Just-about-right and ideal scaling provide similar insights into the influence of sensory attributes on liking

Abstract

Just-about-right (JAR) scaling is criticized for measuring attribute intensity and acceptability simultaneously. Using JAR scaling, an attribute is evaluated for its appropriateness relative to one’s hypothetical ideal level that is pre-defined at the middle of a continuum. Alternatively, ideal scaling measures these two constructs separately. Ideal scaling allows participants to rate their ideal freely on the scale (i.e., without assuming the “Too Little” and “Too Much” regions are equal in size). We hypothesized that constraining participants’ ideal to the center point, as is done in the JAR scale, may cause a scaling bias and, thereby, influence the magnitude of “Too Little” and “Too Much”. Furthermore, we hypothesized that the magnitude of “Too Little” and “Too Much” would influence liking to different extents. Coffee-flavored dairy beverages (n=20) were formulated using a fractional, constrained-mixture design that varied the ratio of water, milk, coffee extract, and sucrose. Participants tasted 4 of 20 prototypes that were served in a monadic sequential order using a balanced incomplete block design. Data reported here are for participants randomly assigned to one of two research conditions: ideal scaling (n=129) or JAR scaling (n=132). For both conditions, participants rated overall liking using a 9-point hedonic scale. Four attributes (sweetness, milk flavor, coffee flavor and thickness) were evaluated. The reliability of an individual participant’s ideal rating for an attribute was evaluated using the standard deviation of their ideal ratings (n=4). All data from a participant were eliminated from further analyses when his/her standard deviation of the ideal ratings for any of the four rated attributes was identified as a statistical outlier. This resulted in the elimination of 15 of 129 (12 %) of participants in the ideal scaling group. Multiple linear regression was employed to model liking as a function of “Too Little” or “Too Much” attribute intensities. Mean ideal ratings (averaged across participants) for all four attributes were significantly different from the central point of the scale (i.e., 50). However, Coffee flavor was the only attribute for which the mean ideal rating (57.2) fell outside the central 10% (45.0–55.0). Even so, the magnitude of “Too Little” and “Too Much” was not affected by the scaling method. The influence of the magnitude of “Too Little” and “Too Much” on liking was asymmetrical. Both scaling methods agreed that sweetness and coffee flavor were the main sensory attributes affecting liking. Overall, JAR scaling and ideal scaling were comparable for measuring “Too Little” and “Too Much”, and identifying the main factors affecting liking.

Vocal Features of Song and Speech: Insights from Schoenberg's Pierrot Lunaire

“…If a feature was perceived as “not right,” then the rater could choose the direction in which it deviated from JAR. In the case of the perceived “average pitch” of a voice, it could be “too high” and “much too high” or “too low” and “much too low.” The rating scale was adapted from studies in consumer product testing (Popper et al, 2004; Popper and Kroll, 2005) and has been used to evaluate piano performances (Kroger and Margulis, 2017). As can be seen in Table 2, most items were bipolar (e.g., the average pitch can be high or low), the items on noisiness and modulations of the voice were unipolar, e.g., the vibrato could be rated as “just about right” or not.…”

Sugar reduction of skim chocolate milk and viability of alternative sweetening through lactose hydrolysis

“…Thompson et al (2007) also reported that in focus groups, adult participants indicated that the commercial chocolate milks were often too sweet. Similarly, Li et al (2014) reported that liking of coffee-flavored beverages was penalized by "too much" sweetness, but not as frequently as "too little" sweetness. These studies also suggest that the sugar content of most commercial chocolate milks could be reduced without affecting liking.…”

Maximizing overall liking results in a superior product to minimizing deviations from ideal ratings: An optimization case study with coffee-flavored milk

“…The low R-squared in the Ideal_Delta model might be due to additional noise introduced by the multiple rating steps required in ideal scaling (rating of intensity, followed by rating of ideal) (Li et al, 2014b). In contrast, the variance explained by JAR_Delta model (72%) was similar to the JAR_liking model (72%).…”

“…In creating Ideal_Delta and JAR_Delta , “Too Little” and “Too Much” were weighted equally, as were each of the four attributes. However, prior work shows that too little or too much of an attribute can influence liking differently (Li et al, 2014b; Xiong & Meullenet, 2006). In a previous paper we demonstrated that the influence of the four attributes assessed herein on liking were not the same (Li et al, 2014b).…”

“…However, prior work shows that too little or too much of an attribute can influence liking differently (Li et al, 2014b; Xiong & Meullenet, 2006). In a previous paper we demonstrated that the influence of the four attributes assessed herein on liking were not the same (Li et al, 2014b). Given Figure 1, one could also surmise that the Ideal_Delta and JAR_Delta should be calculated as the sum of the squared deltas, but this resulted in exactly the same prediction for the optimal formulation (data not shown).…”