Using choice experiments to estimate consumer valuation: the role of experimental design and attribute information loads
通过蒙特卡洛模拟，研究了选择实验中不同实验设计策略在有无先验信息时对消费者估值估计准确性的影响，并发现属性信息负荷会改变设计策略的表现。
Abstract With fixed dimensionality of choice experiments (CEs), previous simulation results show that D‐optimal design with correct a priori information generates more accurate valuation. In the absence of a priori information, random designs and designs incorporate attribute interactions result in more precise valuation estimates. In this article, Monte Carlo simulations demonstrate that the performances of different design strategies are affected by attribute information loads in CEs. Consumer valuation estimates in simulation settings vary with the number of attributes.