A Monte Carlo study of design-generating algorithms for the latent class mixed logit model

Abstract

We compare different procedures which generate $$D_{B}$$ -efficient designs for choice-based conjoint analysis using the latent class mixed logit model which captures latent consumer heterogeneity in a flexible way. These procedures are the Coordinate-Exchange algorithm, the Relabel-Swap-Cycle algorithm, and the remaining six combinations of the individual components of the latter. Halton draws and a minimum potential design for prior draws both of which reduce computation times serve to determine $$D_{B}$$ -errors of designs. We simulate choices for each set of generated designs and constellations which differ with respect to number of choice sets, number of clusters, within cluster heterogeneity, amount of stochastic error, relative cluster size and cluster similarity. Using these artificial choices we estimate parameters of the latent class mixed logit model in the next step. Designs are evaluated by TOPSIS scores which combine estimation accuracy and run time. ANOVA with TOPSIS scores as dependent variable shows that Relabel alone yields the best results of all procedures investigated. Coordinate-Exchange, Swap alone and the combination of Relabel and Swap turn out to be second best. Relabel also leads to much lower run times than the other procedures. We recommend to use Relabel and to avoid Cycle altogether because it performs worst.