Abstract
Survey questionnaires are essential tools in psychological and educational research, as the data they gather directly influence research conclusions and policy decisions. A major challenge in ensuring data quality is identifying aberrant response patterns that can jeopardize research outcomes, as they may introduce errors into subsequent analyses, potentially resulting in flawed theoretical conclusions and misguided practical applications. This study presents a machine learning solution that employs autoencoder neural networks to detect aberrant response patterns in survey data as a computational method. We evaluated the effectiveness of autoencoder neural networks in identifying response anomalies through both simulated and real data. The results indicate that this approach can effectively detect anomalies in responses, providing researchers with more options for their analyses and subsequent conclusions. Ultimately, this enhances the trustworthiness of findings in psychological and educational research.