Analyzing the capabilities of the HB logit model for choice-based conjoint analysis: a simulation study

The authors conduct an extensive simulation study to examine the capabilities of the Hierarchical Bayes (HB) logit model for choice-based conjoint (CBC) studies. The statistical performance of HB is evaluated under experimentally varying factor level settings using criteria for goodness-of-fit, parameter recovery and predictive accuracy. The results provide guidance to market researchers who are confronted with the problem that clients desire to include more and more attributes while keeping the choice task manageable. The results show that for simple CBC settings HB estimation proves to be quite robust. One of the main findings for simple CBC settings is that holding other factors at convenient levels far more attributes than previously suggested can be used in CBC studies. Further, sample size and/or the number of choice tasks per respondent can be noticeably reduced. However, for more complex CBC settings with an already high number of parameters (part-worths) but rather little information available from respondents, the HB model is starting to collapse if more than one of those factors (attributes, sample size, choice tasks) is set to an extreme level.

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