Assessing the effectiveness of indirect questioning techniques by detecting liars

In many fields of applied research, mostly in sociological, economic, demographic and medical studies, misreporting due to untruthful responding represents a nonsampling error that frequently occurs especially when survey participants are presented with direct questions about sensitive, highly personal or embarrassing issues. Untruthful responses are likely to affect the overall quality of the collected data and flaw subsequent analyses, including the estimation of salient characteristics of the population under study such as the prevalence of people possessing a sensitive attribute. The problem may be mitigated by adopting indirect questioning techniques which guarantee privacy protection and enhance respondent cooperation. In this paper, making use of direct and indirect questions, we propose a procedure to detect the presence of liars in sensitive surveys which allows researchers to evaluate the impact of untruthful responses on the estimation of the prevalence of a sensitive attribute. We first introduce the theoretical framework, then apply the proposal to the Warner randomized response method, the unrelated question model, the item count technique, the crosswise model and the triangular model. To assess the effectiveness of the procedure, a simulation study is carried out. Finally, the presence and the amount of liars is discussed in two real studies concerning racism and workplace mobbing.