Is the R-index method for eliciting preference measures from the 9-point hedonic scale fit for purpose?

Abstract

The traditional protocol for using the 9-point hedonic scale for measuring multiple preferences among a set of products, involves assigning numbers (1–9) to the scale’s verbal categories, which are erroneously treated as interval data. Means of these numbers for each product are treated by parametric statistics to yield an ordinal series of means, representing the degrees of liking as the effect sizes for the products in the set. Accordingly, for preference, products with a higher liking mean scores are deemed as being preferred to products with lower mean scores. This gives only the direction of the preferences among the products in the set but not their strengths. These have to be surmised from the means that were significantly different. Recently, an alternative protocol for the 9-point hedonic scale was developed, using the ranks of the scores on the 9-point scale. It used an R-index analysis of these ranks directly to yield preference probabilities for comparisons among products as its effect sizes. Besides appropriate effect sizes, attention was drawn to the absence of questionable statistical assumptions as well as superior ergonomics. Answering a call for further research and confirmation, this paper challenged these early results and was more demanding than the original. Using smaller numbers of products, the results of the R-index analysis shadowed those for the traditional analysis as expected. For these conditions, the only advantage for the R-index analysis was its ergonomics. As the number of products under assessment were increased, another advantage emerged; it was the elimination of artifactual ties. This was confirmed by the number of cases that were significant for the R-index analysis but not for the traditional analysis. All previous experiments were confirmed as were a set of conclusions, which were illustrated by feasible case studies. Together they indicated that the R-index analysis is definitely fit for purpose.