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This paper discusses a survey where some respondents were asked sensitive questions directly and others were asked the same questions using randomized response. The use of randomized response was a factor in a 2 × 2 factorial design and dice were used to perform the randomization. First, the paper shows that the perturbation due to the dice can be described using the concept of misclassification and known conditional misclassification probabilities. Second, the paper formulates the likelihood for loglinear models and shows that latent class software can be used to analyse the data. An example including a power analysis is discussed.

Many issues of interest to social scientists and policy makers are of a sensitive nature in the sense that they are intrusive, stigmatizing, or incriminating to the respondent. This results in refusals to cooperate or evasive cooperation in studies using self-reports. In a seminal article, Warner (1965) proposed to curb this problem by generating an artificial variability in responses to inoculate the individual meaning of answers to sensitive questions. This procedure was further developed and extended and came to be known as the randomized response (RR) technique. Here, the authors propose a unified treatment for eliciting sensitive binary as well as quantitative information with RR based on a model where the inoculating elements are provided for by the randomization device. The procedure is simple and the authors will argue that its implementation in a computer-assisted setting may have superior practical capabilities.

This article discusses two meta-analyses on randomized response technique (RRT) studies, the first on 6 individual validation studies and the second on 32 comparative studies. The meta-analyses focus on the performance of RRTs compared to conventional question-and-answer methods. The authors use the percentage of incorrect answers as effect size for the individual validation studies and the standardized difference score (d-probit) as effect size for the comparative studies. Results indicate that compared to other methods, randomized response designs result in more valid data. For the individual validation studies, the mean percentage of incorrect answers for the RRT condition is .38; for the other conditions, it is .49. The more sensitive the topic under investigation, the higher the validity of RRT results. However, both meta-analyses have unexplained residual variances across studies, which indicates that RRTs are not completely under the control of the researcher.