Cross-Lagged Network Models

Network theory and accompanying methodology are becoming increasingly popular as an alternative to latent variable models for representing and, ultimately, understanding psychological constructs. The core feature of network models is that observed variables (e.g., symptoms of depression) directly influence one another over time (e.g., low mood --> concentration problems), resulting in an interconnected dynamical system. The dynamics of such a system might result in certain states (e.g., a depressive episode). Network modeling has been applied to cross-sectional data and intensive longitudinal designs (e.g., data collected using an Experience Sampling Method). In this paper, we present a cross-lagged panel network model to reveal item-level longitudinal effects that occur within and across constructs that are measured at a small set of measurement occasions. The proposed model uses a combination of regularized regression estimation and structural equation modeling to estimate auto-regressive and cross-lagged pathways that characterize the effects of observed components of psychological constructs on each other over time. We demonstrate the application of this model to longitudinal data on students' commitment to school and self-esteem.