About the Journal

The International Journal of Structural Equation Modeling (IJSEM) is a peer-reviewed, open-access academic journal dedicated to advancing the theory, methodology, and application of structural equation modeling (SEM). IJSEM serves as a premier outlet for researchers across disciplines who utilize SEM to explore complex relationships among observed and latent variables in behavioral, social, medical, business, and computational sciences.

Aim & Scope

IJSEM aims to promote the development and dissemination of innovative SEM techniques and their application to real-world problems. The journal encourages theoretical contributions, applied studies, and methodological critiques that enhance the understanding, utility, and accessibility of SEM in diverse academic and professional domains. It supports both traditional and emerging SEM approaches, including frequentist, Bayesian, and machine learning integrations.

Research Areas

IJSEM welcomes original research articles, methodological notes, simulations, review papers, and case studies in areas such as:

• Theoretical advancements in SEM
• Confirmatory factor analysis (CFA) and measurement models
• Path modeling and mediation analysis
• Multigroup and longitudinal SEM
• Multilevel and hierarchical SEM
• Partial least squares (PLS-SEM)
• Bayesian SEM and model comparison
• SEM in psychological, educational, and sociological research
• SEM in marketing, management, and finance
• Software development and modeling innovations (e.g., using R, Mplus, SmartPLS, SPSS, Process Macro, AMOS, LISREL, lavaan)
• Measurement model
• Structural model
• Convergent and discriminant validity
• Multivariate data analysis
• Moderation and mediation

Additional Features

• Rigorous Peer Review: A double-blind peer-review process with an average decision time of 4-6 weeks.
• Open Access: All articles are freely available to read, download, and share, ensuring global accessibility.
• Special Issues: Themed issues focusing on emerging trends and critical topics in AI and psychology.
• Interdisciplinary Collaboration: Encourages contributions from psychology, computer science, neuroscience, and related fields.
• Engagement Opportunities: Regular webinars, virtual conferences, and interactive forums for researchers and practitioners.

JAIEIA is committed to fostering innovation and excellence in the application of AI to education, with the ultimate goal of enhancing learning outcomes, promoting equity, and preparing individuals for the challenges of the future.