Developmental Data Science: How Machine Learning can Advance Theory Formation in Developmental Psychology

Theories are the vehicle of cumulative knowledge acquisition. At this time, however, many (developmental) psychological theories are insufficiently precise to derive testable hypotheses. This limits the advancement of our principled understanding of development. This problem cannot be resolved by improving the way deductive (confirmatory) research is conducted (e.g., through preregistration and replication), because theory formation requires inductive (exploratory) research. This paper argues that machine learning can help advance theory formation in (developmental) psychology, because it enables rigorous exploration of patterns in data. The paper discusses specific advantages of machine learning, explains core methodological concepts, introduces relevant methods, and describes how data-driven insights are consolidated into theory. Machine learning automates exploration, and incorporates checks and balances to ensure generalizable results. It can assist in phenomenon detection and offers a more holistic understanding of the phenomena associated with an outcome or process of interest.