This paper describes a straightforward procedure for assessing the predictive validity of joint-space models applied to the generation and evaluation of new concepts. The results indicate a marked deterioration in predictive power when using joint spaces derived from data on real objects to predict evaluations of new concepts. Conjoint analysis appears to offer a sounder approach to this aspect of product development.

The recently developed “pick-any” approach to data collection and analysis is described and illustrated by examples that support its face validity, reliability, and convergent validity with other multidimensional scaling techniques. Some solutions to problems that arise in applying the pick-any procedure are suggested, and potential extensions are proposed for use of the procedure in perceptual mapping applications.