Attribute Selection for a Discrete Choice Experiment Incorporating a Best-Worst Scaling Survey

Objectives: Although literature exists on using qualitative methods to generate potential attributes for a discrete choice experiment (DCE), there is little on selecting which attributes to include. We present a case study in which a best-worst scaling case 1 (BWS-1) survey was used to guide attribute selection for a DCE. The case study's context was the decision making of professionals around the choice of augmentative and alternative communication (AAC) systems for children with limited natural speech. Methods: BWS-1 survey attributes were generated from literature reviews and focus groups. DCE attributes were selected from BWS-1 attributes. The selection criteria were: include mostly important attributes; create coherent descriptions of children and AAC systems; address the project's research aims; have an appropriate respondent burden. Attributes' importance was judged using BWS-1 relative importance scores. Results: The BWS-1 survey included 19 child and 18 AAC device/system attributes and was administered to N = 93 AAC professionals. Four child and five device/system attributes were selected for the DCE, administered to N = 155 AAC professionals. Conclusions: In this case study BWS-1 results were useful in DCE attribute selection. Four recommendations are made for future studies: define selection criteria for DCE attributes a priori; consider the impact participant's perspective will have on BWS-1 and DCE results; clearly define key terminology at the start of the study and refine it as the study progresses to reflect interim findings; BWS will be useful when there is little existing stated preference work on a topic and/or qualitative work is difficult.

Automated summary

Using best-worst scaling survey results to guide attribute selection in a discrete choice experiment, this study identified four recommendations for future research: define selection criteria for DCE attributes beforehand, consider participants' perspectives, clearly define key terminology, and utilize BWS when little existing stated preference work exists.