Sawtooth Software: The Survey Software of Choice

Covariates in HB Modeling

Hierarchical Bayes (HB) has been an enormously successful method for estimating individual-level preferences for CBC and MaxDiff data. It is quite robust, and the default settings in our CBC/HB and MaxDiff software make it easy for users to obtain excellent preference scores and robust models. Despite the benefits, many have expressed reservation regarding the software’s assumption that all respondents are drawn from a single, normal distribution, and the fact that every respondent is “shrunk” (to some degree or another) toward that global mean.

The earliest academic articles on HB for conjoint analysis actually involved a more sophisticated approach involving the use of classification variables (covariates) such as demographics, attitudes, and usage patterns. With covariates, respondents weren’t shrunk toward a single population center, but were shrunk toward the preferences of other respondents sharing their characteristics. So, for example, low income respondents’ preferences were influenced principally by other low income respondents; and high income respondents’ preferences were influenced principally by other high income respondents. It was a more streamlined (parsimonious) model than simply segmenting first by the covariate and running HB within each separate segment.

The key practical benefit of covariates in HB modeling is that the resulting part-worths (or MaxDiff scores) will typically demonstrate substantially greater differentiation among respondent groups. This is advantageous for any sort of segmentation work, including simple cross-tabulations. A more accurate and compelling story is portrayed when segments of respondents are not smoothed toward a single mean, but gravitate more toward their true, heterogeneous segment preferences. Covariates such as brand preference, budget thresholds, and preference for product characteristics (perhaps even from BYO questions) are the sorts of variables that bring new and useful information to HB modeling. Standard demographics such as gender and age typically are less predictive of preferences and are less valuable.

A recent white paper published on Sawtooth Software’s website in the Technical Paper’s Library (“Application of Covariates within Sawtooth Software’s CBC/HB Program: Theory and Practical Example”) demonstrates how to use covariates in v5 CBC/HB software. We illustrate how covariates increase the differences in importance scores between segments for a typical CBC study. The table below shows importance scores with generic CBC/HB vs. when using covariates. The final column “Spread” shows the absolute difference in importance scores across segments for each attribute.

Hierarchical Bayes (HB) has been an enormously successful method for estimating individual-level preferences for CBC and MaxDiff data. It is quite robust, and the default settings in our CBC/HB and MaxDiff software make it easy for users to obtain excellent preference scores and robust models. Despite the benefits, many have expressed reservation regarding the software’s assumption that all respondents are drawn from a single, normal distribution, and the fact that every respondent is “shrunk” (to some degree or another) toward that global mean.

The earliest academic articles on HB for conjoint analysis actually involved a more sophisticated approach involving the use of classification variables (covariates) such as demographics, attitudes, and usage patterns. With covariates, respondents weren’t shrunk toward a single population center, but were shrunk toward the preferences of other respondents sharing their characteristics. So, for example, low income respondents’ preferences were influenced principally by other low income respondents; and high income respondents’ preferences were influenced principally by other high income respondents. It was a more streamlined (parsimonious) model than simply segmenting first by the covariate and running HB within each separate segment.

The key practical benefit of covariates in HB modeling is that the resulting part-worths (or MaxDiff scores) will typically demonstrate substantially greater differentiation among respondent groups. This is advantageous for any sort of segmentation work, including simple cross-tabulations. A more accurate and compelling story is portrayed when segments of respondents are not smoothed toward a single mean, but gravitate more toward their true, heterogeneous segment preferences. Covariates such as brand preference, budget thresholds, and preference for product characteristics (perhaps even from BYO questions) are the sorts of variables that bring new and useful information to HB modeling. Standard demographics such as gender and age typically are less predictive of preferences and are less valuable.

A recent white paper published on Sawtooth Software’s website in the Technical Paper’s Library (“Application of Covariates within Sawtooth Software’s CBC/HB Program: Theory and Practical Example”) demonstrates how to use covariates in v5 CBC/HB software. We illustrate how covariates increase the differences in importance scores between segments for a typical CBC study. The table below shows importance scores with generic CBC/HB vs. when using covariates. The final column “Spread” shows the absolute difference in importance scores across segments for each attribute.