Here - Tilburg University

Presenter
Storfinger, Nina; University of Giesen
Authors
Nina Storfinger; University of Giessen
Natalja Menold; GESIS - Leibniz-Institut für Sozialwissenschaften, Mannheim
Peter Winker; University of Giessen
Title
Indicator based method for ex-post identification of falsifications in survey data.
Abstract
Data quality in face-to-face interviews might be affected by interviewers'
irregular behaviour like intentional deviation from the prescribed interviewing
procedures, called cheating or interviewer falsification. As a part of a DFG
research project we develop a multivariate statis-tical method - based on the
motivation of such cheating behaviour - for ex-post identifica-tion of
falsifications in survey data.
As a first step in the project we conducted two explorative studies to identify the
attributes of questionnaires, which would be useful to identify falsified data.
During this step, existing real survey data is compared with “falsified” data
which is fabricated by people participating in the explorative study. First results
indicate clear differences between falsi-fied and real data. Falsifiers show a
higher proportion of denominations of the option “Oth-ers” (in all semi-open
questions which offer the option “other”), show less extreme answers in scale
questions and they overestimate the political knowledge of real respondents.
Fur-ther the falsifiers tend to round their answers to open-ended questions
which require a me-tric answer like income or the frequency of a specific
behaviour. Also they show higher in-ternal consistencies in item sets which are
calculated by means of reliability coefficients.
Based on these results we compute for every interviewer some specific
“indicators of cheating” which are included in the multivariate analysis. For
example we calculate the share of extreme answers in all scale questions or the
share of rounded answers in all open-ended questions, and incorporate them in