A COMPARISON OF VARIABLE REDUCTION TECHNIQUES IN AN ATTITUDINAL INVESTIGATION OF MEAT PRODUCTS

In many attitudinal investigations, particularly those involving free-choice profiling, a very large list of variables or features can emerge. Ordination using generalized Procrustes analysis provides a common base for comparing assessors, but the derived configurations are often high-dimensional and difficult to summarize. This problem can be rectified by selecting a small subset of the original set of variables. Methods of variable selection in principal component analysis can be adapted easily for such purposes, but there is no guarantee with these methods that overall data structure is preserved. A recently introduced variable selection procedure that does aim to preserve the data structure as much as possible would seem to be more appropriate. All methods are described and applied to a set of data arising from an attitudinal investigation of meat products. The results indicate that variable selection should be more widely encouraged.