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dc.contributor.authorStorfinger, Nina
dc.contributor.authorWinker, Peter
dc.date.accessioned2022-08-10T13:51:04Z
dc.date.available2012-01-06T11:58:56Z
dc.date.available2022-08-10T13:51:04Z
dc.date.issued2011
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:hebis:26-opus-85587
dc.identifier.urihttps://jlupub.ub.uni-giessen.de//handle/jlupub/6368
dc.identifier.urihttp://dx.doi.org/10.22029/jlupub-5819
dc.description.abstractFalsifications of survey data might result in specific statistical properties of the generated data differing from those of the surveyed population. Clustering methods have been proposed to identify potential falsifications based on such indicators. As any statistical procedure, the classification might entail errors, i.e. misclassification of honest interviewers as potential falsifiers and failing to identify all falsifications as such. Typically, the robustness of a statistical classification procedure is studied using a large number of problem instances with known allocation to the groups. However, given the sensitivity of falsifications in survey data, the access to datasets comprising correctly identified falsifications is very limited. Consequently, a bootstrap based approach is introduced and applied to assess the clustering method. This approach also allows modifying settings such as number of interviews per interviewer or share of falsifications in the dataset and to study the impact of these settings on the quality of the assignments. Results based on a small real dataset with identified falsifications are reported.en
dc.language.isoende_DE
dc.relation.ispartofseriesDiscussion papers / Zentrum für Internationale Entwicklungs- und Umweltforschung; 57
dc.rightsNamensnennung - Weitergabe unter gleichen Bedingungen Deutschland*
dc.rights.urihttps://creativecommons.org/licenses/by-sa/3.0/?la=de*
dc.subjectInterviewer falsificationsen
dc.subjectcluster analysisen
dc.subjectbootstrap methoden
dc.subject.ddcddc:370de_DE
dc.titleRobustness of clustering methods for identification of potential falsifications in survey dataen
dc.typeworkingPaperde_DE
local.affiliationZentrende_DE
local.opus.id8558
local.opus.fachgebietZentrum für internationale Entwicklungs- und Umweltforschungde_DE


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