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

Höglinger, Marc; Diekmann, Andreas (2016). Uncovering a Blind Spot in Sensitive Question Research: False Positives Undermine the Crosswise-Model RRT (University of Bern Social Sciences Working Papers 24). Bern: University of Bern

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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 non-ignorable 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.

Item Type:

Report (Report)

Division/Institute:

03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Sociology

UniBE Contributor:

Höglinger, Marc and Diekmann, Andreas

Subjects:

300 Social sciences, sociology & anthropology

Series:

University of Bern Social Sciences Working Papers

Publisher:

University of Bern

Language:

English

Submitter:

Marc Höglinger

Date Deposited:

18 Jul 2017 14:56

Last Modified:

18 Jul 2017 14:59

Uncontrolled Keywords:

Sensitive Questions; Sensitive Survey Techniques; Randomized Response Technique; Crosswise Model; Item Count Technique; Data Validity; Social Desirability; Measurement Error; Survey Design

BORIS DOI:

10.7892/boris.94169

URI:

https://boris.unibe.ch/id/eprint/94169

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