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Journal of Official Statistics

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“Interviewer” Effects in Face-to-Face Surveys: A Function of Sampling, Measurement Error, or Nonresponse?

1Survey Methodology Program (SMP), Survey Research Center (SRC), Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI 48106, U.S.A.

2Joint Program in Survey Methodology (JPSM), Ludwig Maximilian University of Munich, Institute for Employment Research (IAB), Nuremberg, Germany

3Institute for Employment Research (IAB), Nuremberg, Germany

This content is open access.

Citation Information: Journal of Official Statistics. Volume 29, Issue 2, Pages 277–297, ISSN (Online) 2001-7367, DOI: https://doi.org/10.2478/jos-2013-0023, October 2013

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Recent research has attempted to examine the proportion of interviewer variance that is due to interviewers systematically varying in their success in obtaining cooperation from respondents with varying characteristics (i.e., nonresponse error variance), rather than variance among interviewers in systematic measurement difficulties (i.e., measurement error variance) - that is, whether correlated responses within interviewers arise due to variance among interviewers in the pools of respondents recruited, or variance in interviewer-specific mean response biases. Unfortunately, work to date has only considered data from a CATI survey, and thus suffers from two limitations: Interviewer effects are commonly much smaller in CATI surveys, and, more importantly, sample units are often contacted by several CATI interviewers before a final outcome (response or final refusal) is achieved. The latter introduces difficulties in assigning nonrespondents to interviewers, and thus interviewer variance components are only estimable under strong assumptions. This study aims to replicate this initial work, analyzing data from a national CAPI survey in Germany where CAPI interviewers were responsible for working a fixed subset of cases.

Keywords: Interviewer variance; nonresponse error variance; measurement error variance; face-to-face data collection; multilevel modeling; PASS study

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