Interviewers' and Respondents' Joint Production of Response Quality in Openended Questions: A Multilevel Negativebinomial Regression Approach

Abstract: Open-ended questions are an important methodological tool for social science researchers, but they suffer from large variations in response quality. In this contribution, we discuss the state of research and develop a systematic approach to the mechanisms of quality generation in open-ended questions, examining the effects from respondents and interviewers as well as those arising from their interactions. Using data from an open-ended question on associations with foreigners living in Germany from the ALLBUS 2016, we first apply a two-level negative binomial regression to model influences on response quality on the interviewer and respondent level and their interaction. In a second regression analysis, we assess how qualitative variation (information entropy) in responses on the interviewer level is related to interviewer characteristics and data quality. We find that respondents' education, age, gender, motivation and topic interest influence response quality. The interviewer-rela

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Interviewers' and Respondents' Joint Production of Response Quality in Openended Questions: A Multilevel Negativebinomial Regression Approach ; volume:15 ; number:1 ; year:2021 ; pages:43-76
Veröffentlichungsversion
begutachtet (peer reviewed)
Methods, data, analyses ; 15, Heft 1 (2021), 43-76

Classification
Sozialwissenschaften, Soziologie, Anthropologie

Event
Veröffentlichung
(where)
Mannheim
(who)
SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
(when)
2021
Creator
Barth, Alice
Schmitz, Andreas

DOI
10.12758/mda.2020.08
URN
urn:nbn:de:101:1-2022062717043896571711
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:33 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Associated

  • Barth, Alice
  • Schmitz, Andreas
  • SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.

Time of origin

  • 2021

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