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.
Keywords: Conjoint analysis, experimental design, regression analysis, fractional
experimental designs, individual designs, dummy variable regression, incomplete
concepts, interactions, pattern-based segmentation.
