The corresponding stacked bar diagram is then displayed. We compute the penalty and test if it is significantly different from 0.Īfter the display of the basic statistics and the correlation matrix for the liking scores and the JAR data, XLSTAT displays a table that shows for each JAR dimension the frequencies for the 5 levels (or 7 or 9 depending on the selected scale).The difference between the means of the 2 non-JAR categories and the JAR category is called mean drops. We then compute and compare the means of the liking scores for the three categories, to identify significant differences.We now have three levels: 'Not enough', 'JAR', and 'Too much'. The data of the JAR scale are aggregated: on one hand, categories 1 and 2 are grouped, and on the other hand categories 4 and 5 are grouped, which leads to a three point scale.Penalty analysis is subdivided into three phases: The penalty is the difference between the mean of the liking scores for the JAR category, and the mean of the scores for the other categories. The word penalty comes from the fact that we are looking for the characteristics which can penalize the consumer satisfaction for a given product. The method, based on multiple comparisons such as those used in ANOVA, consists in identifying, for each characteristic studied on the JAR scale, if the rankings on the JAR scale are related to significantly different results in the liking scores.įor example, if a chocolate is too bitter, does that significantly impact the liking scores? For example, for a chocolate bar, one can rate the bitterness, and for the comfort of the car, the sound volume of the engine. 1 corresponds not « Not enough at all », 2 to « Not enough », 3 to « JAR » (Just About Right), an ideal for the consumer, 4 to « Too much » and 5 to « Far too much ». These correspond to ratings ranging from 1 to 6 for one or more characteristics of the product of interest. Data collected on a JAR (Just About Right) scale.Preference data (or liking scores) that correspond to a global satisfaction index for a product (for example, liking scores on a 9 point scale for a chocolate bar), or for a characteristic of a product (for example, the comfort of a car rated from 1 to 10).Penalty analysis is a method used in sensory data analysis to identify potential directions for the improvement of products, on the basis of surveys performed on consumers or experts.

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