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MaxDiff, also known as best-worst scaling (BWS), is a choice-based measurement method. Rather than asking a respondent to report one favorite choice among several alternative profiles, MaxDiff asks a respondent to report both a best and a worst choice. The MaxDiff approach can provide more information about preferences than an approach where a respondent reports only a favorite choice. For background on MaxDiff studies, see Louviere et al. (2015). For background on choice modeling, see Louviere et al. (2015), Train (2009), and Rossi et al. (2005).
MaxDiff analysis uses the framework of random utility theory. A choice is assumed to have an underlying value, or utility, to respondents. The MaxDiff platform estimates these utilities. The MaxDiff platform also estimates the probabilities that a choice is preferred over other choices. This is done using conditional logistic regression. See McFadden (1974).
If there are not sufficient data to specify “By groups,” you can segment in JMP by clustering subjects using response data and the Save Gradients by Subject option. The option creates a new data table containing the average Hessian-scaled gradient on each parameter for each subject. For an example, see Example of Segmentation in Choice Models. For details about the gradient values, see Gradients in Choice Models.

Help created on 3/19/2020