Using the List Experiment/Item Count Technique to Elicit Honest Answers to Sensitive Survey Questions

An increasing number of survey researchers have adopted an indirect questioning technique known as the list experiment (or the item count technique) in order to minimize bias due to dishonest or evasive survey responses. However, standard practice with the list experiment requires a large sample size, is not readily adaptable to regression or multivariate analysis, and provides only limited diagnostics. This work addresses all three of these issues, presenting design principles to minimize bias, reduce variance, and provide diagnostics (as well as providing sample size formulas for the planning of studies). Additionally, this work demonstrates that data from the list experiment can be used to diagnose some violations of the implicit behavioral assumptions and to estimate the probability that an individual holds the socially undesirable opinion/behavior (allowing multivariate analysis). This will be illustrated with a number of experiments designed to assess the true support for female and minority presidential candidates.