The thesis deals with the survey, specifically its quality and the associated total survey error. Particularly it is focused on those activities, which are linked with the activity of interviewer. Subsequently, this is related to interviewers of the Public Opinion Research Centre. The first part deals with the concept of total survey error. It summarizes the basic concepts and possible points of view on error and data quality. The work also deals with the complexity of the total error and of interdependence of its components. Today's new concepts of research quality are introduced. Briefly it is mentioned the history of surveys, of their methodology and quality. Another part is from the perspective of data quality focused on the interviewer's work and possible influences on the course of questioning, which are associated. The various activities with regard to possible sources of error are discussed. From this perspective, it is mapped and evaluated the activity of interviewers of CVVM and some improvement of work with interviewers is designed.

This thesis has two purposes; emphasizing the importance of data quality of Big Data, and identifying and evaluating potential error sources in JavaScript tracking (a client side on - site online behavior clickstream data collection method commonly used in web analytics). The importance of data quality of Big Data is emphasized through the evaluation of JavaScript tracking. The Total Survey Error framework is applied to JavaScript tracking and 17 nonsampling error sources are identified and evaluated. The bias imposed by these error sources varies from large to small, but the major takeaway is the large number of error sources actually identified. More work is needed. Big Data has much to gain from quality work. Similarly, there is much that can be done with statistics in web analytics.