The most common CBC task is to ask respondents to select their one highest-utility product concept (Discrete Choice). This is a robust approach and has been the standard for three decades of CBC research.
Best-Worst Choice asks respondents to select both the highest and the lowest utility product concepts within each task. Some researchers have argued in favor of this format, for at least two reasons:
1) It is efficient to ask respondents to give us more information per choice task. It takes only a little bit more time to indicate both best and worst concepts rather than just the one best or the one worst concept.
2) In some kinds of research contexts (such as healthcare outcomes for the treatment of cancer), the respondent is perhaps even more interested in avoiding exceptionally bad outcomes than achieving exceptionally good ones. Thus, asking the respondent to select the worst concept in addition to the best would seem useful.
However, there are some drawbacks to the Best-Worst format.
1) It isn't well known whether the additional time spent asking respondents to indicate worst concepts in each task wouldn't be better spent asking respondents to answer a few more best-only choice tasks. For example, which would be better: 10 Best-Worst tasks or 13 Best-Only tasks? Both approaches would likely take about the same amount of time to complete.
2) The magnitude of the response error made in Best choices is usually different from Worst choices. Also, academics (Allenby et al.) have recently raised the issue that some respondents orient themselves by choosing the worst concept first and the best concept last. And, depending on the order of choice (best first or worst first) the errors involved in making the judgments differ, as well as the context in terms of number of alternatives per set. Allenby et al. have raised concerns regarding whether it is appropriate to treat the choices equally (in terms of equal error magnitude, or scale factor, and task context) when estimating part-worth utilities.
For now, we don't have a strong opinion on the matter, but offer the best-worst choice option because many researchers are interested in using this approach for CBC studies. We hope to see more research on the suitability of the approach vs. standard discrete choice.
For utility estimation, the best and worst tasks are formatted as independent choice tasks. The design matrix for best tasks is coded exactly as in standard Discrete Choice (best-only format). The design matrix is simply multiplied by -1 for worst tasks. (This is the same procedure as is used for our MaxDiff utility estimation.)
Constant Sum/Chip Allocation
Constant Sum/Chip Allocation allows respondents to indicate how many of each concept they would choose. Often, researchers ask respondents to allocate points (chips) across the concepts that sum to a fixed amount, such as 10. But, it is possible to relax that requirement, so the points do not sum to some constant. (In either case, the HB and Latent Class estimation routines by default percentage the chips to 100% within each choice task, so there isn't a formal recognition that a respondent may have allocated 20 chips in task 1, but 13 chips in task 2.)
Constant Sum has the benefit of collecting a lot more statistical information for each choice task. But, it is well known that respondents often struggle with providing reliable chip-allocation data. So, we recommend caution and pretesting before using this option.