Structural Equation Modeling (SEM) is used in psychology to model complex structures of data. However, sample sizes often cannot be as large as ideal forSEM, leading to a problem of insufficient power. Bayesian estimation with informed priors can be beneficial in this context. Our simulation study examines this issue over a real case of a mediation model. Parameter recovery, power and coverage were considered. The advantage of a Bayesian approach was evident for the smallest effects. The correct formalization of the theoretical expectations is crucial, and it allows for increased collaboration among researchers in Psychology and Statistics
