Psychological Network Analysis (PNA) has emerged as a powerful tool for understanding the complex interplay of constructs in developmental and educational sciences. Unlike traditional models that assume relationships among variables arise from latent factors, PNA conceptualizes them as dynamic systems of interacting components. This tutorial introduces PNA's theoretical foundations, key concepts (e.g., nodes, edges, network structures), and its methodological applications using cross-sectional, longitudinal, intensive, and cohort data. Through step-by-step guidance and real-world examples, we illustrate how PNA can capture developmental changes, reveal causal structures using Directed Acyclic Graphs (DAGs), and support developmental and educational research. Special emphasis is given to practical implementation using R, including network estimation, accuracy testing, and visualization. By equipping researchers with the necessary tools to construct and interpret psychological networks, this chapter provides a comprehensive framework for leveraging PNA to explore the multifaceted relationships shaping learning, motivation, and social-emotional development.
