In social sciences it is easy to carry out sensory experiments using say a J-point hedonic scale. One major problem with the J-point hedonic scale is that a conversion from the category scales to numeric scores might not be sensible because the panelists generally view increments on the hedonic scale as psychologically unequal. In the current problem several products are rated by a set of panelists on the J-point hedonic scale. One objective is to select the best subset of products and to assess the quality of the products by estimating the mean and standard deviation response for the selected products. A priori information about which subset is the best is incorporated, and a stochastic ordering is modified to select the best subset of the products. The method introduced in this article is sampling based, and it uses Monte Carlo integration with rejection sampling. The methodology is applied to select the best set of entrees in a military ration, and then to estimate the probability of at least a neutral response for the judged best entrees. A comparison is made with the method, which converts the category scales to numeric scores.
"A Bayesian Subset Analysis Of Sensory Evaluation Data,"
Journal of Modern Applied Statistical Methods:
2, Article 13.
Available at: http://digitalcommons.wayne.edu/jmasm/vol4/iss2/13