Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study
比较了Hagerty和Kamakura提出的通过平均个体响应来提高联合分析预测准确性的方法，发现它们并不优于传统的个体层面联合分析。
Recently, both Hagerty and Kamakura have proposed insightful suggestions for improving the predictive accuracy of conjoint analysis via various types of averaging of individual responses. Hagerty uses Q-type factor analysis (i.e., optimal weighting) and Kamakura a hierarchical cluster analysis that optimizes predictive validity. Both approaches are compared with conventional conjoint and self-explicated utility models using real datasets. Neither the Hagerty nor the Kamakura suggestions lead to higher predictive validities than are obtained by conventional conjoint analysis applied to individual response data.