It is generally accepted that interviewers have a considerable effect on survey response. The difference between response success and failure does not only affect the response rate, but can also influence the composition of the realized sample or respondent set, and consequently introduce nonresponse bias. To measure these two different aspects of the obtained sample, response propensities will be used. They have an aggregate mean and variance that can both be used to construct quality

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... s for the obtained sample of respondents. As these propensities can also be measured on the interviewer level, this allows evaluation of the interviewer group and of the extent to which individual interviewers contribute to a biased respondent set. In this article, a procedure based on a multilevel model with random intercepts and random slopes is elaborated and illustrated. The results show that the procedure is informative to detect influential interviewers with an impact on nonresponse basis.