Advancing Psychological Research With Random Forests: A Review of Methods, Tools, and Applications

Abstract

Contemporary psychological research increasingly involves machine-learning techniques, including random forests, for their capability in analyzing complex, high-dimensional data sets and modeling nonlinear predictive relations. In this article, we provide a comprehensive review of random-forest methods in psychological research. We begin by introducing the fundamental concepts of decision trees, followed by the theoretical framework of random forests as an ensemble method. Next, we review the methodological development and commonly used software tools for random-forest models. We discuss the practical issues and challenges when implementing random forests in psychological studies. Importantly, we then systematically review the empirical psychological research articles published between 2020 and 2022 that used random forests; we summarize the applications of random forests, with a special emphasis on data structure, software implementation, hyperparameter tuning, and approaches for handling missing data. By synthesizing the theoretical foundation and current empirical practices, in this article, we identify significant methodological gaps in applying random forests to psychological data and hope to initiate much needed conversations on how psychologists can effectively use the random-forest method to advance psychological science.