The basic (generic) hierarchical Bayes estimation that the first versions of Sawtooth Software's CBC/HB program supported assumed that respondents were drawn from a single, multivariate-normal distribution. All respondents were "shrunk" to some degree or another toward the population means. This article describes relaxing the assumption of a single normal population via covariates included in the upper-level model of the hierarchy. Covariates are segmentation variables that are predictive of respondent's choices. When used in HB, they allow shrinkage to respondent-specific locations in the distribution, depending on the characteristics of the covariates. The results can lead to modest improvement in predictive accuracy, but substantial improvement in terms of differences between segments of respondents on the means. This is of substantial benefit to segmentation research, where the generic model could obscure the true differences between respondents.
Application of Covariates within Sawtooth Software’s CBC/HB Program: Theory and Practical Example (2009)
- Category: Hierarchical Bayes Estimation