Comparison of a full and partial choice set design in a labeled discrete choice experiment

Abstract

AbstractLabeled discrete choice experiments (DCEs) commonly present all alternatives using a full choice set design (FCSD), which could impose a high cognitive burden on respondents. In the setting of employment preferences, this study explored if a partial choice set design (PCSD) reduced cognitive burden whilst maintaining convergent validity compared with a FCSD. Respondents' preferences between the two designs were investigated. In the experimental design, labeled utility functions were rewritten into a single generic utility function using label dummy variables to generate an efficient PCSD with 3 alternatives shown in each choice task (out of 6). The DCE was embedded in a nationwide survey of 790 Australian pharmacy degree holders where respondents were presented with both a block of FCSD and PCSD tasks in random order. The PCSD's impact on error variances was investigated using a heteroscedastic conditional logit model. The convergent validity of PCSD was based on the equality of willingness‐to‐forgo‐expected‐salary estimates from Willingness‐to‐pay‐space mixed logit models. A nested logit model was used combined with respondents' qualitative responses to understand respondents' design preferences. We show a promising future use of PCSD by providing evidence that PCSD can reduce cognitive burden while satisfying convergent validity compared to FCSD.