The compatibility of psychological research and machine learning analysis based on theoretical frameworks

The use of supervised machine learning in psychological and psychiatric research has been rapidly increasing in recent years, offering significant advantages over traditional statistical methods, particularly in developing models from sample data to enhance predictions on independent test datasets. Machine learning also provides access to sophisticated statistical algorithms and allows for the empirical selection of a smaller subset of predictor variables. However, many researchers often rely on a large number of predictors without utilizing theoretical frameworks to inform their selections, which can elevate the risks of Type I errors, increase false positives, and diminish the generalizability of findings. To address this, it is crucial to incorporate theory into the research process in psychology. We examine how theory can guide variable selection and data analysis in psychological research utilizing machine learning, providing recommendations for integrating theory into the machine learning framework. For instance, in cyberpsychology, we demonstrate how a theoretical framework can inform the selection of predictor variables, enhancing the robustness and interpretability of the model. This approach not only mitigates the risk of spurious results but also ensures that research outcomes are closely aligned with established psychological theories. In summary, while machine learning offers powerful tools for data analysis, its application in psychology requires careful theoretical guidance to avoid common pitfalls and yield meaningful, generalizable insights.