Handling missing data in consumer hedonic tests arising from direct scaling
In sensory evaluation, it may be necessary to design experiments that yield incomplete data sets. As such, sensory scientists will need to utilize statistical methods capable of handling data sets with missing values. This article demonstrates the advantages of a model-based imputation procedure that simultaneously accounts for heterogeneity while imputing.