Data management and techniques for best–worst discrete choice experiments
Farahnaz Islam (),
James F. Thrasher (),
Feifei Xiao (),
Robert R. Moran () and
James W. Hardin ()
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Farahnaz Islam: Tempus Labs
James F. Thrasher: University of South Carolina
Feifei Xiao: University of Florida
Robert R. Moran: University of South Carolina
James W. Hardin: University of South Carolina
Stata Journal, 2023, vol. 23, issue 4, 1020-1044
Abstract:
In this article, we present software that is suitable for use with Stata’s choice modeling suite of commands, which begin with cm. Within the context of choice models, we focus on best–worst data. In such data, respondents are presented a set of choices and are required to select a best and a worst choice from among the alternatives. Optionally, respondents may indicate an opt-out choice, in which no best or worst choice exists in the choice set. Such data are simplified versions of experiments in which respondents rank all the choices. Once best– worst data are collected, there are specific types of data expansions that analysts use to take advantage of both explicit and implicit information. The commands described in this article support data expansion and model estimation.
Keywords: cm_expand; cm_bwpairs; cm_bwsumm; cm_bestworst; choice models; postestimation; attributes; discrete choice experiments; best–worst; maxdiff choice models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:23:y:2023:i:4:p:1020-1044
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DOI: 10.1177/1536867X231212437
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