Efficient Design and Analysis for a Selective Choice Process

Abstract

Variable selection is a decision heuristic that describes a selective choice process in which choices are made on the basis of 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 that arises from a selective choice process. The authors propose a new dual-objective compound design criterion that incorporates prior information for the joint purpose of efficiently estimating the effects of the active attributes and detecting the effects of attributes labeled as inactive that 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 in which individual designs were customized instantaneously using self-stated active-inactive attribute status.