Analyzing misclassified data: Randomized response and post randomization

Abstract

The subject of this thesis is the analysis of randomized response (RR) data and the analysis of data that are subject to the post randomization method (PRAM). RR is an interview technique that can be used when sensitive questions have to be asked and respondents are reluctant to answer directly. RR variables can be seen as misclassified categorical variables where conditional misclassification probabilities are known. The misclassification protects the privacy of the individual respondent. The idea of RR can also be used to protect data that have already been collected. PRAM can be seen as an application of RR where the misclassification is applied using a computer. The thesis investigates how statistical models such as the log-linear model and the linear regression model should be adapted to take the misclassification into account. In addition, measures of disclosure risk are discussed when PRAM is applied.