It is common to test bidirectional and dynamic associations between constructs to understand how they are intertwined and unfold over time. However, there are two primary limitations in the current literature. First, studies often report only the average effects of the dynamic associations assuming average findings can be generalized to all individuals in a certain population. Second, studies often interpret local coefficients such as autoregressive and cross-lagged coefficients in a separate manner. However, focusing solely on interpreting these statistics separately may not reveal holistic dynamic patterns as it is possible that the local and holistic dynamic patterns may not align with each other. Our paper aims to address these two limitations by introducing techniques to produce person-specific holistic dynamic patterns through a visualization approach so that the dynamic associations between constructs for each person can be directly observed and examined. We utilized a case example with 200 emerging adults diagnosed with type 1 diabetes to examine the dynamic associations between their diabetes self-efficacy and self-care behavior. We applied a dynamic structural equation modeling to generate person-specific local autoregressive and cross-lagged coefficients and used vector plots and eigenvalues to visualize person-specific holistic dynamic patterns.
