| Abstract: | In sensory data sets, an important source of differences between panelists is in their use of the measurement scale. These differences can be summarized in differences in location , the overall level, and differences in dispersion , the range of the scale used. This paper discusses a method of correcting for these differences by jointly modeling location and dispersion using a see-saw algorithm. This approach is also applicable when scores are not normally distributed and when there is a (nonlinear) relationship between the dispersion and the location. The approach is illustrated with an example for flavor data of freeze-dried and hot-air dried peppers. |
