Improving studies of sensitive topics using prior evidence: an informative Bayesian approach for list experiments

Abstract Estimates of sensitive questions from list experiments are often much less precise than desired. We address this well-known inefficiency problem by presenting an informative Bayesian approach that combines indirect measures with prior information. Specifying informed priors amounts to a principled combination of information that increases the efficiency of model estimates. This framework generalizes a range of different modeling approaches for list experiments, such as the inclusion of direct items, auxiliary information, the double list experiment, and the combination of list experiments with other indirect questioning techniques. As we demonstrate in real-world examples from political science, the informative Bayesian approach not only improves the utility but also changes the substantive implications drawn from list experiments.