Analysis of sensory check-all-that-apply (CATA) data which includes the evaluation of a single ideal product
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 on whether a sample is characterized in the same way as the ideal product; (iii) contextualizing results via a fragility index; (iv) Monte Carlo tests of independence to determine differences between real and ideal products; (v) the use of mixture of latent trait models with common slope parameters (MCLT) with ideal product data.
Here we focus on consumer clustering via MCLT, which has the potential of discovering consumer clusters around ideal products. In contrast to hedonic clusters that are based on real samples, these clusters around ideal products are based on imagined products, which might be characterized differently from any of the real samples evaluated. After clustering, further investigation can be done within clusters, e.g. using penalty-lift and other analyses. The analysis methods are illustrated using data from a whole wheat bread consumer study.
Castura, J.C., Tang, Y., Meyners, M. (2019). Analysis of sensory check-all-that-apply (CATA) data which includes the evaluation of a single ideal product. 13th Pangborn Sensory Science Symposium. 28 July-1 August. Edinburgh, UK. (Poster).