Idiographic network models estimate an individual's psychological dynamics, promising personalized insight into psychopathology, but recovering their full structure demands more observations than clinical studies typically collect, and researchers lack systematic guidance on how many are needed. This thesis evaluated two Bayesian Factor Design Analysis (BFDA) procedures, one to plan that sample size and one to monitor it as data accumulate, across a 2 × 2 × 2 design crossing network density, edge strength, and prior specification in graphical vector autoregressive (GVAR) networks. 

Method 1, a fixed-N analysis over 100 Monte Carlo replications, estimated the number of observations required for full temporal recovery; Method 2, a sequential design with futility monitoring, tested at successive interim points whether further data would yield compelling evidence. Both procedures performed as intended: Method 1 estimated the required sample size a priori, and Method 2 monitored the accumulating evidence at each interim point, allowing early stopping either because that evidence had become sufficient or because further data collection would be futile. Together, these procedures mark an important step toward the practical use of idiographic network models.