Differentiating the Contribution of Elevation, Scatter and Shape in Profile Similarity

The aims of this paper are: (1) to present a similarity generating function composed of elevation, scatter and shape parameters; (2) to describe linear models for integrating these parameters either for euclidean distance or vector-product association indices; and (3) to suggest a computational strategy based upon the Eckart-Young (1936) theorem that has certain advantages for minimizing the effects of measurement error in estimating profile similarity. Given these developments, the investigator may differentiate the independent contribution of each parameter to more global indices of resemblance. A brief example from the classification of psychopathology is discussed.