Uncovering a Blind Spot in Sensitive Question Research: False Positives Undermine the Crosswise-Model RRT

Abstract

Validly measuring sensitive issues such as norm violations or stigmatizing traits through self-reports in surveys is often problematic. Special techniques for sensitive questions like the Randomized Response Technique (RRT) and, among its variants, the recent crosswise model should generate more honest answers by providing full response privacy. Different types of validation studies have examined whether these techniques actually improve data validity, with varying results. Yet, most of these studies did not consider the possibility of false positives, i.e., that respondents are misclassified as having a sensitive trait even though they actually do not. Assuming that respondents only falsely deny but never falsely admit possessing a sensitive trait, higher prevalence estimates have typically been interpreted as more valid estimates. If false positives occur, however, conclusions drawn under this assumption might be misleading. We present a comparative validation design that is able to detect false positives without the need for an individual-level validation criterion — which is often unavailable. Results show that the most widely used crosswise-model implementation produced false positives to a nonignorable extent. This defect was not revealed by several previous validation studies that did not consider false positives — apparently a blind spot in past sensitive question research.