A Comparison of Criteria to Design Efficient Choice Experiments

Abstract

To date, no attempt has been made to design efficient choice experiments by means of the G-and V-optimality criteria. These criteria are known to make precise response predictions, which is exactly what choice experiments aim to do. In this article, the authors elaborate on the G-and V-optimality criteria for the multinomial logit model and compare their prediction performances with those of the D-and A-optimality criteria. They make use of Bayesian design methods that integrate the optimality criteria over a prior distribution of likely parameter values. They employ a modified Fedorov algorithm to generate the optimal choice designs. They also discuss other aspects of the designs, such as level overlap, utility balance, estimation performance, and computational effectiveness.

Experimental design influences on stated choice outputs: An empirical study in air travel choice

“…Researchers need not assume precise prior parameter values but rather, may assume prior parameter distributions that are expected to contain within their range, the true population parameter. In taking this approach, the resulting Bayesian efficient design is then optimised over a range of possible parameter values, without the analyst having to know the precise population value in advance (see e.g., Sandor andWedel 2001 andKessels et al 2006). For the current study, the two D-efficient designs were generated using parameter priors obtained from a small pilot study consisting of 36 respondents yielding a total of 216 choice observations.…”

“…Beginning with Fowkes et al (1993), Bunch et al (1996) and Huber and Zwerina (1996), new experimental design theories began to emerge in the early to mid 1990s specifically for the generation of experiments for the non-linear discrete choice models often associated with DCE. Further theoretical advancements in the field of experimental designs have been made since including, but not limited to work undertaken by Bliemer et al (2009), Burgess and Street (2003), Ferrini and Scarpa (2006), Kanninen (2002), Kessels et al (2006), Sándor and Wedel (2001, 2002, Rose and Bliemer (2008), Scarpa and Rose (2008), Street and Burgess (2004) and Toner et al (1999). The types of designs constructed by these researchers, typically referred to as efficient designs, each have the common goal of seeking to minimize the determinant of the asymptotic variance-covariance (AVC) matrix of models estimated on data collected using the designs, which essentially minimizes the standard errors.…”

The Importance of Attribute Interactions in Conjoint Choice Design and Modeling

“…This is different from optimal designs for linear regression models where the information provided by the design does not depend on the model parameters. Kessels et al (2005) summarize three approaches for coping with the problem of the optimal designs' dependence on the unknown parameters. The first approach is to use zero prior parameter values.…”

“…In most of the literature on the optimal design of conjoint choice experiments, researchers focus on optimal main-effects designs for the MNL logit model, and neglect interactions between attributes (e.g., Bunch et al 1996;Kessels et al 2005;Sandor and Wedel 2001).…”

Optimal Paired Choice Block Designs

“…D-optimal designs have been obtained theoretically under the utility-neutral setup, for example, see Graßhoff et al (2003), Graßhoff et al (2004), Street and Burgess (2007), Street and Burgess (2012), Demirkale, Donovan, and Street (2013), Bush (2014), Großmann and Schwabe (2015) and Singh, Chai, and Das (2015). In contrast, in the locally-optimal and the Bayesian approach, D-optimal designs have been obtained using computer algorithms (see, Huber and Zwerina (1996), Sándor and Wedel (2001), Sándor and Wedel (2002), Sándor and Wedel (2005), Kessels, Goos, and Vandebroek (2006), , , Kessels et al (2009), Yu, Goos, and Vandebroek (2009)). In this paper, we follow the utility-neutral approach.…”