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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-9064
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dc.contributor.authorHöglinger, Marc-
dc.contributor.authorDiekmann, Andreas-
dc.date.accessioned2018-08-17T06:57:47Z-
dc.date.available2018-08-17T06:57:47Z-
dc.date.issued2017-
dc.identifier.issn1047-1987de_CH
dc.identifier.issn1476-4989de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/9064-
dc.descriptionErworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)de_CH
dc.description.abstractValidly 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.de_CH
dc.language.isoende_CH
dc.publisherCambridge University Pressde_CH
dc.relation.ispartofPolitical Analysisde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc000: Allgemeines und Wissenschaftde_CH
dc.titleUncovering a blind spot in sensitive question research : false positives undermine the crosswise-model RRTde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
dc.identifier.doi10.1017/pan.2016.5de_CH
dc.identifier.doi10.21256/zhaw-9064-
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end137de_CH
zhaw.pages.start131de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume25de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Höglinger, M., & Diekmann, A. (2017). Uncovering a blind spot in sensitive question research : false positives undermine the crosswise-model RRT. Political Analysis, 25(1), 131–137. https://doi.org/10.1017/pan.2016.5
Höglinger, M. and Diekmann, A. (2017) ‘Uncovering a blind spot in sensitive question research : false positives undermine the crosswise-model RRT’, Political Analysis, 25(1), pp. 131–137. Available at: https://doi.org/10.1017/pan.2016.5.
M. Höglinger and A. Diekmann, “Uncovering a blind spot in sensitive question research : false positives undermine the crosswise-model RRT,” Political Analysis, vol. 25, no. 1, pp. 131–137, 2017, doi: 10.1017/pan.2016.5.
HÖGLINGER, Marc und Andreas DIEKMANN, 2017. Uncovering a blind spot in sensitive question research : false positives undermine the crosswise-model RRT. Political Analysis. 2017. Bd. 25, Nr. 1, S. 131–137. DOI 10.1017/pan.2016.5
Höglinger, Marc, and Andreas Diekmann. 2017. “Uncovering a Blind Spot in Sensitive Question Research : False Positives Undermine the Crosswise-Model RRT.” Political Analysis 25 (1): 131–37. https://doi.org/10.1017/pan.2016.5.
Höglinger, Marc, and Andreas Diekmann. “Uncovering a Blind Spot in Sensitive Question Research : False Positives Undermine the Crosswise-Model RRT.” Political Analysis, vol. 25, no. 1, 2017, pp. 131–37, https://doi.org/10.1017/pan.2016.5.


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