Research data are fragile and subject to classical measurement error as well as to the risk of manipulation. This also applies to survey data which might be affected by deviant behavior at different stages of the data collection process. Assuring data quality requires focusing on the incentives to which all actors in the process are exposed. Relevant actors and some specific incentives are presented. The role of data based methods for detection of deviant behavior is highlighted as well as limitations when actors are aware of them. Conclusions are drawn on how settings can be improved to provide positive incentives. Furthermore, it is stressed that a proper documentation of data quality issues in survey data is required both in order to increase trust in the data eventually used for analysis and to provide input for the development of new methods for detection of deviant behavior.
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