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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Segmentation of BIB consumer liking of high-fatigue products: Sensory confirmation of statistical methods

Sara King/ July 26, 2009/ Poster/ 0 comments

Consumer testing of products which create sensory fatigue have a number of serious challenges. The effect of consumption of alcoholic beverages, extremely spicy foods, intense flavors or numbing ingredients limit the collection of complete block data to a small number of samples. If a study with a large number of samples is conducted by collecting consumer data over several days,

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Consumer segmentation of BIB liking data of 12 cabernet sauvignon wines: A case study

Sara King/ July 21, 2008/ Poster/ 0 comments

Consumer testing of beverage alcohol has a number of serious challenges. The effect of consumption of alcohol is a limiting factor in obtaining complete block data. Collecting consumer data over several days affects the quality of the consumer response. By the third day, most consumers are behaving like trained assessors, a conclusion that is supported by the decrease in first

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