Investigating Endogeneity Bias in Conjoint Models
نویسندگان
چکیده
The use of adaptive designs in conjoint analysis has been shown to lead to an endogeneity bias in part-worth estimates using sampling experiments. In this paper, we re-examine the endogeneity issue in light of the likelihood principle. The likelihood principle asserts that all relevant information in the data about model parameters is contained in the likelihood function. We show that adhering to the likelihood principle leads to analysis where the endogeneity bias becomes irrelevant. The likelihood principle is implicit to Bayesian analysis, and discussion is offered about the role of sampling experiments in Bayesian versus frequentist analysis.
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