Analysis is typically conducted, as with DCEs more generally, assuming that respondents makes choices according to a random utility model (RUM).
Louviere attributes the idea to the early work of Anthony A. J. Marley in his PhD thesis, who together with Duncan Luce in the 1960s produced much of the ground-breaking research in mathematical psychology and psychophysics to axiomatise utility theory.
Marley had encountered problems axiomatising certain types of ranking data and speculated in the discussion of his thesis that examination of the "inferior" and "superior" items in a list might be a fruitful topic for future research.
The three authors have (individually and together) already published many of the key academic peer-reviewed articles describing BWS theory,[2][3][4] practice,[5][6] and a number of applications in health,[5] social care,[7] marketing,[6] transport, voting,[8] and environmental economics.
[14] There are two different purposes of BWS – as a method of data collection, and/or as a theory of how people make choices when confronted with three or more items.
To assume that respondents do evaluate all possible pairs is a strong assumption and in 14 years of presentations, the three co-authors have virtually never found a course or conference participant who admitted to using this method to decide their best and worst choices.
Case 2 has predominated in health and the items are the attribute levels describing a single profile of the type familiar to choice modellers.
Case 2 has proved to be powerful in eliciting preferences among vulnerable groups, such as the elderly,[20][21] older carers,[22] and children,[23] who find conventional multi-profile discrete choice experiments difficult.
Case 2 BWS studies can use Orthogonal Main Effects Plans (OMEPs) or efficient designs, although the former has predominated to date.
In 2003 at the ESOMAR Latin America Conference in Punta del Este, Uruguay, Steve and his co-author, Dr. Leopldo Neira, compared BWS results to those obtained by rating scale methods.
At the 2003 Sawtooth Software Conference, Steve Cohen's paper, "Maximum Difference Scaling: Improved Measures of Importance and Preference for Segmentation," was selected as Best Presentation.
Later in 2004, Cohen and Orme won the David K. Hardin Award from the AMA for their paper which was published in Marketing Research Magazine entitled, "What's your preference?
This prompted the collaboration with Flynn and ultimately the link-up with Marley, who had begun working with Louviere independently to prove the properties of BWS estimators.
The book contains an introductory chapter summarising the history of BWS and the three cases, together with why the respondent must think whether (s)he wishes to use it to understand theory (processes) of decision-making and/or merely to collect data in a systematic way.
[citation needed] The basic steps in conducting all types of BWS study are: Estimation of the utility function is performed using any of a variety of methods.
Respondents find these ratings scales very easy but they do tend to deliver results which indicate that everything is "quite important", making the data not especially actionable.
[citation needed] BWS on the other hand forces respondents to make choices between options, while still delivering rankings showing the relative importance of the items being rated.