Aims and scope

Nexus of Structural Equation Modeling (NSEM) aims to become the leading journal in presenting original research articles and review papers about Structural Equation Modeling (SEM), approaches to explain Socio-Techno Sciences phenomena. NSEM provides a platform for the dissemination of original empirical studies and comprehensive review articles that contribute to theoretical development and practical applications of SEM techniques. NSEM encourages submissions that utilize a wide range of SEM methodologies, including to Path Analysis, Confirmatory Factor Analysis, Multi-Group SEM, Moderated Mediation, and Latent Growth Modeling. Theoretical articles address new developments; applied articles deal with innovative structural equation modeling applications. Emphasis is placed on the rigorous application of these techniques to real-world problems in diverse fields such as Science, Economics, Finance, Business/Marketing, Management, Engineering, psychology, medicine, sociology, and Computer Science. Manuscripts should demonstrate methodological soundness, theoretical significance, and potential implications for practice or policy.

 

The scope of the Nexus of Structural Equation Modeling (NSEM) covers, but is not limited to, the following areas:

1. Methodological Advances in SEM
♦ Development of new estimation techniques, model evaluation criteria, and goodness-of-fit measures.
♦ Extensions of SEM to handle complex data structures (e.g., non-normal, longitudinal, hierarchical, and missing data).
♦ Bayesian SEM, Mixture SEM, Partial Least Squares (PLS-SEM), and hybrid modeling frameworks.

2. Measurement and Model Specification
♦ Reflective and formative measurement models.
♦ Validation of constructs and measurement invariance testing.
♦ Confirmatory factor analysis (CFA) and higher-order factor models.

3. Applied SEM Research
♦ Applications in psychology, education, economics, marketing, social sciences, health sciences, and management.
♦ Model-based investigations of mediation, moderation, and latent interactions.
♦ SEM applications for policy evaluation, behavioral modeling, and decision science.

4. Integration with Emerging Technologies
♦ SEM in the context of big data, machine learning, and artificial intelligence.
♦ Multilevel and multigroup modeling using modern computational tools.
♦ Simulation studies and methodological comparisons.

5. Software, Tools, and Computational Innovations
♦ Development and evaluation of SEM software packages.
♦ Tutorials, best-practice papers, and reproducible workflows for SEM researchers.

6. Theoretical and Conceptual Contributions
♦ Discussion papers on theoretical modeling issues and identification problems.
♦ Conceptual frameworks linking SEM with other statistical