<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

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

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

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

Institute for Employment Research (IAB), Nuremberg, Germany

Journal of Official Statistics

Recent research has attempted to examine the proportion of interviewer variance that is due to interviewers systematically varying in their success in...

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

Publicado en línea: 03 oct 2013

Páginas: 277 - 297

DOI: https://doi.org/10.2478/jos-2013-0023

Palabras clave
Interviewer variance, nonresponse error variance, measurement error variance, face-to-face data collection, multilevel modeling, PASS study

This content is open access.

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

Publicado en línea: 03 oct 2013

Páginas: 277 - 297

DOI: https://doi.org/10.2478/jos-2013-0023

Palabras claveInterviewer variance, nonresponse error variance, measurement error variance, face-to-face data collection, multilevel modeling, PASS study

This content is open access.

Palabras clave
Interviewer variance, nonresponse error variance, measurement error variance, face-to-face data collection, multilevel modeling, PASS study