Bayesian structural equation modeling (BSEM) is receiving increasing interest primarily due to its ability to address some of the issues found in the mainstream frequentist approach (e.g., nonconvergence, Heywood cases, sample size limitations, and inadmissible solutions). Additionally, BSEM allows for the fitting of complex models that classical maximum likelihood methods might struggle to handle. A critical component of any Bayesian analysis is the prior distribution of the unknown model parameters. A key distinction between Bayesian structural equation modeling and frequentist structural equation modeling is the use of priors. Researchers may be skeptical about the subjectivity of prior distributions and their impact on Bayesian modeling. However, priors are a significant advantage of using Bayesian statistics, as they allow previously known information to be transparently and directly included in the model specification. Proper prior elicitation is essential for translating knowledge and judgment about a phenomenon into a probability distribution. Priors allow for the quantification of uncertainty and encapsulate available knowledge about the parameters before observing the data. There are several ways to translate prior knowledge into distribution parameters. Results show that researchers tend to rely on weakly informative priors (i.e., small-variance priors). However, prior elicitation in Bayesian structural equation modeling still has a long way to go in terms of development and widespread adoption.

The primary challenge of this project lies in the effective elicitation and incorporation of prior knowledge into Bayesian Structural Equation Modeling (BSEM). Researchers often struggle with the subjectivity and selection of appropriate prior distributions, which can substantially impact model estimates and interpretations of the distribution a posteriori. Additionally, the project aims to address the computational demands associated with fitting complex models, requiring substantial high-performance computing resources.

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With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific projects with the Sustainable Development Goals is now available in Ciência_Iscte. These are the Sustainable Development Goals identified for this project. For more detailed information on the Sustainable Development Goals, click here.