian model using preference measurements (obtained from paired preference tests) is described. This model allows the generation of a multivariate map using an ideal point model. Drivers of LikingŪ attributes can then be determined through regression onto the map.
Drivers of LikingŪ FOR AN HOMOGENOUS SEGMENT
This methodology uses a combination of consumer hedonic ratings and descriptive information (consumer, expert, analytical,...) to generate a map where the ideal point is uncovered and its sensory characteristics identified.
A Thurstonian model for the A-Not A method is given. Designs and corresponding appropriate statistical techniques for handling replicated measures are outlined.
Drivers of LikingŪ FOR MULTIPLE SEGMENTS: Landscape Segmentation AnalysisŪ
Based on the similarity model, maps of individual ideal points are generated that show the structure of the consumers' liking space. High consumer densities indicate segmentation and descriptive information can be used to specify the sensory and analytical characteristics of optimum product(s).
COMPETITIVE STRATEGIES IN PRODUCT PORTFOLIO DESIGN
A map representing market structure can be generated based on the similarity model. Several strategies can then be used in order to estimate how many products should be placed on the market to optimize appeal to consumers, as well as their relative locations.
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