Empirical surveys frequently make use of conjoint data records, where respondents can be split up into segments of different size. A lack of knowledge how to handle such random samples when using Hierarchical Bayes-regression gave cause to a more detailed observation of the preciseness of estimation results. The study on hand comprises a survey on the effects of disproportionate random samples on the calculation of part-worths in choice-based conjoint analyses. An explorative simulation using artificial data demonstrates that disproportionate segment sizes have mostly negative effects on the goodness of part-worth estimation when applying Hierarchical Bayes-regression. These effects vary depending on the degree of disproportion. This finding could be generated due to the introduction of a quality criterion designed to compare both true and estimated part-worths, which is applied on a flexible range of sample structure. Subsequent to the simulation, recommendations will be issued how to best handle disproportionate data samples.
CITATION STYLE
Fuchs, S., & Schwaiger, M. (2007). Disproportionate samples in hierarchical bayes CBC analysis. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 441–448). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-540-70981-7_50
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