A typical challenge we face as researchers is to estimate a variety of weights (such as utility scores, coefficients, or attribute importances) using a limited amount of data. Hierarchical Bayes (HB) improves these estimates, leading to greater stability and validity. HB improves conjoint/choice analysis utilities. It may also be applied to MaxDiff scaling or general regression-based problems (where respondents have provided multiple cases or observations). Our HB software is the fastest in the world and you'll obtain excellent results using its robust default settings.
HB estimation is automatically integrated within our popular CBC, ACBC, MBC, and MaxDiff software systems.
We also offer the following HB software tools:
- CBC/HB (for CBC and other user-supplied discrete choice problems)
- HB-Reg (for general regression-based problems)
- ACA/HB (for Adaptive Conjoint Analysis)
- CVA/HB (for traditional full-profile conjoint)
You don't need to have used our tools for designing the questionnaire and collecting the data. Our CBC/HB and HB-Reg tools may be used for data you have collected under third-party applications and procedures.
For introductory articles on HB, please see: