Detecting Interviewer Fraud Using Multilevel Models

Interviewer falsification, such as the complete or partial fabrication of interview data, has been shown to substantially affect the results of survey data. In this study, we apply a method to identify falsifying face-to-face interviewers based on the development of their behavior over the survey field period. We postulate four potential falsifier types: steady low-effort falsifiers, steady high-effort falsifiers, learning falsifiers, and sudden falsifiers. Using large-scale survey data from Germany with verified falsifications, we apply multilevel models with interviewer effects on the intercept, scale, and slope of the interview sequence to test whether falsifiers can be detected based on their dynamic behavior. In addition to identifying a rather high-effort falsifier previously detected by the survey organization, the model flagged two additional suspicious interviewers exhibiting learning behavior, who were subsequently classified as deviant by the survey organization. We additionally apply the analysis approach to publicly available cross-national survey data and find multiple interviewers who show behavior consistent with the postulated falsifier types.