Data management and techniques for best–worst discrete choice experiments

Author:

Islam Farahnaz1,Thrasher James F.2,Xiao Feifei3,Moran Robert R.4,Hardin James W.4

Affiliation:

1. Department of Data Science–Biostatistics, Tempus Labs, Chicago, IL,

2. Department of Health Promotion, Education & Behavior, University of South Carolina, Columbia, SC,

3. Department of Biostatistics, University of Florida, Gainesville, FL,

4. Department of Epidemiology & Biostatistics, University of South Carolina, Columbia, SC,

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.

Publisher

SAGE Publications

Subject

Mathematics (miscellaneous)

Reference13 articles.

1. Stated Preference Methods Using R

2. Best–worst scaling: What it can do for health care research and how to do it

3. Two for the price of one: If moving beyond traditional single‐best discrete choice experiments, should we use best‐worst, best‐best or ranking for preference elicitation?

4. Kauermann G., Carroll R. J. 2000. The sandwich variance estimator: Efficiency properties and coverage probability of confidence intervals. Collaborative Research Center 386, Discussion Paper 189, Ludwig-Maximilians-Universität München. https://doi.org/10.5282/ubm/epub.1579.

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