Bayesian Structural Equation Modeling (SEM) has received increasing interest due to its capacity to address challenges in the frequentist approach, such as nonconvergence, Heywood cases, small sample sizes, and inadmissible solutions. A defining feature of Bayesian SEM is the use of a priori distributions, which play a fundamental role in parameter estimation, model interpretation, and uncertainty quantification. While researchers may express skepticism regarding the subjectivity of priors, they represent a substantial advantage of Bayesian methods, enabling the integration of prior knowledge about parameters before observing the data. However, the choice and specification of a priori distributions remain an underexplored aspect of Bayesian SEM. This protocol outlines the goal of a scoping review designed to explore the application of a priori distributions in Bayesian SEM, with a particular focus on confirmatory factor analysis and full SEM models. Key aspects of a priori distribution usage will be examined, including their application across different dimensionality structures and model parameters. Special attention will be given to priors for variances, loadings, regression coefficients, and covariances, both in terms of distribution families and hyperparameter values, highlighting their impact on posterior distributions, model estimation, and performance. By synthesizing recent literature, this review will identify trends, challenges, and gaps in the use of a priori distributions within the Bayesian SEM framework. The findings aim to promote informed decision-making regarding prior elicitation and enhance the robustness of Bayesian SEM applications.

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