Statistical Benefits of Choices from Subsets
研究了在品牌集合中，从子集而非全集进行选择能提供更多统计信息的原因，包括任务更易、协方差估计更准、份额自动平衡，对联合分析设计有指导意义。
Abstract Marketers often analyze multinomial choice from a set of branded products to learn about demand. Given a set of brands to study, the authors analyze three reasons why choices from strict subsets of the brands can contain more statistical information about demand than choices from all the brands in the study: First, making choices from smaller subsets is easier, so it is possible to use more choice tasks when the choice data come from a choice-based conjoint survey. Second, choices from subsets of brands better identify and more accurately estimate the covariance structure of unobserved utility shocks associated with brands. Third, subsets automatically balance the brand shares when some of the brands are less popular than others. The authors demonstrate these three benefits of subsets using a mixture of analytical results and numerical simulations and provide implications for the design of choice-based conjoint analyses. They find that the optimal subset size depends on the model, the number of b...