Validating psychometric survey responses
This work addresses the issue of false data in web-surveys for researchers or survey administrators, but it appears incremental as it applies existing methods to a new domain.
The paper tackles the problem of detecting invalid survey responses by using machine learning models on mouse activity data, achieving a method that can predict validity without analyzing specific answers.
We present an approach to classify user validity in survey responses by using a machine learning techniques. The approach is based on collecting user mouse activity on web-surveys and fast predicting validity of the survey in general without analysis of specific answers. Rule based approach, LSTM and HMM models are considered. The approach might be used in web-survey applications to detect suspicious users behaviour and request from them proper answering instead of false data recording.