Efficient Design and Analysis for a Selective Choice Process

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Variable selection is a decision heuristic that describes a selective choice process — choices are made based on only a subset of product attributes while the presence of other (inactive) attributes plays no active role in the decision. Within this context, the authors address two integrated topics that have received scant attention: the efficient design of choice experiments and the analysis of data arising from a selective choice process. The authors propose a new dual-objective compound design criterion that incorporates prior information for the joint purpose of efficient estimation of the effects of the active attributes and detection of the effects of attributes stated as inactive but may turn out to be active. The approach leverages self-stated auxiliary data as prior information both for individual-level customized design construction and in a heterogeneous variable selection model. The authors demonstrate the efficiency advantages of the approach relative to design benchmarks and highlight practical implications using both simulated data and actual data from a conjoint choice experiment where individual designs were customized on-the-fly using self-stated active-inactive attribute status.