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ACA/HB Module



ACA/HB is a system for hierarchical Bayes estimation of individual level utilities using data collected with ACA. In the last few years, leading academics have developed a new technique for estimating conjoint utilities called Hierarchical Bayes (HB). HB significantly improves conjoint analysis results. While improvements are most dramatic for traditional conjoint (CVA) and choice-based methods (CBC), ACA also benefits from HB estimation.


  1. The ACA/HB module improves the quality of each individual's utility estimates by "borrowing" information from other individuals. This translates to more accurate predictions of both individual choices and share estimations.
  2. ACA has been criticized because of potential scale incompatibilities between the self-explicated priors and conjoint pairs segments of the interview. ACA/HB provides a more theoretically sound way of combining data from these two sections of the interview. Not only is the technique more defensible, but the results are generally better.
  3. ACA/HB does a better job of estimating utilities for the levels not taken forward into pairs when using "Most Likelies" and "Unacceptables."

As an additional benefit, your ACA surveys can now be shorter. Using ACA/HB allows you to drop the "Importance" questions. For additional information, please read our technical paper, The "Importance" Question in ACA: Can It Be Omitted? (2005).

System Requirements

ACA/HB requires Windows XP or later.

Lighthouse Studio

Lighthouse Studio is our flagship software for producing and analyzing online and offline surveys. It contains modules for general interviewing, choice-based conjoint, adaptive choice-based conjoint, adaptive choice analysis, choice-value analysis, and maxdiff exercises.

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