Comparing Derived Importance Weights Across Attributes

In a recent paper, Currim, Weinberg, and Wittink (1981) noted that attribute importance weights inferred from conjoint analysis results may be influenced by the number of levels on which an attribute is defined. For a main-effects part-worth model, attribute importance is typically computed by taking the difference between the "best" and "worst" levels' estimated utilities. Currim, Weinberg, and Wittink argued that the minimum weight obtainable for a three-level attribute is higher than the rain hum weight for an attribute with only two levels, if forced tank order or equivalent preference judgments are collected. However, they recognized that the empirical finding could also be due to a "managerial" reason (attributes perceived as critical are defined on a greater number of levels) or a "psychological" phenomenon (respondents may pay more attention to attributes as the number of levels increases). In this paper, we investigate in more detail the possible relation between derived importances and number of attribute levels.