The use of face-to-face interviews is still a very common data collection method in social sciences. The danger associated with the use of such data collection methods is a reduction in the resulting survey data’s quality due to interviewers’ fabrications or falsifications, which in turn has led to the emergence of a large set of methods for controlling the data collection process, the focus of which is limited and bypasses the behavioral characteristics of interviewers. In this context, paradata and GPS-paradata are an important new tool for use in the process of quality control of collected data or as part of a methodological audit, allowing not only to potentially identify and prevent falsification or fabrication by interviewers, but also to assess the correctness of methodological instructions. This article provides an overview of the available and practiced methods for using GPS-paradata in two main strategies (data point analysis and interviewer path analysis): geofencing, strand length, curbstoning, connecting interviews’ locations and interviewer path analysis. The possibilities of using such control methods depend on the sample design and on the methodological features of the surveys in general. However, the use of GPS-paradata to control the data collection process is not in itself a surefire method for detecting interviewers’ fabrications or falsifications, as it may be subject to technical inaccuracies or unintentional interviewer errors. It is a useful additional method aimed at identifying “suspicious” interviews which require the use of more resource-intensive methods of control (for example, repeated contact). In addition, the article presents an analysis of the quality of acquired GPS-paradata on the example of 26th wave of the RLMS-HSE, based on analyzing missing data and quality of measurements (HDOP). The results show that the quality of GPS-paradata can be related both to the region where the interview is conducted and to the characteristics of the interviewers.