Advances in data collection have made intensive longitudinal data easier to collect, unlocking potential for methodological innovations to model such data. Dynamic structural equation modeling (DSEM) is one such methodology but recent studies have suggested that its small N performance is poor. This is problematic because small N data are omnipresent in empirical applications due to logistical and financial concerns associated with gathering many measurements on many people. In this paper, we discuss how previous studies considering small samples have focused on Bayesian methods with diffuse priors. The small sample literature has shown that diffuse priors may cause problems because they become unintentionally informative. Instead, we outline how researchers can create weakly informative admissible-range-restricted priors, even in the absence of previous studies. A simulation study shows that metrics like relative bias and non-null detection rates with these admissible-range-restricted priors improve small N properties of DSEM compared to diffuse priors.