Detecting careless response patterns in Likert scales using supervised machine learning and Monte Carlo simulations

Key Points

• Careless response patterns significantly distort data integrity in Likert scales, impacting research results.
• Machine learning models effectively identify these patterns, achieving a detection accuracy of over 85%.
• Supervised learning combined with Monte Carlo simulations enhances the robustness of the analysis across various datasets.
• Findings highlight the need for innovative tools to ensure high-quality responses in survey-based research.