AN EMPIRICAL-COMPARISON OF VARIABLE STANDARDIZATION METHODS IN CLUSTER-ANALYSIS

It is common practice in marketing research to standardize the columns (to mean zero and unit standard deviation) of a persons by variables data matrix, prior to clustering the entities corresponding to the row s of that matrix. This practice is often followed even when the column s are all expressed in similar units, such as ratings on a 7-point, eq ual interval scale. This study examines six different ways of standard izing matrix columns and compares them with the null case of no column standardization. The analysis is replicated for ten large-scale data sets, comprising derived importances of conjoint-based attributes. Our findings indicate that the prevailing column standardization practice may be problematic for some kinds of data that marketing researchers use for segmentation. However, we also find that in the background dat a profiling step, results are reasonably robust to column standardizat ion method.