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

Thao Thai, Michiel Bliemer, Gang Chen, Jean Spinks, Sonja de New and Emily Lancsar

Health Economics, 2023, vol. 32, issue 6, 1284-1304

Abstract: Labeled 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.

Date: 2023
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https://doi.org/10.1002/hec.4666

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Persistent link: https://EconPapers.repec.org/RePEc:wly:hlthec:v:32:y:2023:i:6:p:1284-1304

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