Abstract

The chapter deals with experimental designs used in conjoint analysis. The approach permutes the structure of the underlying fractional experimental design in order to create different sets of combinations. The resulting experimental designs, called isomorphic permuted experimental designs (IPEDs), create diverse sets of the variables and levels, producing an array of different designs that are statistically equivalent to each other. By creating an array of distinctive different individual designs (one design for each respondent), IPEDs reduce the bias caused by some possibly unusually strong performing combinations. IPEDs create the conditions for statistical analyses to detect and estimate interactions among variables. IPEDs also allow cluster analysis to identify pattern-based segments emerging from individual models of utilities. The chapter presents the theoretical foundation of the approach, formalizes the algorithmic implementation and shows a practical example its use.