Published: Jan 2009
| ||Format||Pages||Price|| |
|PDF ()||2||$25||  ADD TO CART|
|Complete Source PDF (11M)||2||$55||  ADD TO CART|
Many of the other techniques in this guideline treat the effect of JAR scales on the overall rating one scale at a time. Regression analysis allows the researcher to evaluate the joint effects of the scales levels on overall response. The stronger the relationship between a JAR scale and the overall response, the more important that “Just-Right” attribute is in explaining the liking attribute, even after controlling for the other attributes. The regression can be either non-parametric (ordinal) or parametric (ordinary regression) and the JAR scales can have either a linear or non-linear effect on the response. The examples below use linear regression because of ease of use and widespread availability in many statistical packages. The more general approaches require more statistical sophistication. Regression analysis can be done for the entire data set (all samples combined) or for each individual sample. Conducting the analysis on all samples combined gives a general overview of how the “Just Right” attributes work together to explain liking. This analysis can be conducted using either the individual respondent data or product mean scores.
Herskovic, Joseph E.
Sensory ConAgra Foods, Inc., Omaha, NE