The 9-point hedonic scale: Using R-Index Preference Measurement to compute effect size and eliminate artifactual ties

214 consumers used the verbal 9-point hedonic scale to assess 4 types of flavor coated peanuts and 4 types of flavored teas. They used the traditional ANOVA/LSD analysis to provide mean values derived from the 9-point hedonic scale along with measures of significant difference. However, these data did not provide effect sizes. They did not give direct measures of the strength of preference between the various products, which was the main interest. Accordingly, effect sizes were computed. For this, each consumer had also ranked their preferences as they made their ratings on the 9-point hedonic scale. From these, R-Index values were computed to provide the percentages of consumers, who preferred each product to every other product. These direct measures of effect size completed the analysis begun by the ANOVA analysis of the set of mean scores. Also, the measures were nonparametric and avoided issues of the validity of a parametric statistical analysis. They also avoided the problem with the traditional analysis when products in the same scale category are attributed the same scores, when they are not equally liked. Experiment 2, using 207 consumers indicated that this problem was only serious enough to reduce the power of the traditional analysis, compared with the R-Index Preference Measurement, when the number of products being tested approached a dozen say, for product optimization.