Abstract

Interviewer-administered surveys are, in many respects, seen as the gold-standard form of data collection. Interviewers play a vital role in achieving high data quality by contacting, identifying, and recruiting target respondents, answering their queries, and administering standardized interviews. However, some interviewers may be enticed to intentionally deviate from the prescribed interviewing guidelines and fabricate (parts of or entire) interviews. Such fabrication can lead to severe bias, especially in multivariate analyses. Hence, several studies have proposed methods for preventing or identifying complete falsifications. At the same time, current literature mostly neglects two important aspects: (1) How can survey researchers detect partial falsifications? and (2) How can researchers effectively detect different falsification forms in panel survey data? The common notion in panel surveys is that falsifications are easy to detect, since inconsistent or implausible answers between waves could be flagged as suspicious. Nonetheless, information on the concrete implementation of “between waves” checks, as well as evaluations on the effectiveness of such checks for detecting falsifications are missing from the literature. Further, previous literature lacks methods targeted on partial falsifications for both longitudinal and cross-sectional data. In the present case study, we aim to close these gaps by examining whether we can effectively identify partial falsifications in the German Panel Study “Labour Market and Social Security” (PASS), which included verified cases of interviewer misbehaviour and partial falsifications. First, we assess whether established statistical detection methods and falsification indicators also succeed in identifying partial falsifications. Second, we test the common notion that falsifiers provide inconsistent answers to otherwise time-stable items between different waves of data collection. Altogether, the results of this study inform how survey researchers can improve their quality control procedures for panel surveys.