Check-all-that-apply (CATA) questionnaires have seen a widespread use recently. In this paper, we briefly review some of the existing approaches to analyze data obtained from such a study. Proposed extensions to these methods include a generalization of Cochran’s Q to test for product differences across all attributes, and a more informative penalty analysis.
Sensory professionals continue to face the challenge of quantifying and accounting for differences in individual panelist performance within their difference testing programs. In this presentation we discuss how recent developments in Thurstonian modeling can be used to track panelist performance over a series of (potentially non-replicated) difference tests within the same product category.
Principal component analysis (PCA) is frequently used to analyse sensory descriptive analysis data to better understand the multivariate sensory space. Consider that even well-trained descriptive sensory panelists might retain some distinctive characteristics, including a tendency to use somewhat different scale levels and ranges than other panelists. Panelists might also show other innate differences in sensitivity to particular attributes or differences
Check-All-That-Apply (CATA) questions are increasingly being incorporated into consumer tests because they provide a simple mechanism for consumers to communicate their perceptions of products being evaluated. We review existing and propose new approaches for analysing data obtained from such a study.
Check-all-that-apply (CATA) questions are increasing being used to investigate consumers’ product perceptions. We sought to evaluate a new process for validating CATA terms for consumer relevance prior to testing. The proposed method allows an opportunity for consumer feedback on a proposed CATA list without a more expensive pre-trial questionnaire involving real products.
Consumer research has advanced its business relevance through segmenting consumer populations into clusters based upon liking. Products designed to meet the expectations and desires of specific niche markets have demonstrated commercial success. The studies that are typically designed to reveal liking segments require a relatively large number of products and a large sample of consumers in a complete block design.
The complexity of sensory data can be overwhelming to the uninitiated, particularly when considering the nature of time-related testing (Time Intensity, Temporal Dominance of Sensations, Temporal Order of Sensations, Progressive Profiling, etc.). Researchers attempt to simplify the story told by these rich data sets through graphs, but the tools currently available limit the meaningful and approachable visualizations that can be