Ambiguity surrounds any importance measure in cases in which predictor variables are correlated. However, a new measure is proposed that has attractive properties, such as providing individual contributions that are both non-negative and sum to R2. The new measure is compared with four other...

In lieu of Abstract, here is the first paragraph: Jackson provides a clear statement of a basic (and noncontroversial) limitation of all "importance" measures in multiple regression-namely, there is no unambiguous measure of importance in the case of correlated predictor variables. Darlington...

A simple allocation model is developed in which response is assumed to be linear in the logs of the control variables. It is then shown that the optimal allocation of a fixed total resource is proportional to the partial regression weights when the linear-in-logs model is fitted by least...

Choice-constrained conjoint analysis (CCCA) is a new method for metric conjoint analysis studies. It computes part-worth utility functions that account for “revealed preference” — those products a respondent actually selects in an independent choice situation. CCCA uses an iterative...

Increasingly, researchers are becoming interested in the relationship of part-worth functions, obtained from conjoint analysis, to other aspects of the respondents (e.g., their demographics, preferences for current brands, etc.). This paper describes a straight-forward procedure for determining...

Marketing Research and Modeling addresses state of the art developments including new techniques and methodologies by leading experts in marketing and marketing research. This work emphasizes new developments in Bayesian Decision Analysis, Multivariate Analysis, Multidimensional Scaling,...

Selection of "matched" areas for test marketing is an important undertaking if reliable comparisons among markets are to be made. This usually has been done on a rather arbitrary basis, largely because of the large number of market characteristics on which markets can be viewed as similar or...