What Can We Learn with Statistical Truth Serum?

Due to the inherent sensitivity of many survey questions, a number of researchers have adopted an indirect questioning technique known as the list experiment (or the item-count technique) in order to reduce dishonest or evasive responses. however, standard practice with the list experiment requires a large sample size, utilizes only a difference-inmeans estimator, and does not provide a measure of the sensitive item for each respondent. this paper addresses all of these issues. first, the paper presents design principles for the standard list experiment (and the double list experiment) for the reduction of bias and variance as well as providing sample-size formulas for the planning of studies. second, this paper proves that a respondent-level probabilistic measure for the sensitive item can be derived. this provides a basis for diagnostics, improved estimation, and regression analysis. the techniques in this paper are illustrated with a list experiment from the 2008-2009 american national election studies (anes) panel study and an adaptation of this experiment. introduction the sensitivity of some topics presents measurement problems for many areas of social science research. sex, drugs, crime, religion, race, and politics are all inherently sensitive subjects. furthermore, the inaccuracy of measurement due to the use of sensitive survey questions is not trivial. in a canonical example,