B1490
Title: Replication of partial-profile choice designs: Factor permutation as an alternative to simple repetition
Authors: Heiko Grossmann - Otto-von-Guericke-University Magdeburg (Germany) [presenting]
Abstract: For design problems in linear models where a finite group of transformations acts transitively on a finite design space, it is well known that for convex optimality criteria which are invariant under the group, optimal approximate designs can be constructed by symmetrizing a given design. The underlying ideas can also be used to address some practical issues which arise in the area of partial-profile discrete choice experiments. In these experiments, there exist potentially many qualitative factors, of which only a subset is used in each question of a choice questionnaire. Certain exact designs for these experiments possess a high efficiency but are rigid in the sense that only relatively few of all possible subsets of the factors are used. When using such a design as the basis for a survey, where the number of potential respondents would allow several replications of the design, simply repeating the rigid base design does not seem to be advisable. Instead, we propose to use replications where each replication of the design uses a different permutation of the factors. For the rigid base designs we consider, this approach leads to replicated designs with better coverage of the design space and higher statistical efficiency. Moreover, the replicated designs appear to be robust against efficiency losses due to non-response. We illustrate the general ideas by referring to a design from an actual choice experiment.
