Using choice experiments to estimate consumer valuation: the role of experimental design and attribute information loads

Abstract

AbstractWith 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.