<abstract xmlns="http://www.w3.org/1999/xhtml"><p>There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey “Panel Study ‘Labour Market and Social Security’” (PASS), where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.</p></abstract>

There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey “Panel Study ‘Labour Market and Social Security’” (PASS), where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.

Interviewer Effects on a Network-Size Filter Question

Opinion Market Research & Consulting GmbH, Rollnerstr. 8, 90408 Nuremberg, Germany.

Institute for Employment Research, Regensburger Str. 104, 90478 Nuremberg, Germany and University of Bamberg, Germany.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Interviewer Effects on a Network-Size Filter Question"}

There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions....

Interviewer Effects on a Network-Size Filter Question

Published Online: May 28, 2016

Page range: 349 - 373

Received: Aug 28, 2014

Accepted: Sep 08, 2015

DOI: https://doi.org/10.1515/jos-2016-0020

Keywords
Partial falsification, network generator, filter questions, interviewer cheating

© 2016 Michael Josten et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Interviewer Effects on a Network-Size Filter Question

Published Online: May 28, 2016

Page range: 349 - 373

Received: Aug 28, 2014

Accepted: Sep 08, 2015

DOI: https://doi.org/10.1515/jos-2016-0020

KeywordsPartial falsification, network generator, filter questions, interviewer cheating

© 2016 Michael Josten et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Partial falsification, network generator, filter questions, interviewer cheating