Falsifications of survey data may be classified by the source of the falsification: data providers, interviewers, or interviewed persons. This entry focuses on interviewers’ falsifications. Interviewers’ falsifications cause significant problems with the reported data and are difficult to identify reliably. The reported prevalence of falsifications by interviewers in cases where established quality standards and controls are used is low. Falsifications may seriously impact on data analysis results, regardless of how frequent they are in the data. Methods of detection include control procedures such as reinterview or ex-post data analysis methods. This entry first discusses large-scale survey data and forms of falsification before describing specific cases of interviewers’ falsifications, how data are contaminated through interviewers’ falsifications, and methods to detect falsifications.