Statistical testing of attribute signficance is not possible in conjoint studies that use nonmetric algorithms to analyze respondet ranks of multiattibute product profiles. Procedures that test attribute significance at the aggregate (e.g., segment) level but maintain individual differences can be used (1) to confirm differences among benefits sought by hypothesized segments of respondents, (2) to eliminate insignificant attributes, reducing the time and cost of conjoint choice simulations, and (3) to design subsequent conjoint studies for the same product class. The author presents two tests of attribute significance in conjoint analysis. One is appropriate when consumer preferences for attribute levels can be ordered a priori and the other can be used when such ordering is not permissible, Each test permits different levels of an attribute to appear in a different number of product profiles. The proposed tests assess attribute significance across multiple respondents with idiosyncratic preferences. Becase they use rank order data, the testing procedures are not limited to a specific scaling algorithm. A Monte Carlo simulation indicates that eliminating insignificant attributes does not affect share-of-choices predictions for new product concepts, if the number of insignificant attributes in not very large. Ohterwise, the usual tradeoff between parsimony and predictive accuracy is necessary.
Kohli, Rajeev. "Assessing Attribute Significance in Conjoint Analysis: Nonparametric Tests and Validation." Journal of Marketing Research 25, no. 2 (May 1988): 123-33.
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