Effect of Experimental Design on Choice‐Based Conjoint Valuation Estimates
通过蒙特卡洛模拟，研究不同实验设计对支付意愿估计的影响，发现随机设计或包含属性交互的设计比仅主效应设计更精确，且大样本可弥补设计不足。
Abstract In this article, we investigate the effect of several commonly used experimental designs on willingness‐to‐pay in a Monte Carlo environment where true utility parameters are known. All experimental designs considered in this study generated unbiased valuation estimates. However, random designs or designs that explicitly incorporated attribute interactions generated more precise valuation estimates than main effects only designs. A key result of our analysis is that a large sample size can substitute for a poor experimental design. Overall, our results indicate that certain steps can be taken to achieve a manageably sized experimental design without sacrificing the credibility of welfare estimates.