Unified structural equation modeling (uSEM) implemented in the Group Iterative Multiple Model Estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: 1) estimating autoregressive relations by default in the null model (GIMME-AR), and 2) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best – and were roughly equivalent in performance – when autoregressive relations were strong (e.g., β = .60). When they were weak (β <= .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state fMRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies typically disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific contexts which allow researchers to place confidence in directionality results.
The Influence of Autoregressive Relation Strength and Search Strategy on Directionality Recovery in GIMME
