Symptom network theory describes psychopathology as an interconnected web of symptoms that interact with each other and cluster together to form internalizing disorders, like depression or anxiety. Network theory can explain why certain disorders frequently co-occur together (a phenomenon known as comorbidity) and has the potential to identify central symptoms as targets for treatment. Although previous research has examined the network structure of depression and related conditions, most studies are cross-sectional (they only look at one time point), and almost all of them test associations between-person rather than within-person. Consequently, their findings may not accurately describe experiences that unfold over time or within the same person.

In my current project, I will use a recently developed analytic technique, group iterative multiple model estimation (GIMME), to analyze daily diary data collected from university students (N = 217). The data includes daily self-report ratings on symptoms related to internalizing constructs like depression, anxiety, and stress. GIMME will estimate a dynamic network model of symptoms for each participant individually before searching for group-level commonalities. This technique will allow me to examine associations between symptoms within-person and over time to gain a deeper understanding of the dynamic processes underlying internalizing symptoms that are experienced by individuals in their daily lives.