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a cluster analysis. Using this multivariate method we try to split the interviewers
into two groups, correct and possibly cheating ones. The perfor-mance of this
method is then assessed referring to the fraction of correctly assigned inter-
viewers. Because of knowing the cheating interviewers beforehand, we are able
to validate the clustering process immediately. We conduct some separate
cluster analyses differencing in the amount of included indicators to assess the
performance of every single indicator.
Results of the analysis show that a high share of all falsifiers is actually
pooled together in one cluster albeit some of the honest interviewers are also
added to this group. Concerning the performance of every single indicator the
“extreme-answers ratio” shows the highest share of correctly assigned
interviewers; more than half of the honest and half of the cheat-ing interviewers
could be identified. Thus, we might argue that the “indices of cheating” em-
ployed, help to identify cheaters. The sensitivity of the clustering method is then
analysed by means of bootstrapping. In a synthetic setting, we modify the
number of interviewers and the number of interviews to obtain results with
different sample sizes. As expected a higher number of interviews (by each
interviewer) induces a more correct clustering, meaning that the identification of
the cheating interviewers improves markedly.