In a rank-order choice-based conjoint experiment, the respondent is asked to rank a number of alternatives of a number of choice sets. In this paper, we study the efficiency of those experiments and propose a D-optimality criterion for rank-order experiments to find designs yielding the most precise parameter estimators. For that purpose, an expression of the Fisher information matrix for the rank-ordered conditional logit model is derived which clearly shows how much additional information is provided by each extra ranking step. A simulation study shows that, besides the Bayesian D-optimal ranking design, the Bayesian D-optimal choice design is also an appropriate design for this type of experiments. Finally, it is shown that considerable improvements in estimation and prediction accuracy are obtained by including extra ranking steps in an experiment.

Bayesian optimal design, D-optimality, conjoint analysis, rank-order experiment, rank-ordered conditional logit model
dx.doi.org/10.1016/j.jspi.2011.01.019, hdl.handle.net/1765/23652
Journal of Statistical Planning and Inference
Erasmus Research Institute of Management

Vermeulen, B, Goos, P.P, & Vandebroek, M. (2011). Rank-order choice-based conjoint experiments: Efficiency and design. Journal of Statistical Planning and Inference, 141(8), 2519–2531. doi:10.1016/j.jspi.2011.01.019