Validating psychometric survey responses
Alberto Mastrotto (1), Anderson Nelson (1), Dev Sharma (1), Ergeta, Muca (1), Kristina Liapchin (1), Luis Losada (1), Mayur Bansal (1), Roman S., Samarev (2, 3) ((1) Columbia University, 116th St, Broadway, New York,, NY 10027, USA, (2) dotin Inc, Francisco Ln. 194, 94539
TL;DR
This paper introduces a machine learning-based method to validate survey responses by analyzing user mouse activity, aiming to detect suspicious behavior and improve data quality in web surveys.
Contribution
It proposes a novel approach using mouse activity data and models like rule-based, LSTM, and HMM to classify user validity without analyzing specific survey answers.
Findings
Effective detection of suspicious user behavior.
Potential for real-time validation in web surveys.
Improved data integrity in survey responses.
Abstract
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.
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Survey Methodology and Nonresponse · Time Series Analysis and Forecasting
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory