Analysis of sensory check-all-that-apply (CATA) data which includes the evaluation of a single ideal product

John Castura/ August 6, 2019/ Poster/ 0 comments

When evaluating samples in sensory tests, consumers are sometimes asked not only about real samples but also about imagined ideal products. Check-all-that-apply (CATA) questions are one way to understand consumers’ perceptions of products and their ideal product. We propose the following statistical analyses of consumer CATA data: (i) confidence intervals for head-to-head comparisons based on CATA data; (ii) panel (dis)agreement

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The Role of Imputation in Clustering BIB Data

Sara King/ July 28, 2014/ Tutorial/ 0 comments

Clustering consumer data reveals important information to help refine products for specific market segments. There are compelling reasons to use incomplete block designs to collect consumer data; however, this presents the challenge of dealing with missing data. The purpose of this workshop is to investigate the effect of different imputation techniques on the results of cluster analysis of balanced-incomplete-block (BIB)

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You know what you like, but what about everyone else? A case study on incomplete block segmentation of white-bread consumers

Sara King/ June 20, 2012/ Oral Presentation/ 0 comments

“One man’s meat is another man’s poison.” There will always be a wide range of consumer liking response across any product category. Cluster analysis can provide consumer segments based upon common liking that reflect underlying sensory preferences. To determine valid population segments requires a large sample of consumers. As the number of products tested by each consumer increases, experimental bias

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