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A General Method for Constructing Efficient Choice Designs

Researchers have traditionally built choice designs using extensions of concepts from the general linear design literature. We show that a computerized search strategy can generate efficient choice designs with standard personal computers. This approach holds three important advantages over previous design strategies. First, it allows the incorporation of anticipated model parameters, thereby increasing design efficiency and considerably reducing the number of required choices. Second, complex choice designs can be easily generated, allowing researchers to conduct choice experiments that more closely mirror actual market conditions. Finally, researchers can explore model and design modifications and examine trade-offs between a design’s statistical benefits and its operational and behavioral costs.