Incorporating Expert Knowledge in Structural Equation Models: Applications in Psychological Research

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.