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Problems arise in generalizing research results based on artificial objects to the case of real products. The few previous investigations of this issue may have obtained inflated assessments of predictive validity by focusing unrealistically on stimuli with isomorphic features and perceptions. Such isomorphism occurs when each feature is matched with a uniquely appropriate perceptual response. To handle the case of nonisomorphism, where features and perceptions do not show a one-to-one correspondence, we propose a method to assess the convergent validity between preference structures for artificial objects and real products via the use of shadow features and imputed preferences. In this approach, shadow features (analogous to the economist's “shadow prices”) are weighted by utilities estimated via conjoint analysis on artificial objects to derive imputed preferences toward real products. The correlation of imputed with actual preferences gauges the convergent validity between artificial and real preference structures. This estimate of convergent validity suggests the maximum predictive validity that one can reasonably expect in generalizing results based on artificial objects to the case of real products (for similar samples of subjects and sets of stimuli). We illustrate the proposed method in a study that collected data on perceptual and affective responses to real and artificial recordings of performances by male pop singers. This illustrative application suggests that, even with satisfactory results in separate analyses for artificial objects (AF = 0.81) and real products (AS = 0.82), one may find only moderate convergent validity (AA = 0.52) and disappointing predictive validity (r15 = 0.40) in applying utilities estimated on artificial objects to the prediction of preferences toward real products.

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