Professor of Decision Sciences
Data from surveys often include errors, and such errors can have a serious effect on inferences about behavior or perceptions. In this paper a model is developed for making inferences based on dichotomous survey data with possible errors. A likelihood analysis reveals an identification problem, which can be avoided when a Bayesian approach is taken. The model is illustrated with purchase recall data from two previous studies, and the analysis shows that errors can have a significant impact on inferences about behavior. Ignoring such errors leads to point estimates that are unrealistically narrow. The effective amount of information in the survey data is reduced dramatically by the presence of errors. These results have important implications for the use and value of survey data in marketing and in many other areas.