Mixture of Truthful–Untruthful Responses in Public Surveys

Abstract

Summary When sensitive issues are surveyed, collecting truthful data and obtaining reliable estimates of population parameters is a persistent problem in many fields of applied research mostly in sociological, economic, demographic, ecological and medical studies. In this context, and moving from the so‐called negative survey, we consider the problem of estimating the proportion of population units belonging to the categories of a sensitive variable when collected data are affected by measurement errors produced by untruthful responses. An extension of the negative survey approach is proposed herein in order to allow respondents to release a true response. The proposal rests on modelling the released data with a mixture of truthful and untruthful responses that allows researchers to obtain an estimate of the proportions as well as the probability of receiving the true response by implementing the EM‐algorithm. We describe the estimation procedure and carry out a simulation study to assess the performance of the EM estimates vis‐à‐vis certain benchmark values and the estimates obtained under the traditional data‐collection approach based on direct questioning that ignores the presence of misreporting due to untruthful responding. Simulation findings provide evidence on the accuracy of the estimates and permit us to appreciate the improvements that our approach can produce in public surveys, particularly in election opinion polls, when the hidden vote problem is present.

Private Support for Public Disaster Aid

“…The GSS "Sensitive Data Files" identify the location of each respondent by county: and it has incorporated methods to overcome bias from framing effects and subject nonresponses (Sterrett et al 2017). It is also regularly reviewed for potential sources of bias by researchers from a variety of disciplines on behalf of the NSF (Rasinski et al 1999;Christofides and Perri 2019). Using consistency across administrations to the same respondents spanning the period 2006-2014 and objective comparison data, Hout and Hastings (2016) found that the reliability of responses to more than 50% of the core questions in the GSS was greater than 70%.…”