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

Author

Listed:
  • Maren Hein

    (Clausthal University of Technology)

  • Peter Kurz

    (bms Marketing Research + Strategy)

  • Winfried J. Steiner

    (Clausthal University of Technology)

Abstract

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.

Suggested Citation

  • Maren Hein & Peter Kurz & Winfried J. Steiner, 2020. "Analyzing the capabilities of the HB logit model for choice-based conjoint analysis: a simulation study," Journal of Business Economics, Springer, vol. 90(1), pages 1-36, February.
    • Handle: RePEc:spr:jbecon:v:90:y:2020:i:1:d:10.1007_s11573-019-00927-4
    DOI: 10.1007/s11573-019-00927-4
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    More about this item

    Keywords

    Choice-based conjoint analysis; Hierarchical Bayes; Simulation; Sensitivity analysis;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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