Disproportionate Samples in Hierarchical Bayes CBC Analysis

Abstract

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.